Lidé

doc. Ing. Martin Saska, Dr. rer. nat.

Všechny publikace

Present and Future of SLAM in Extreme Environments: The DARPA SubT Challenge

  • DOI: 10.1109/TRO.2023.3323938
  • Odkaz: https://doi.org/10.1109/TRO.2023.3323938
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This paper surveys recent progress and discusses future opportunities for Simultaneous Localization And Mapping (SLAM) in extreme underground environments. SLAM in subterranean environments, from tunnels, caves, and man-made underground structures on Earth, to lava tubes on Mars, is a key enabler for a range of applications, such as planetary exploration, search and rescue, disaster response, and automated mining, among others. SLAM in underground environments has recently received substantial attention, thanks to the DARPA Subterranean (SubT) Challenge , a global robotics competition aimed at assessing and pushing the state of the art in autonomous robotic exploration and mapping in complex underground environments. This paper reports on the state of the art in underground SLAM by discussing different SLAM strategies and results across six teams that participated in the three-year-long SubT competition. In particular, the paper has four main goals. First, we review the algorithms, architectures, and systems adopted by the teams; particular emphasis is put on LIDAR-centric SLAM solutions (the go-to approach for virtually all teams in the competition), heterogeneous multi-robot operation (including both aerial and ground robots), and real-world underground operation (from the presence of obscurants to the need to handle tight computational constraints). We do not shy away from discussing the “dirty details” behind the different SLAM systems, which are often omitted from technical papers. Second, we discuss the maturity of the field by highlighting what is possible with the current SLAM systems and what we believe is within reach with some good systems engineering. Third, we outline what we believe are fundamental open problems, that are likely to require further research to break through. Finally, we provide a list of open-source SLAM implementations and datasets that have been produced during the SubT challenge and related efforts, and constitute a useful resource for researchers and practitioners.

A Perception-Aware NMPC for Vision-Based Target Tracking and Collision Avoidance with a Multi-Rotor UAV

  • DOI: 10.1109/ICUAS54217.2022.9836071
  • Odkaz: https://doi.org/10.1109/ICUAS54217.2022.9836071
  • Pracoviště: Multirobotické systémy
  • Anotace:
    A perception-aware Nonlinear Model Predictive Control (NMPC) strategy aimed at performing vision-based target tracking and collision avoidance with a multi-rotor aerial vehicle is presented in this paper. The proposed control strategy considers both realistic actuation limits at the torque level and visual perception constraints to enforce the visibility coverage of a target while complying with the mission objectives. Furthermore, the approach allows to safely navigate in a workspace area populated by dynamic obstacles with a ballistic motion. The formulation is meant to be generic and set upon a large class of multi-rotor vehicles that covers both coplanar designs like quadrotors as well as fully-actuated platforms with tilted propellers. The feasibility and effectiveness of the control strategy are demonstrated via closed-loop simulations achieved in MATLAB.

Adaptive arbitration of aerial swarm interactions through a Gaussian kernel for coherent group motion

  • DOI: 10.3389/frobt.2022.1006786
  • Odkaz: https://doi.org/10.3389/frobt.2022.1006786
  • Pracoviště: Multirobotické systémy
  • Anotace:
    Swarm behaviors offer scalability and robustness to failure through a decentralized and distributed design. When designing coherent group motion as in swarm flocking, virtual potential functions are a widely used mechanism to ensure the aforementioned properties. However, arbitrating through different virtual potential sources in real-time has proven to be difficult. Such arbitration is often affected by fine tuning of the control parameters used to select among the different sources and by manually set cut-offs used to achieve a balance between stability and velocity. A reliance on parameter tuning makes these methods not ideal for field operations of aerial drones which are characterized by fast non-linear dynamics hindering the stability of potential functions designed for slower dynamics. A situation that is further exacerbated by parameters that are fine-tuned in the lab is often not appropriate to achieve satisfying performances on the field. In this work, we investigate the problem of dynamic tuning of local interactions in a swarm of aerial vehicles with the objective of tackling the stability–velocity trade-off. We let the focal agent autonomously and adaptively decide which source of local information to prioritize and at which degree—for example, which neighbor interaction or goal direction. The main novelty of the proposed method lies in a Gaussian kernel used to regulate the importance of each element in the swarm scheme. Each agent in the swarm relies on such a mechanism at every algorithmic iteration and uses it to tune the final output velocities. We show that the presented approach can achieve cohesive flocking while at the same time navigating through a set of way-points at speed. In addition, the proposed method allows to achieve other desired field properties such as automatic group splitting and joining over long distances. The aforementioned properties have been empirically proven by an extensive set of simulated and field experiments, in communication-full and communication-less scenarios. Moreover, the presented approach has been proven to be robust to failures, intermittent communication, and noisy perceptions.

Autonomous capture of agile flying objects using UAVs: The MBZIRC 2020 challenge

  • DOI: 10.1016/j.robot.2021.103970
  • Odkaz: https://doi.org/10.1016/j.robot.2021.103970
  • Pracoviště: Multirobotické systémy
  • Anotace:
    In this paper, a novel approach for autonomously catching fast flying objects is presented, as inspired by the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. In this competition, an autonomous Unmanned Aerial Vehicle (UAV) was used to intercept a ball carried by a fast flying drone. The presented solution utilizes a 3D LiDAR sensor for quick and robust target detection. The trajectory of the target is estimated and predicted to select a suitable interception position. The interceptor UAV is navigated into the interception position to safely approach the target. The interception position is frequently being adjusted based on the updated estimation and prediction of the target’s motion to ensure that the ball is caught in the dedicated onboard net. After a successful interception is detected, the UAV lands in a designated landing area. The proposed concept was intensively tested and refined in demanding outdoor conditions with strong winds and varying perception conditions to achieve the robustness required by both the demanding application and the competition. In the MBZIRC 2020 competition, our solution scored second place in Challenge 1 and first place in a combined Grand Challenge. This manuscript will provide a detailed description of the applied methods and an evaluation of our approach with data collected from real-world experiments. In addition, we present achievements of our R&D towards the transition from the MBZIRC competition to an autonomous drone interceptor, which was the main motivation of this challenge.

Controlling a Swarm of Unmanned Aerial Vehicles Using Full-Body k-Nearest Neighbor Based Action Classifier

  • DOI: 10.1109/ICUAS54217.2022.9836097
  • Odkaz: https://doi.org/10.1109/ICUAS54217.2022.9836097
  • Pracoviště: Multirobotické systémy
  • Anotace:
    The intuitive control of robot swarms becomes crucial when humans are working in close proximity with the swarm in unknown environments. In such operations, it is necessary to maintain the autonomy of the swarm while giving the human operator enough means to influence the decision-making process of the robots. This paper presents a human-swarm interaction approach using full-body action recognition to control an autonomous flock of unmanned aerial vehicles. We estimate the full-body pose of the human operator and use a k-nearest neighbor algorithm to classify the action made by the humans. Finally, the swarm uses the identified action to decide its goal direction. We demonstrate the practicality of our approach with a multi-stage experimental setup to evaluate the prediction accuracy and robustness of the system.

Cooperative Navigation and Guidance of a Micro-Scale Aerial Vehicle by an Accompanying UAV using 3D LiDAR Relative Localization

  • DOI: 10.1109/ICUAS54217.2022.9836116
  • Odkaz: https://doi.org/10.1109/ICUAS54217.2022.9836116
  • Pracoviště: Multirobotické systémy
  • Anotace:
    A novel approach for cooperative navigation and guidance of a micro-scale aerial vehicle by an accompanying Unmanned Aerial Vehicle (UAV) using 3D Light Detection and Ranging (LiDAR) relative localization is proposed in this paper. The use of 3D LiDARs represents a reliable way of environment perception and robust UAV self-localization in Global Navigation Satellite System (GNSS)-denied environments. However, 3D LiDARs are relatively heavy and they need to be carried by large UAV platforms. On the contrary, visual cameras are cheap, light-weight, and therefore ideal for small UAVs. However, visual self-localization methods suffer from loss of precision in texture-less environments, scale unobservability during certain maneuvers, and long-term drift with respect to the global frame of reference. Nevertheless, a micro-scale camera-equipped UAV is ideal for complementing a 3D LiDAR-equipped UAV as it can reach places inaccessible to a large UAV platform. To gain the advantages of both navigation approaches, we propose a cooperative navigation and guidance architecture utilizing a large LiDAR-equipped UAV accompanied by a small secondary UAV carrying a significantly lighter monocular camera. The primary UAV is localized by a robust LiDAR Simultaneous Localization and Mapping (SLAM) algorithm, while the secondary UAV utilizes a Visual-Inertial Odometry (VIO) approach with lower precision and reliability. The LiDAR data are used for markerless relative localization between the UAVs to enable precise guidance of the secondary UAV in the frame of reference of the LiDAR SLAM. The performance of the proposed approach has been extensively verified in simulations and real-world experiments with the algorithms running onboard the UAVs with no external localization infrastructure.

Decentralized Multi-robot Velocity Estimation for UAVs Enhancing Onboard Camera-based Velocity Measurements

  • DOI: 10.1109/IROS47612.2022.9981894
  • Odkaz: https://doi.org/10.1109/IROS47612.2022.9981894
  • Pracoviště: Multirobotické systémy
  • Anotace:
    Within the field of multi-robot systems, developing systems that rely only on onboard sensing without the use of external infrastructure (e.g. GNSS) has many potential applications. However, relying only on visual-based modalities for localization presents challenges in terms of accuracy and reliability. We introduce a decentralized multi-robot lateral velocity estimation method for Unmanned Aerial Vehicles (UAVs) to improve onboard measurements in case GNSS infrastructure is not available. This method relies on sharing the onboard measurements of neighbors, as well as the estimation of the relative motion of a focal UAV within the swarm, based on observation of coworking robots. The proposed velocity estimation method does not rely on centralized communication to achieve high reliability and scalability within the swarm system. The performance of the state estimation approach has been verified in simulations and real-world experiments. The results have shown that a swarm of UAVs using the proposed velocity estimator can stabilize individual robots when their primary onboard localization source is not reliable enough.

Deployment of Reliable Visual Inertial Odometry Approaches for Unmanned Aerial Vehicles in Real-world Environment

  • Autoři: Bednář, J., Ing. Matěj Petrlík, Teixeira Vivaldini, K., doc. Ing. Martin Saska, Dr. rer. nat.,
  • Publikace: 2022 International Conference on Unmanned Aircraft Systems (ICUAS). Piscataway: IEEE Industrial Electronics Society, 2022. p. 167-176. 2022. ISSN 2575-7296. ISBN 978-1-6654-0593-5.
  • Rok: 2022
  • DOI: 10.1109/ICUAS54217.2022.9836067
  • Odkaz: https://doi.org/10.1109/ICUAS54217.2022.9836067
  • Pracoviště: Multirobotické systémy
  • Anotace:
    Integration of Visual Inertial Odometry (VIO) methods into a modular control system designed for deployment of Unmanned Aerial Vehicles (UAVs) and teams of cooperating UAVs in real-world conditions are presented in this paper. Reliability analysis and fair performance comparison of several methods integrated into a control pipeline for achieving full autonomy in real conditions is provided. Although most VIO algorithms achieve excellent localization precision and negligible drift on artificially created datasets, the aspects of reliability in non-ideal situations, robustness to degraded sensor data, and the effects of external disturbances and feedback control coupling are not well studied. These imperfections, which are inherently present in cases of real-world deployment of UAVs, negatively affect the ability of the most used VIO approaches to output a sensible pose estimation. We identify the conditions that are critical for a reliable flight under VIO localization and propose workarounds and compensations for situations in which such conditions cannot be achieved. The performance of the UAV system with integrated VIO methods is quantitatively analyzed w.r.t. RTK ground truth and the ability to provide reliable pose estimation for the feedback control is demonstrated onboard a UAV that is tracking dynamic trajectories under challenging illumination.

MRS Modular UAV Hardware Platforms for Supporting Research in Real-World Outdoor and Indoor Environments

  • DOI: 10.1109/ICUAS54217.2022.9836083
  • Odkaz: https://doi.org/10.1109/ICUAS54217.2022.9836083
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This paper presents a family of autonomous Unmanned Aerial Vehicles (UAVs) platforms designed for a diverse range of indoor and outdoor applications. The proposed UAV design is highly modular in terms of used actuators, sensor configurations, and even UAV frames. This allows to achieve, with minimal effort, a proper experimental setup for single, as well as, multi-robot scenarios. Presented platforms are intended to facilitate the transition from simulations, and simplified laboratory experiments, into the deployment of aerial robots into uncertain and hard-to-model real-world conditions. We present mechanical designs, electric configurations, and dynamic models of the UAVs, followed by numerous recommendations and technical details required for building such a fully autonomous UAV system for experimental verification of scientific achievements. To show strength and high variability of the proposed system, we present results of tens of completely different real-robot experiments in various environments using distinct actuator and sensory configurations.

PACNav: A collective navigation approach for UAV swarms deprived of communication and external localization

  • DOI: 10.1088/1748-3190/ac98e6
  • Odkaz: https://doi.org/10.1088/1748-3190/ac98e6
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This article proposes Persistence Administered Collective Navigation (PACNav) as an approach for achieving decentralized collective navigation of Unmanned Aerial Vehicle (UAV) swarms. The technique is based on the flocking and collective navigation behavior observed in natural swarms, such as cattle herds, bird flocks, and even large groups of humans. As global and concurrent information of all swarm members is not available in natural swarms, these systems use local observations to achieve the desired behavior. Similarly, PACNav relies only on local observations of relative positions of UAVs, making it suitable for large swarms deprived of communication capabilities and external localization systems. We introduce the novel concepts of path persistence and path similarity that allow each swarm member to analyze the motion of other members in order to determine its own future motion. PACNav is based on two main principles: (1) UAVs with little variation in motion direction have high path persistence, and are considered by other UAVs to be reliable leaders; (2) groups of UAVs that move in a similar direction have high path similarity, and such groups are assumed to contain a reliable leader. The proposed approach also embeds a reactive collision avoidance mechanism to avoid collisions with swarm members and environmental obstacles. This collision avoidance ensures safety while reducing deviations from the assigned path. Along with several simulated experiments, we present a real-world experiment in a natural forest, showcasing the validity and effectiveness of the proposed collective navigation approach in challenging environments. The source code is released as open-source, making it possible to replicate the obtained results and facilitate the continuation of research by the community.

Side-Pull Maneuver: A Novel Control Strategy for Dragging a Cable-Tethered Load of Unknown Weight Using a UAV

  • DOI: 10.1109/LRA.2022.3190092
  • Odkaz: https://doi.org/10.1109/LRA.2022.3190092
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This work presents an approach for dealing with suspended-cable load transportation using unmanned aerial vehicles (UAVs), specifically when the cargo overcomes the lifting capacity. Herein, this approach is referred to as the Side-Pull Maneuver (SPM). This maneuver is an alternative and viable strategy for cases where there is no impediment or restriction to dragging the load along a surface, such as with pastures or marine environments. The proposal is based on a joint observation of the thrust and altitude of the UAV. To make this possible, the high-level rigid-body dynamics model is described and represented as an underactuated system. Its altitude-rate control input is then analyzed during flight. A flight state supervisor decides whether the cargo should be carried by lifting or by side-pulling, or whether it should be labeled as nontransportable. Comparative real experiments validate the proposal according to which maneuver (lifting or dragging) is performed for transport.

SphereMap: Dynamic Multi-Layer Graph Structure for Rapid Safety-Aware UAV Planning

  • DOI: 10.1109/LRA.2022.3195194
  • Odkaz: https://doi.org/10.1109/LRA.2022.3195194
  • Pracoviště: Multirobotické systémy
  • Anotace:
    A flexible topological representation consisting of a two-layer graph structure built on-board an Unmanned Aerial Vehicle (UAV) by continuously filling the free space of an occupancy map with intersecting spheres is proposed in this letter. Most state-of-the-art planning methods find the shortest paths while keeping the UAV at a pre-defined distance from obstacles. Planning over the proposed structure reaches this pre-defined distance only when necessary, maintaining a safer distance otherwise, while also being orders of magnitude faster than other state-of-the-art methods. Furthermore, we demonstrate how this graph representation can be converted into a lightweight shareable topological-volumetric map of the environment, which enables decentralized multi-robot cooperation. The proposed approach was successfully validated in several kilometers of real subterranean environments, such as caves, devastated industrial buildings, and in the harsh and complex setting of the final event of the DARPA SubT Challenge, which aims to mimic the conditions of real search and rescue missions as closely as possible, and where our approach achieved the 2nd place in the virtual track.

Swarming of Unmanned Aerial Vehicles by Sharing Distributed Observations of Workspace

  • Autoři: Křížek, M., Ing. Jiří Horyna, doc. Ing. Martin Saska, Dr. rer. nat.,
  • Publikace: 2022 International Conference on Unmanned Aircraft Systems (ICUAS). Piscataway: IEEE Industrial Electronics Society, 2022. p. 300-309. 2022. ISSN 2575-7296. ISBN 978-1-6654-0593-5.
  • Rok: 2022
  • DOI: 10.1109/ICUAS54217.2022.9836073
  • Odkaz: https://doi.org/10.1109/ICUAS54217.2022.9836073
  • Pracoviště: Multirobotické systémy
  • Anotace:
    A control and relative localization approach for a swarm of unmanned aerial vehicles (UAVs) flying in a forest environment is proposed in this paper. To achieve robust mutual relative localization of agents in such an obstacle-rich environment, we propose a decentralized localization approach based on a comparison of the workspace observation by on-board sensors of cooperating UAVs. We propose sharing sparse local obstacle maps to estimate bearing and distance between swarm members by fitting spacialy and time-distributed scans. Moreover, we propose fully decentralized flocking control rules adapted for deployment in such demanding conditions of real forests. The proposed approach was verified in the realistic Gazebo simulator, as well as in outdoor experiments. The approach introduced in this paper was also compared with a state-of-the-art method for relative localization and navigation of a swarm through a forest.

UVDAR-COM: UV-Based Relative Localization of UAVs with Integrated Optical Communication

  • DOI: 10.1109/ICUAS54217.2022.9836151
  • Odkaz: https://doi.org/10.1109/ICUAS54217.2022.9836151
  • Pracoviště: Multirobotické systémy
  • Anotace:
    An optical inter-agent communication integrated into a relative localization system designed for the stabilization of teams of Unmanned Aerial Vehicles (UAVs) is introduced in this paper. We propose an alternative optical communication channel using UV light as a physical transmission medium in free space. The proposed communication system is suitable for implicit short-range inter-agent communication. It is robust against channel saturation and radio jamming that are bottlenecks of radio communication commonly used within aerial swarms. The proposed localization-communication system UVDAR-COM was verified in simulations and real-world experiments. Additionally, we present a simulated experiment showing the performance of the UVDAR-COM system within a decentralized swarm application.

Vehicle Fault-Tolerant Robust Power Transmission Line Inspection Planning

  • DOI: 10.1109/ETFA52439.2022.9921692
  • Odkaz: https://doi.org/10.1109/ETFA52439.2022.9921692
  • Pracoviště: Centrum umělé inteligence, Multirobotické systémy
  • Anotace:
    This paper concerns fault-tolerant power transmission line inspection planning as a generalization of the multiple traveling salesmen problem. The addressed inspection planning problem is formulated as a single-depot multiple-vehicle scenario, where the inspection vehicles are constrained by the battery budget limiting their inspection time. The inspection vehicle is assumed to be an autonomous multi-copter with a wide range of possible flight speeds influencing battery consumption. The inspection plan is represented by multiple routes for vehicles providing full coverage over inspection target power lines. On an inspection vehicle mission interruption, which might happen at any time during the execution of the inspection plan, the inspection is re-planned using the remaining vehicles and their remaining battery budgets. Robustness is introduced by choosing a suitable cost function for the initial plan that maximizes the time window for successful re-planning. It enables the remaining vehicles to successfully finish all the inspection targets using their respective remaining battery budgets. A combinatorial metaheuristic algorithm with various cost functions is used for planning and fast re-planning during the inspection.

A Multi-Layer Software Architecture for Aerial Cognitive Multi-Robot Systems in Power Line Inspection Tasks

  • DOI: 10.1109/ICUAS51884.2021.9476813
  • Odkaz: https://doi.org/10.1109/ICUAS51884.2021.9476813
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This paper presents a multi-layer software architecture to perform cooperative missions with a fleet of quad-rotors providing support in electrical power line inspection operations. The proposed software framework guarantees the compliance with safety requirements between drones and human workers while ensuring that the mission is carried out successfully. Besides, cognitive capabilities are integrated in the multi-vehicle system in order to reply to unforeseen events and external disturbances. The feasibility and effectiveness of the proposed architecture are demonstrated by means of realistic simulations.

A Multi-UAV System for Detection and Elimination of Multiple Targets

  • DOI: 10.1109/ICRA48506.2021.9562057
  • Odkaz: https://doi.org/10.1109/ICRA48506.2021.9562057
  • Pracoviště: Multirobotické systémy
  • Anotace:
    The problem of safe interception of multiple intruder UAVs by a team of cooperating autonomous aerial vehicles is addressed in this paper. The presented work is motivated by the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020 where this task was simplified to an interaction with a set of static and dynamic objects (balloons and a UAV), and by a real autonomous aerial interception system of Eagle.One that our team has been working on. We propose a general control, perception, and coordination system for the fast and reliable interception of targets in a 3D environment relying only on onboard sensors and processing. The proposed methods and the entire complex multi-robot sys- tem were successfully verified in demanding desert conditions, with the main focus on reliability and fast deployment. In the MBZIRC competition, the proposed approach exhibited the greatest reliability and fastest solution. It was crucial to our team in winning the entire competition and achieving the second place in the intruder UAV interception scenario.

Admittance Force-Based UAV-Wall Stabilization and Press Exertion for Documentation and Inspection of Historical Buildings

  • DOI: 10.1109/ICUAS51884.2021.9476873
  • Odkaz: https://doi.org/10.1109/ICUAS51884.2021.9476873
  • Pracoviště: Multirobotické systémy
  • Anotace:
    An approach that enables autonomous Unmanned Aerial Vehicles (UAV) with onboard sensor-based force control to interact with the indoor walls of historical buildings is proposed in this paper. The motivation for enabling UAVs to be pressed against walls is twofold: 1) it enables providing strong-side lighting on places where a light source needs to be remotely pressed against the wall for documentation by another drone with a camera and 2) it is a technique for enabling remote placement of infrastructure in difficult-to-access indoor locations, e.g., smart sensors for continuous monitoring of temperature and humidity. We propose therefore an admittance force-based control system that enables a UAV to interact with a wall in a stabilized manner at a pre-defined location. The UAV is coupled with a mechanism that can measure the interacting force, allowing the proposed controller to be in constant contact with the wall based on a measured force, and to regulate the force to the amount required by a given application. The proposed approach has been verified through numerous simulations in Gazebo and experiments with real robots in GNSS-denied environments relying solely on onboard sensors.

AL-TUNE: A Family of Methods to Effectively Tune UAV Controllers in In-flight Conditions

  • DOI: 10.1007/s10846-021-01441-y
  • Odkaz: https://doi.org/10.1007/s10846-021-01441-y
  • Pracoviště: Multirobotické systémy
  • Anotace:
    In the paper, a family of novel real-time tuning methods for an unmanned aerial vehicle (UAV) altitude controller in in-flight conditions. The methods allow the controller’s gains to be adapted only on the basis of measurements from a basic sensory equipment and by constructing the optimization cost function in an on-line fashion with virtually no impeding computational complexity; in the case of the altitude controller as in this paper for a hexacopter, altitude measurements were used only. The methods are not dependent on the measurement level, and present the approach in a generally applicable form to tuning arbitrary controllers with low number of parameters. Real-world experimental flights, preceded by simulation tests, have shown which method should behave best in a noisy environment when e.g. wind disturbances act on a UAV while it is in autonomous flight. As the methods can potentially be extended to other control loops or controller types, making this a versatile, rapid-tuning tool. It has been shown that a well-tuned controller using the proposed AL-TUNE scheme outperforms controllers that are tuned just to stabilize the system. AL-TUNE provides a new way of using UAVs in terms of adaptivity to changing their dynamic properties and can be deployed rapidly. This enables new applications and extends the usability of fully autonomous UAVs, unlike other tuning methods, which basically require the availability of a UAV model. The core difference with respect to other research from the field is that other authors either use a model of a UAV to optimize the gains analytically or use machine learning techniques, what increases time consumption, whereas the presented methods offer a rapid way to tune controllers, in a reliable way, with deterministic time requirements.

An Application of Stereo Thermal Vision for Preliminary Inspection of Electrical Power Lines by MAVs

  • Autoři: Demkiv, L., Ruffo, M., Silano, G., Bednář, J., doc. Ing. Martin Saska, Dr. rer. nat.,
  • Publikace: 2021 Aerial Robotic Systems Physically Interacting with the Environment (AIRPHARO). Piscataway: IEEE Industrial Electronics Society, 2021. 1th. ISBN 978-1-6654-3389-1.
  • Rok: 2021
  • DOI: 10.1109/AIRPHARO52252.2021.9571025
  • Odkaz: https://doi.org/10.1109/AIRPHARO52252.2021.9571025
  • Pracoviště: Multirobotické systémy
  • Anotace:
    An application of stereo thermal vision to perform preliminary inspection operations of electrical power lines by a particular class of small Unmanned Aerial Vehicles (UAVs), aka Micro Unmanned Aerial Vehicles (MAVs), is presented in this paper. The proposed hardware and software setup allows the detection of overheated power equipment, one of the major causes of power outages. The stereo vision complements the GPS information by finely detecting the potential source of damage while also providing a measure of the harm extension. The reduced sizes and the light weight of the vehicle enable to survey areas otherwise difficult to access with standard UAVs. Gazebo simulations and real flight experiments demonstrate the feasibility and effectiveness of the proposed setup.

An Autonomous Unmanned Aerial Vehicle System for Fast Exploration of Large Complex Indoor Environments

  • DOI: 10.1002/rob.22021
  • Odkaz: https://doi.org/10.1002/rob.22021
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This paper introduces an autonomous system employing multirotor unmanned aerial vehicles for fast 3D exploration and inspection of vast, unknown, dynamic, and complex environments containing large open spaces as well as narrow passages. The system exploits the advantage of small-size aerial vehicles capable of carrying all necessary sensors and computational power while providing full autonomy and mobility in constrained unknown environments. Particular emphasis is put on the robustness of the algorithms with respect to challenging real-world conditions and the real-time performance of all algorithms that enable fast reactions to changes in environment and thus also provide effective use of limited flight time. The system presented here was employed as a part of a heterogeneous ground and aerial system in the modeled Search & Rescue scenario in an unfinished nuclear power plant during the Urban Circuit of the Subterranean Challenge (SubT Challenge) organized by the Defense Advanced Research Projects Agency. The main goal of this simulated disastrous scenario is to autonomously explore and precisely localize specified objects in a completely unknown environment and to report their position before the end of the mission. The proposed system was part of the multirobot team that finished in third place overall and in first place among the self-funded teams. The proposed unmanned aerial vehicle system outperformed all aerial systems participating in the SubT Challenge with respect to versatility, and it was also the self-deployable autonomous aerial system that explored the largest part of the environment.

Autonomous Aerial Filming With Distributed Lighting by a Team of Unmanned Aerial Vehicles

  • DOI: 10.1109/LRA.2021.3098811
  • Odkaz: https://doi.org/10.1109/LRA.2021.3098811
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This letter describes a method for autonomous aerial cinematography with distributed lighting by a team of unmanned aerial vehicles (UAVs). Although camera-carrying multi-rotor helicopters have become commonplace in cinematography, their usage is limited to scenarios with sufficient natural light or of lighting provided by static artificial lights. We propose to use a formation of unmanned aerial vehicles as a tool for filming a target under illumination from various directions, which is one of the fundamental techniques of traditional cinematography. We decompose the multi-UAV trajectory optimization problem to tackle non-linear cinematographic aspects and obstacle avoidance at separate stages, which allows us to re-plan in real time and react to changes in dynamic environments. The performance of our method has been evaluated in realistic simulation scenarios and field experiments, where we show how it increases the quality of the shots and that it is capable of planning safe trajectories even in cluttered environments.

Autonomous Aerial Swarming in GNSS-denied Environments with High Obstacle Density

  • DOI: 10.1109/ICRA48506.2021.9561284
  • Odkaz: https://doi.org/10.1109/ICRA48506.2021.9561284
  • Pracoviště: Multirobotické systémy
  • Anotace:
    The compact flocking of relatively localized Un- manned Aerial Vehicles (UAVs) in high obstacle density areas is discussed in this paper. The presented work tackles realistic scenarios in which the environment map is not known apriori and the use of a global localization system and communication infrastructure is difficult due to the presence of obstacles. To achieve flocking in such a constrained environment, we propose a fully decentralized, bio-inspired control law that uses only onboard sensor data for safe flocking through the environment without any communication with other agents. In the proposed approach, each UAV agent uses onboard sensors to self-localize and estimate the relative position of other agents in its local reference frame. The usability and performance of the proposed approach were verified and evaluated using various experiments in a realistic robotic simulator and a natural forest. The pre- sented experiments also validate the utility of onboard relative localization for autonomous multi-UAV applications in the ab- sence of global localization information and communication.

Autonomous Collaborative Transport of a Beam-Type Payload by a Pair of Multi-rotor Helicopters

  • DOI: 10.1109/ICUAS51884.2021.9476789
  • Odkaz: https://doi.org/10.1109/ICUAS51884.2021.9476789
  • Pracoviště: Multirobotické systémy
  • Anotace:
    Collaborative payload carrying by multi-rotor Unmanned Aerial Vehicles (UAVs) is presented in this paper. We propose a unique control strategy for a pair of UAVs operating with a beam-type payload that is independent of precise localization techniques or unconventional sensor equipment, allowing the system to be operable outside of the laboratory environments. The designed control system comes out with the dynamics of the coupled system, which corresponds to a bicopter aerial vehicle. Such a configuration allows for the use of estimation and control methods typical for a conventional multi-rotor aerial vehicle. The proposed master-slave control system consists of a feedback controller and an MPC reference tracker on the side of the master agent. The slave agent serves as an actuator under command of the master. In addition to the control, a system for payload detection and localization is presented. We fuse the data from RGB and depth cameras to provide sufficient conditions during payload grasping. A state machine was designed to synchronize the master-slave collaborative operations, including payload grasping or response to failure.

Autonomous Firefighting Inside Buildings by an Unmanned Aerial Vehicle

  • DOI: 10.1109/ACCESS.2021.3052967
  • Odkaz: https://doi.org/10.1109/ACCESS.2021.3052967
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This paper presents a novel approach to autonomous extinguishing of indoor fires inside a building by a Micro-scale Unmanned Aerial Vehicle (MAV). In particular, controlling and estimating the MAV state, detection of a building entrance, multi-modal MAV localization during the outdoor-indoor transition, interior motion planning and exploration, fire detection and position estimation, and fire extinguishing are discussed. The performance of these elements, as well as of the entire integrated system, are evaluated in simulations and field tests in various demanding real-world conditions. The system presented here is part of a complex multi-MAV solution that won the Mohamed Bin Zayed International Robotics Challenge 2020 (MBZIRC 2020) competition, and is being used as the core of a fire-fighting Unmanned Aerial System (UAS) industrial platform under development. A video attachment to this paper is available at the website http://mrs.felk.cvut.cz/2020firechallenge-insidefires.

Autonomous Flying into Buildings in a Firefighting Scenario

  • DOI: 10.1109/ICRA48506.2021.9560789
  • Odkaz: https://doi.org/10.1109/ICRA48506.2021.9560789
  • Pracoviště: Multirobotické systémy
  • Anotace:
    We propose an approach enabling an Unmanned Aerial Vehicle (UAV) to autonomously enter a target building through an open window. We use a fusion of depth camera and 2D Light Detection and Ranging (LiDAR) data for window detection and continuous estimation of its position, orientation, and size. The proposed algorithms are capable of running both with and without available a priori information. The obtained detections are utilized for planning collision-free trajectories through the target window. We use a sensor fusion algorithm for robust altitude estimation from laser rangefinder data while flying over ground with inconsistent elevation. Particular focus is given to the transition between outdoor and indoor environments and vice-versa to achieve the required reliability of UAV state estimation. The proposed approach has been verified in multiple real-world experiments, where the UAV was able to successfully enter and leave the target building both under normal conditions and under decreased visibility conditions in a smoke-filled environment.

Bio-inspired compact swarms of unmanned aerial vehicles without communication and external localization

  • DOI: 10.1088/1748-3190/abc6b3
  • Odkaz: https://doi.org/10.1088/1748-3190/abc6b3
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This article presents a unique framework for deploying decentralized and infrastructure-independent swarms of homogeneous aerial vehicles in the real world without explicit communication. This is a requirement in swarm research, which anticipates that global knowledge and communication will not scale well with the number of robots. The system architecture proposed in this article employs the ultraviolet direction and ranging technique to directly perceive the local neighborhood for direct mutual localization of swarm members. The technique allows for decentralization and high scalability of swarm systems, such as can be observed in fish schools, bird flocks, or cattle herds. The bio-inspired swarming model that has been developed is suited for real-world deployment of large particle groups in outdoor and indoor environments with obstacles. The collective behavior of the model emerges from a set of local rules based on direct observation of the neighborhood using onboard sensors only. The model is scalable, srequires only local perception of agents and the environment, and requires no communication among the agents. Apart from simulated scenarios, the performance and usability of the entire framework is analyzed in several real-world experiments with a fully-decentralized swarm of unmanned aerial vehicles (UAVs) deployed in outdoor conditions. To the best of our knowledge, these experiments are the first deployment of decentralized bio-inspired compact swarms of UAVs without the use of a communication network or shared absolute localization. The entire system is available as open-source athttps://github.com/ctu-mrs.

Collision-free trajectory planning of multi-rotor UAVs in a wind condition based on modified potential field

  • DOI: 10.1016/j.mechmachtheory.2020.104140
  • Odkaz: https://doi.org/10.1016/j.mechmachtheory.2020.104140
  • Pracoviště: Multirobotické systémy
  • Anotace:
    A multi-rotor unmanned aerial vehicle (UAV) is a rotorcraft with more than two rotors to enhance payload capability and endurance. A key feature required for the use of these vehicles under complex conditions is a technique to solve the problem of trajectory planning analytically. Hence, this paper proposes an indirect solution of the optimal control problem for path planning of the hexarotor system with different cost functions under different wind loads. First, the generalized Euler-Lagrange formulation is used to derive the dynamic equations of a hexarotor system. Hamiltonian function for a proper objective function is formed, then using the PMP optimality necessary conditions are obtained. Finally, in order to verify the effectiveness of the proposed approach, several simulation studies on a hexacopter are performed for finding the optimal paths at point-to-point motion with different objective functions like minimum effort, collision-free and windy environment. Also, a novel approach for obstacle avoidance of unmanned aerial vehicle is proposed by using a modified artificial potential field which overcomes the local minima issue and finds a practical trajectory for robot path planning. The results clearly show the effectiveness of the proposed approach on the multirotor systems.

Embedded Fast Nonlinear Model Predictive Control for Micro Aerial Vehicles

  • DOI: 10.1007/s10846-021-01522-y
  • Odkaz: https://doi.org/10.1007/s10846-021-01522-y
  • Pracoviště: Multirobotické systémy
  • Anotace:
    Very small size or micro, aerial vehicles are being recently studied due to the large influence of environmental disturbances. The multirotor aerial vehicle (MAV) usually requires control approaches that can guarantee a safe operation. However, limitations with respect to the embedded system (i.e. energy, processing power, memory, etc.) are usually present. In this work, we propose the use of Nonlinear model predictive control (NMPC), which can safely respect input constraints. In contrast, the application of NMPC in embedded systems of Micro-MAV is typically challenging. To solve this issue, we propose a modification on the NMPC called Embedded Fast NMPC that can ensure the implementation of the position controller safely and stably. Micro Multirotor Aerial Vehicles (Micro-MAVs) use low processing power boards. These boards usually rely solely on on-board sensors to perform localization and target detection, which in turn makes this platform suitable for experiments in GNSS-denied environments. We validate our approach with real robot experiments using a Micro-MAV.

Extinguishing of Ground Fires by Fully Autonomous UAVs Motivated by the MBZIRC 2020 Competition

  • DOI: 10.1109/ICUAS51884.2021.9476723
  • Odkaz: https://doi.org/10.1109/ICUAS51884.2021.9476723
  • Pracoviště: Multirobotické systémy
  • Anotace:
    In this paper, a system for autonomous extinguishing of ground fires using the placement of fire blankets by Multi-rotor Unmanned Aerial Vehicles (UAVs) is proposed. The proposed system, relying on the fusion of multiple onboard sensors using only onboard computers, is infrastructure independent with a focus on high reliability in safety-critical missions that require power-on-and-go full autonomy. This task was part of the third challenge of MBZIRC 2020 aimed at the development of autonomous robotic systems for extinguishing fires inside and outside of buildings. The MBZIRC competition promotes the development of such robotics applications that are highly demanded by society and, due to their complexity and required robot abilities, go beyond the current robotic state of the art. As far as we are aware, our team was one of only two teams to achieve successful system for placement of fire blankets fully autonomously with vision-based target localization without using Real-time kinematic (RTK)-global navigation satellite system (GNSS), as was required in the competition and also for the real missions of first responders.

Fast collective evasion in self-localized swarms of unmanned aerial vehicles

  • DOI: 10.1088/1748-3190/ac3060
  • Odkaz: https://doi.org/10.1088/1748-3190/ac3060
  • Pracoviště: Multirobotické systémy
  • Anotace:
    A novel approach for achieving fast evasion in self-localized swarms of unmanned aerial vehicles (UAVs) threatened by an intruding moving object is presented in this paper. Motivated by natural self-organizing systems, the presented approach of fast and collective evasion enables the UAV swarm to avoid dynamic objects (interferers) that are actively approaching the group. The main objective of the proposed technique is the fast and safe escape of the swarm from an interferer discovered in proximity. This method is inspired by the collective behavior of groups of certain animals, such as schools of fish or flocks of birds. These animals use the limited information of their sensing organs and decentralized control to achieve reliable and effective group motion. The system presented in this paper is intended to execute the safe coordination of UAV swarms with a large number of agents. Similar to natural swarms, this system propagates a fast shock of information about detected interferers throughout the group to achieve dynamic and collective evasion. The proposed system is fully decentralized using only onboard sensors to mutually localize swarm agents and interferers, similar to how animals accomplish this behavior. As a result, the communication structure between swarm agents is not overwhelmed by information about the state (position and velocity) of each individual and it is reliable to communication dropouts. The proposed system and theory were numerically evaluated and verified in real-world experiments.

Gamma Radiation Source Localization for Micro Aerial Vehicles with a Miniature Single-Detector Compton Event Camera

  • DOI: 10.1109/ICUAS51884.2021.9476766
  • Odkaz: https://doi.org/10.1109/ICUAS51884.2021.9476766
  • Pracoviště: Multirobotické systémy
  • Anotace:
    A novel method for localization and estimation of compact sources of gamma radiation for Micro Aerial Vehicles (MAVs) is presented in this paper. The method is developed for a novel single-detector Compton camera, developed by the authors. The detector is extremely small and weighs only 40 g, which opens the possibility for use on sub-1 kg class of drones. The Compton camera uses the MiniPIX TPX3 CdTe event camera to measure Compton scattering products of incoming high-energy gamma photons. The 3D position and the sub-nanosecond time delay of the measured scattering products are used to reconstruct sets of possible directions to the source. The proposed method utilizes a filter for fusing the measurements and estimating the radiation source position during the flight. The computations are executed in real-time onboard and allow integration of the detector info into a fully-autonomous system. Moreover, the real-time nature of the estimator potentially allows estimating states of a moving radiation source. The proposed method was validated in simulations and demonstrated in a real-world experiment with a Cs137 radiation source. The approach can localize a gamma source without estimating the gradient or contours of radiation intensity, which opens possibilities for localizing sources in a cluttered and urban environment.

Large-Scale Exploration of Cave Environments by Unmanned Aerial Vehicles

  • DOI: 10.1109/LRA.2021.3098304
  • Odkaz: https://doi.org/10.1109/LRA.2021.3098304
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This letter presents a self-contained system for the robust utilization of aerial robots in the autonomous exploration of cave environments to help human explorers, first responders, and speleologists. The proposed system is generally applicable to an arbitrary exploration task within an unknown and unstructured subterranean environment and interconnects crucial robotic subsystems to provide full autonomy of the robots. Such subsystems primarily include mapping, path and trajectory planning, localization, control, and decision making. Due to the diversity, complexity, and structural uncertainty of natural cave environments, the proposed system allows for the possible use of any arbitrary exploration strategy for a single robot, as well as for a cooperating team. A multi-robot cooperation strategy that maximizes the limited flight time of each aerial robot is proposed for exploration and search & rescue scenarios where the homing of all deployed robots back to an initial location is not required. The entire system is validated in a comprehensive experimental analysis comprising of hours of flight time in a real-world cave environment, as well as by hundreds of hours within a state-of-the-art virtual testbed that was developed for the DARPA Subterranean Challenge robotic competition. Among others, experimental results include multiple real-world exploration flights traveling over 470 m on a single battery in a demanding unknown cave environment.

LIDAR-based Stabilization, Navigation and Localization for UAVs Operating in Dark Indoor Environments

  • DOI: 10.1109/ICUAS51884.2021.9476837
  • Odkaz: https://doi.org/10.1109/ICUAS51884.2021.9476837
  • Pracoviště: Centrum umělé inteligence, Multirobotické systémy
  • Anotace:
    Autonomous operation of UAVs in a closed environment requires precise and reliable pose estimate that can stabilize the UAV without using external localization systems such as GNSS. In this work, we are concerned with estimating the pose from laser scans generated by an inexpensive and lightweight LIDAR. We propose a localization system for lightweight (under 200 g) LIDAR sensors with high reliability in arbitrary environments, where other methods fail. The general nature of the proposed method allows deployment in wide array of applications. Moreover, seamless transitioning between different kinds of environments is possible. The advantage of LIDAR localization is that it is robust to poor illumination, which is often challenging for camera-based solutions in dark indoor environments and in the case of the transition between indoor and outdoor environment. Our approach allows executing tasks in poorly-illuminated indoor locations such as historic buildings and warehouses, as well as in the tight outdoor environment, such as forest, where vision-based approaches fail due to large contrast of the scene, and where large well-equipped UAVs cannot be deployed due to the constrained space.

Mobile Manipulator for Autonomous Localization, Grasping and Precise Placement of Construction Material in a Semi-structured Environment

  • DOI: 10.1109/LRA.2021.3061377
  • Odkaz: https://doi.org/10.1109/LRA.2021.3061377
  • Pracoviště: Katedra počítačů, Centrum umělé inteligence, Multirobotické systémy
  • Anotace:
    Mobile manipulators have the potential to revolutionize modern agriculture, logistics and manufacturing. In this work, we present the design of a ground-based mobile manipulator for automated structure assembly. The proposed system is capable of autonomous localization, grasping, transportation and deployment of construction material in a semi-structured environment. Special effort was put into making the system invariant to lighting changes, and not reliant on external positioning systems. Therefore, the presented system is self-contained and capable of operating in outdoor and indoor conditions alike. Finally, we present means to extend the perceptive radius of the vehicle by using it in cooperation with an autonomous drone, which provides aerial reconnaissance. Performance of the proposed system has been evaluated in a series of experiments conducted in real-world conditions.

Multi-Robot Sensor Fusion Target Tracking with Observation Constraints

  • DOI: 10.1109/ACCESS.2021.3070180
  • Odkaz: https://doi.org/10.1109/ACCESS.2021.3070180
  • Pracoviště: Multirobotické systémy
  • Anotace:
    In Mobile Robotics, visual tracking is an extremely important sub-problem. Some solutions found to reduce the problems arising from partial and total occlusion are the use of multiple robots. In this work, we propose a three-dimensional space target tracking based on a constrained multi-robot visual data fusion on the occurrence of partial and total occlusion. To validate our approach we first implemented a non-cooperative visual tracking where only the data from a single robot is used. Then, a cooperative visual tracking was tested, where the data from a team of robots is fused using a particle filter. To evaluate both approaches, a visual tracking environment with partial and total occlusions was created where the tracking was performed by a team of robots. The result of the experiment shows that the non-cooperative approach presented a lower computational cost than the cooperative approach but the inferred trajectory was impaired by the occlusions, a fact that did not occur in the cooperative approach due to the data fusion.

Multi-tour Set Traveling Salesman Problem in Planning Power Transmission Line Inspection

  • DOI: 10.1109/LRA.2021.3091695
  • Odkaz: https://doi.org/10.1109/LRA.2021.3091695
  • Pracoviště: Centrum umělé inteligence, Multirobotické systémy
  • Anotace:
    This paper concerns optimal power transmission line inspection formulated as a proposed generalization of the traveling salesman problem for a multi-route one-depot scenario. The problem is formulated for an inspection vehicle with a limited travel budget. Therefore, the solution can be composed of multiple runs to provide full coverage of the given power lines. Besides, the solution indicates how many vehicles can perform the inspection in a single run. The optimal solution of the problem is solved by the proposed Integer Linear Programming (ILP) formulation, which is, however, very computationally demanding. Therefore, the computational requirements are addressed by the combinatorial metaheuristic. The employed greedy randomized adaptive search procedure is significantly less demanding while providing competitive solutions and scales better with the problem size than the ILP-based approach. The proposed formulation and algorithms are demonstrated in a real-world scenario to inspect power line segments at the electrical substation.

Optimum Trajectory Planning for Multi-Rotor UAV Relays with Tilt and Antenna Orientation Variations

  • DOI: 10.23919/EUSIPCO54536.2021.9616232
  • Odkaz: https://doi.org/10.23919/EUSIPCO54536.2021.9616232
  • Pracoviště: Multirobotické systémy
  • Anotace:
    Multi-rotor Unmanned Aerial Vehicles (UAVs) need to tilt in order to move; this modifies the UAV's antenna orientation. We consider the scenario where a multi-rotor UAV serves as a communication relay between a Base Station (BS) and another UAV. We propose a framework to generate feasible trajectories for the multi-rotor UAV relay while considering its motion dynamics and the motion-induced changes of the antenna orientation. The UAV relay's trajectory is optimized to maximize the end-to-end number of bits transmitted. Numerical simulations in MATLAB and Gazebo show the benefits of accounting for the antenna orientation variations due to the UAV tilt.

Power Line Inspection Tasks with Multi-Aerial Robot Systems via Signal Temporal Logic Specifications

  • DOI: 10.1109/LRA.2021.3068114
  • Odkaz: https://doi.org/10.1109/LRA.2021.3068114
  • Pracoviště: Multirobotické systémy
  • Anotace:
    A framework for computing feasible and constrained trajectories for a fleet of quad-rotors leveraging on Signal Temporal Logic (STL) specifications for power line inspection tasks is proposed in this paper. The planner allows the formulation of complex missions that avoid obstacles and maintain a safe distance between drones while performing the planned mission. An optimization problem is set to generate optimal strategies that satisfy these specifications and also take vehicle constraints into account. Further, an event-triggered replanner is proposed to reply to unforeseen events and external disturbances. An energy minimization term is also considered to implicitly save quad-rotors battery life while carrying out the mission. Numerical simulations in MATLAB and experimental results show the validity and the effectiveness of the proposed approach, and demonstrate its applicability in real-world scenarios.

Safe Documentation of Historical Monuments by an Autonomous Unmanned Aerial Vehicle

  • DOI: 10.3390/ijgi10110738
  • Odkaz: https://doi.org/10.3390/ijgi10110738
  • Pracoviště: Multirobotické systémy
  • Anotace:
    The use of robotic systems, especially multi-rotor aerial vehicles, in the documentation of historical buildings and cultural heritage monuments has become common in recent years. However, the teleoperated robotic systems have significant limitations encouraging the ongoing development of autonomous unmanned aerial vehicles (UAVs). The autonomous robotic platforms provide a more accurate and safe measurement in distant and difficult to access areas than their teleoperated counterpart. Through the use of autonomous aerial robotic systems, access to such places by humans and building of external infrastructures like scaffolding for documentation purposes is no longer necessary. In this work, we aim to present a novel autonomous unmanned aerial vehicle designed for the documentation of hardly attainable areas of historical buildings. The prototype of this robot was tested in several historical monuments comprising scanned objects located in dark and hardly accessible areas in the upper parts of tall naves. This manuscript presents the results from two specific places: the Church of St. Anne and St. Jacob the Great in Stará Voda, and St. Maurice Church in Olomouc, both in the Czech Republic. Finally, we also compare the three-dimensional map obtained with the measurements made by the 3D laser scanner carried onboard UAV against the ones performed by a 3D terrestrial laser scanner.

Safe Tightly-Constrained UAV Swarming in GNSS-denied Environments

  • DOI: 10.1109/ICUAS51884.2021.9476794
  • Odkaz: https://doi.org/10.1109/ICUAS51884.2021.9476794
  • Pracoviště: Multirobotické systémy
  • Anotace:
    —A decentralized algorithm for flocking of Unmanned Aerial Vehicles (UAV) in environments with high obstacle density is proposed in this work. The method combines a local planning loop with bio-inspired swarming rules for navigating a compact UAV flock in a real workspace without relying on external infrastructures, such as motion capture system and GNSS. The group stability and coherence are achieved by employing a purposely designed onboard UVDAR system for mutual localization of teammates in local proximity of each UAV. The required robustness and scalability of the multi-UAV system are therefore achieved without any need for communication among the swarm particle. Such minimal sensory and communication requirements have allowed the system to become a backup technique for centralized multirobot systems in case of communication and GNSS dropout. The proposed approach has been verified in numerous simulations and real experiments inside a forest that represents one of the most challenging environments for deployment of compact groups of aerial vehicles.

Self-Organized UAV Flocking Based on Proximal Control

  • DOI: 10.1109/ICUAS51884.2021.9476847
  • Odkaz: https://doi.org/10.1109/ICUAS51884.2021.9476847
  • Pracoviště: Multirobotické systémy
  • Anotace:
    In this work, we address the problem of achieving cohesive and aligned flocking (collective motion) with a swarm of unmanned aerial vehicles (UAVs). We propose a method that requires only onboard sensing of the relative range and bearing of neighboring UAVs, and therefore requires only proximal control for achieving formation. Our method efficiently achieves flocking in the absence of any explicit orientation information exchange (alignment control), and achieves flocking in a random direction without externally provided directional information. To implement proximal control, the Lennard-Jones potential function is used to maintain cohesiveness and avoid collisions. Our approach may be used independently from any external positioning system such as GNSS or Motion Capture, and can therefore be used in GNSS-denied environments. The performance of the approach was tested in real-world conditions by experiments with UAVs that rely only on a relative visual localization system called UVDAR, proposed by our group. To evaluate the degree of alignment and cohesiveness, we used the order metric and the steady-state value.

The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles

  • DOI: 10.1007/s10846-021-01383-5
  • Odkaz: https://doi.org/10.1007/s10846-021-01383-5
  • Pracoviště: Multirobotické systémy
  • Anotace:
    We present a multirotor Unmanned Aerial Vehicle (UAV) control and estimation system for supporting replicable research through realistic simulations and real-world experiments. We propose a unique multi-frame localization paradigm for estimating the states of a UAV in various frames of reference using multiple sensors simultaneously. The system enables complex missions in GNSS and GNSS-denied environments, including outdoor-indoor transitions and the execution of redundant estimators for backing up unreliable localization sources. Two feedback control designs are presented: one for precise and aggressive maneuvers, and the other for stable and smooth flight with a noisy state estimate. The proposed control and estimation pipeline are constructed without using the Euler/Tait-Bryan angle representation of orientation in 3D. Instead, we rely on rotation matrices and a novel heading-based convention to represent the one free rotational degree-of-freedom in 3D of a standard multirotor helicopter. We provide an actively maintained and well-documented open-source implementation, including realistic simulation of UAV, sensors, and localization systems. The proposed system is the product of years of applied research on multi-robot systems, aerial swarms, aerial manipulation, motion planning, and remote sensing. All our results have been supported by real-world system deployment that subsequently shaped the system into the form presented here. In addition, the system was utilized during the participation of our team from the Czech Technical University in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions, and also in the DARPA Subterranean challenge. Each time, our team was able to secure top places among the best competitors from all over the world.

Trajectory Planning of Quadrotor Systems for Various Objective Functions

  • DOI: 10.1017/S0263574720000247
  • Odkaz: https://doi.org/10.1017/S0263574720000247
  • Pracoviště: Multirobotické systémy
  • Anotace:
    Quadrotors are unmanned aerial vehicles with many potential applications ranging from mapping to supporting rescue operations. A key feature required for the use of these vehicles under complex conditions is a technique to analytically solve the problem of trajectory planning. Hence, this paper presents a heuristic approach for optimal path planning that the optimization strategy is based on the indirect solution of the open-loop optimal control problem. Firstly, an adequate dynamic system modeling is considered with respect to a configuration of a commercial quadrotor helicopter. The model predicts the effect of the thrust and torques induced by the four propellers on the quadrotor motion. Quadcopter dynamics is described by differential equations that have been derived by using the Newton–Euler method. Then, a path planning algorithm is developed to find the optimal trajec- tories that meet various objective functions, such as fuel efficiency, and guarantee the flight stability and high-speed operation. Typically, the necessary condition of optimality for a constrained opti- mal control problem is formulated as a standard form of a two-point boundary-value problem using Pontryagin’s minimum principle. One advantage of the proposed method can solve a wide range of optimal maneuvers for arbitrary initial and final states relevant to every considered cost function. In order to verify the effectiveness of the presented algorithm, several simulation and experiment stud- ies are carried out for finding the optimal path between two points with different objective functions by using MATLAB software. The results clearly show the effect of the proposed approach on the quadrotor systems.

A Robust UAV System for Operations in a Constrained Environment

  • DOI: 10.1109/LRA.2020.2970980
  • Odkaz: https://doi.org/10.1109/LRA.2020.2970980
  • Pracoviště: Katedra kybernetiky, Centrum umělé inteligence, Multirobotické systémy
  • Anotace:
    In this letter we present an autonomous system intended for aerial monitoring, inspection and assistance in Search and Rescue (SAR) operations within a constrained workspace. The proposed system is designed for deployment in demanding real-world environments with extremely narrow passages only slightly wider than the aerial platform, and with limited visibility due to the absence of illumination and the presence of dust. The focus is on precise localization in an unknown environment, high robustness, safety and fast deployment without any need to install an external infrastructure such as an external computer and localization system. These are the main requirements of the targeted SAR scenarios. The performance of the proposed system was successfully evaluated in the Tunnel Circuit of the DARPA Subterranean Challenge, where the UAV cooperated with ground robots to precisely localize artifacts in a coal mine tunnel system. The challenge was unique due to the intention of the organizers to emulate the unpredictable conditions of a real SAR operation, in which there is no prior knowledge of the obstacles that will be encountered.

Autonomous Reflectance Transformation Imaging by a Team of Unmanned Aerial Vehicles

  • DOI: 10.1109/LRA.2020.2970646
  • Odkaz: https://doi.org/10.1109/LRA.2020.2970646
  • Pracoviště: Multirobotické systémy
  • Anotace:
    A Reflectance Transformation Imaging technique (RTI) realized by multi-rotor Unmanned Aerial Vehicles (UAVs) with a focus on deployment in difficult to access buildings is presented in this letter. RTI is a computational photographic method that captures a surface shape and color of a subject and enables its interactive re-lighting from any direction in a software viewer, revealing details that are not visible with the naked eye. The input of RTI is a set of images captured by a static camera, each one under illumination from a different known direction. We present an innovative approach applying two multi-rotor UAVs to perform this scanning procedure in locations that are hardly accessible or even inaccessible for people. The proposed system is designed for its safe deployment within real-world scenarios in historical buildings with priceless historical value.

Cooperative path planning for multiple MAVs operating in unknown environments

  • DOI: 10.1109/ICUAS48674.2020.9213896
  • Odkaz: https://doi.org/10.1109/ICUAS48674.2020.9213896
  • Pracoviště: Multirobotické systémy
  • Anotace:
    In recent years, Micro Aerial Vehicles (MAVs) have become widely available and are successfully used in many real scenarios. While the early applications like surveillance mostly utilized single MAVs or a group of multiple, yet non-cooperative MAVs, recent research is more focused on a group of cooperating MAVs. A typical example is the payload transport task, where multiple MAVs carry a single object. This problem has been studied mainly from the control theory point of view, providing robust control to cooperating MAVs using the dynamics of the whole system. Real applications, however, require operating in unknown environments with obstacles, which needs motion planning. In this paper, we propose a novel motion planning method for multiple MAVs operating in unknown environments. The proposed work is based on the Sensor-based Random Trees method (SRT), which was originally intended for exploration of unknown environments. We extend the method for online path planning of multi MAVs. In the proposed method, each MAV makes a motion plan and exchanges key waypoints with other MAVs to ensure that their mutual positions satisfy the mission constraints. The performance of the method is demonstrated in various simulation experiments.

DARPA Subterranean Challenge: Multi-robotic exploration of underground environments

  • DOI: 10.1007/978-3-030-43890-6_22
  • Odkaz: https://doi.org/10.1007/978-3-030-43890-6_22
  • Pracoviště: Centrum umělé inteligence, Vidění pro roboty a autonomní systémy, Multirobotické systémy
  • Anotace:
    The Subterranean Challenge (SubT) is a contest organised by the Defense Advanced Research Projects Agency (DARPA). The contest reflects the requirement of increasing safety and efficiency of underground search-and-rescue missions. In the SubT challenge, teams of mobile robots have to detect, localise and report positions of specific objects in an underground environment. This paper provides a description of the multi-robot heterogeneous exploration system of our CTU-CRAS team, which scored third place in the Tunnel Circuit round, surpassing the performance of all other non-DARPA-funded competitors. In addition to the description of the platforms, algorithms and strategies used, we also discuss the lessons-learned by participating at such contest.

Design of an Active-Reliable Grasping Mechanism for Autonomous Unmanned Aerial Vehicles

  • Autoři: Nedungadi, A., doc. Ing. Martin Saska, Dr. rer. nat.,
  • Publikace: 6th International Workshop on Modelling and Simulation for Autonomous Systems. Wien: Springer, 2020. p. 162-179. ISSN 1611-3349. ISBN 9783030438890.
  • Rok: 2020
  • DOI: 10.1007/978-3-030-43890-6_13
  • Odkaz: https://doi.org/10.1007/978-3-030-43890-6_13
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This paper presents a novel design of an active grasping mechanism for autonomous Unmanned Aerial Vehicles (UAVs) aimed to carry ferromagnetic objects in assembly tasks. The proposed design uses electromagnets along with a combination of sensors to provide fast and reliable feedback. The designed gripper with its control system is aimed to be fully autonomous and will be employed in a task in the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020 competition where a group of autonomous UAVs cooperatively build a wall. The design is optimized for the Tarot 650 drone platform and for outdoor operation, while taking into consideration robustness of performance and resilience to aerial maneuvers. We describe the design of the gripper, the overall system and the approach used to obtain the feedback from the sensors which is crucial for robust aerial grasping and for high level planning of the assembly task. Various outdoor experiments were conducted on fabricated bricks to verify the proposed approach and to demonstrate the ability of the system to autonomously build a wall.

Dronument: System for Reliable Deployment of Micro Aerial Vehicles in Dark Areas of Large Historical Monuments

  • DOI: 10.1109/LRA.2020.2969935
  • Odkaz: https://doi.org/10.1109/LRA.2020.2969935
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This letter presents a self-contained system for robust deployment of autonomous aerial vehicles in environments without access to global navigation systems and with limited lighting conditions. The proposed system, application-tailored for documentation in dark areas of large historical monuments, uses a unique and reliable aerial platform with a multi-modal lightweight sensory setup to acquire data in human-restricted areas with adverse lighting conditions, especially in areas that are high above the ground. The introduced localization method relies on an easy-to-obtain 3-D point cloud of a historical building, while it copes with a lack of visible light by fusing active laser-based sensors. The approach does not rely on any external localization, or on a preset motion-capture system. This enables fast deployment in the interiors of investigated structures while being computationally undemanding enough to process data online, onboard an MAV equipped with ordinary processing resources. The reliability of the system is analyzed, is quantitatively evaluated on a set of aerial trajectories performed inside a real-world church, and is deployed onto the aerial platform in the position control feedback loop to demonstrate the reliability of the system in the safety-critical application of historical monuments documentation.

Fast Nonlinear Model Predictive Control for Very-Small Aerial Vehicles

  • DOI: 10.1109/ICUAS48674.2020.9213924
  • Odkaz: https://doi.org/10.1109/ICUAS48674.2020.9213924
  • Pracoviště: Multirobotické systémy
  • Anotace:
    Highly dynamic systems such as Micro Multirotor Aerial Vehicles (Micro-MAVs) require control approaches that enable safe operation where extreme limitations in embedded systems, such as energy, processing capability and memory, are present. Nonlinear model predictive control (NMPC) approaches can respect operational constraints in a safe manner. However, they are typically challenging to implement using embedded computers on-board of Micro-MAVs. Implementations of classic NMPC approaches rely on high-performance computers. In this work, we propose a fast nonlinear model predictive control approach that ensures the stabilization and control of Micro Multirotor Aerial Vehicles (Micro-MAVs). This aerial robotic system uses a low processing power board that relies solely on on-board sensors to localize itself, which makes it suitable for experiments in GPS-denied environments. The proposed approach has been verified in numerical simulations using processing capabilities that are available on Micro-MAVs.

Formation control of unmanned micro aerial vehicles for straitened environments

  • DOI: 10.1007/s10514-020-09913-0
  • Odkaz: https://doi.org/10.1007/s10514-020-09913-0
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This paper presents a novel approach for control and motion planning of formations of multiple unmanned micro aerial vehicles(multi-rotor helicopters, in the literature also often called unmanned aerial vehicles—UAVs or unmanned aerial system—UAS) in cluttered GPS-denied on straitened environments. The proposed method enables us to autonomously design complexmaneuvers of a compact Micro Aerial Vehicles (MAV) team in a virtual-leader-follower scheme. The results of the motionplanning approach and the required stability of the formation are achieved by migrating the virtual leader along with the hullsurrounding the formation. This enables us to suddenly change the formation motion in all directions, independently from thecurrent orientation of the formation, and therefore to fully exploit the maneuverability of small multi-rotor helicopters. Theproposed method was verified and its performance has been statistically evaluated in numerous simulations and experimentswith a fleet of MAVs.

Localization of Ionizing Radiation Sources by Cooperating Micro Aerial Vehicles With Pixel Detectors in Real-Time

  • DOI: 10.1109/LRA.2020.2978456
  • Odkaz: https://doi.org/10.1109/LRA.2020.2978456
  • Pracoviště: Multirobotické systémy
  • Anotace:
    We provide a complex software package allowing the user to deploy multiple ionizing radiation sources and detectors modeled after the Timepix miniature pixel detector. The software is provided to the community as open-source, and allows preliminary testing and method development even without a pixel detector or radiation sources. Our simulation model utilizes ray-tracing and Monte Carlo methods to resolve interactions of ionizing radiation with the detector, obstacles and the atmosphere. An open-source implementation is provided as a plugin for Gazebo, a simulator popular within the robotics community. The plugin is capable of simulating radiation sources with activities in the order of GBq1 in real-time with a conventional PC. We also provide a ROS interface, which allows full integration of the Timepix pixel detector into a robotic system. The credibility and the precision of the simulator plugin were confirmed via a real-world experiment with a micro aerial vehicle (MAV) equipped with a Timepix detector mapping the radiation intensity of an Am-241 sample. Finally, we present a method for cooperative localization of a source of ionizing radiation by a group of autonomous MAVs in an environment with obstacles.

Marker-Less Micro Aerial Vehicle Detection and Localization Using Convolutional Neural Networks

  • DOI: 10.1109/LRA.2020.2972819
  • Odkaz: https://doi.org/10.1109/LRA.2020.2972819
  • Pracoviště: Multirobotické systémy
  • Anotace:
    A relative localization system for micro aerial vehicles (MAVs), which is able to work without any markers or other specialized equipment, is presented in this letter. The system utilizes images from an onboard camera to detect nearby MAVs using a convolutional neural network. When compared to traditional computer vision-based relative localization systems, this approach removes the need for specialized markers to be placed on the MAVs, saving weight and space, while also enabling localization of noncooperating robots. The system is designed and implemented to run online, onboard an MAV platform in order to enable relative stabilization of several MAVs in a formation or swarm-like behavior, when operating in a closed feedback loop with the control system of the MAVs. We demonstrate the viability and robustness of the proposed method in real-world experiments. The method was also designed for the purpose of autonomous aerial interception and is a fitting complement to other MAV detection and relative localization methods for this purpose, as is shown in the experiments.

On training datasets for machine learning-based visual relative localization of micro-scale UAVs

  • DOI: 10.1109/ICRA40945.2020.9196947
  • Odkaz: https://doi.org/10.1109/ICRA40945.2020.9196947
  • Pracoviště: Multirobotické systémy
  • Anotace:
    By leveraging our relative Micro-scale Unmanned Aerial Vehicle localization sensor UVDAR, we generated an automatically annotated dataset MIDGARD, which the community is invited to use for training and testing their machine learning systems for the detection and localization of Microscale Unmanned Aerial Vehicles (MAVs) by other MAVs. Furthermore, we provide our system as a mechanism for rapidly generating custom annotated datasets specifically tailored for the needs of a given application. The recent literature is rich in applications of machine learning methods in automation and robotics. One particular subset of these methods is visual object detection and localization, using means such as Convolutional Neural Networks, which nowadays enable objects to be detected and classified with previously inconceivable precision and reliability. Most of these applications, however, rely on a carefully crafted training dataset of annotated camera footage. These must contain the objects of interest in environments similar to those where the detector is expected to operate. Notably, the positions of the objects must be provided in annotations. For non-laboratory settings, the construction of such datasets requires many man-hours of manual annotation, which is especially the case for use onboard Micro-scale Unmanned Aerial Vehicles. In this paper, we are providing for the community a practical alternative to that kind of approach.

UAV Vision-Based Nonlinear Formation Control Applied to Inspection of Electrical Power Lines

  • DOI: 10.1109/ICUAS48674.2020.9213967
  • Odkaz: https://doi.org/10.1109/ICUAS48674.2020.9213967
  • Pracoviště: Multirobotické systémy
  • Anotace:
    Cooperation of humans workers and a team of UAV co-workers for inspection and maintenance of electrical power is the main motivation of research presented in this paper. Collaborative human-UAV works at height are beneficial from several reasons including providing images from the ideal point of view, monitoring of the safety of individual workers, and even aerial delivering of required tools. These tasks also involve cognitive capabilities in the monitoring of the workers and the detection of unsafe behaviors, transportation of tools or parts needed by the workers and collective manipulation with the workers. In general, interaction of humans and teams of UAVs becomes an important task as aerial robots are widely spread in various applications that require the presence of people in their workspace. To achieve such interaction, group control of multiple UAVs must take states of workers (e.g. position relative to aerial co-workers and prediction of worker's future behavior), maintaining an adaptable formation and maximizing the observation of the worker. Thus, we propose in this work, a distributed vision-based nonlinear formation control (DVNFC) approach that results in an adaptable formation where the controller minimizes the error in observation always maintaining the visualization of the human by the whole formation. We performed several numerical simulations using ROS/Gazebo with real-time visual feedback to validate our approach.

Wildfire Fighting by Unmanned Aerial System Exploiting Its Time-Varying Mass

  • DOI: 10.1109/LRA.2020.2972827
  • Odkaz: https://doi.org/10.1109/LRA.2020.2972827
  • Pracoviště: Katedra řídicí techniky, Multirobotické systémy
  • Anotace:
    This paper presents an approach for accurately dropping a relatively large amount of fire retardant, water or some other extinguishing agent onto a wildfire from an autonomous unmanned aerial vehicle (UAV), in close proximity to the epicenter of the fire. The proposed approach involves a risky maneuver outside of the safe flight envelope of the UAV. This maneuver exploits the expected weight reduction resulting from the release of the payload, enabling the UAV to recover without impacting the terrain. The UAV is tilted to high pitch angles, at which the thrust may be pointed almost horizontally. The vehicle can therefore achieve higher horizontal speeds than would be allowed by conventional motion planners. This high speed allows the UAV to significantly reduce the time spent close to the fire. As a result, the overall high heat exposure is reduced, and the payload can be dropped closer to the target, minimizing its dispersion. A constrained optimal control problem (OCP) is solved taking into account environmental parameters such as wind and terrain gradients, as well as various payload releasing mechanisms. The proposed approach was verified in simulations and in real experiments. Emphasis was put on the real time recalculation of the solution, which will enable future adaptation into a model predictive controller (MPC) scheme.

Autonomous compact monitoring of large areas using micro aerial vehicles with limited sensory information and computational resources

  • Autoři: Ješke, P., Klouček, Š., doc. Ing. Martin Saska, Dr. rer. nat.,
  • Publikace: Modelling and Simulation for Autonomous Systems (MESAS 2018). Cham: Springer International Publishing AG, 2019. p. 158-171. ISSN 0302-9743. ISBN 978-3-030-14983-3.
  • Rok: 2019
  • DOI: 10.1007/978-3-030-14984-0_14
  • Odkaz: https://doi.org/10.1007/978-3-030-14984-0_14
  • Pracoviště: Multirobotické systémy
  • Anotace:
    In this paper, a new approach for autonomous real-time monitoring of large areas using small unmanned areal vehicles with limited sensory and computational resources is proposed. Most of the existing solutions of area monitoring require large aerial vehicles to be equipped with a list of expensive sensors and powerful computational resources. Recent progress in Micro Aerial Vehicles (MAVs) allows us to consider their utilization in new tasks, such as the considered compact monitoring, which are dedicated to large well-equipped aerial vehicles so-far only. The proposed solution enables online area monitoring using MAVs equipped with minimal sensory and computational resources and to process the obtained data only with cell phones capabilities, which considerably extends application possibilities of the drone technology. The proposed methodology was verified under various outdoor conditions of real application scenarios with a simple autonomous MAV controlled by the onboard model predictive control in a robotic operation system (ROS), while the user interface was provided on a standard smartphone with Android OS.

Autonomous landing on a moving vehicle with an unmanned aerial vehicle

  • DOI: 10.1002/rob.21858
  • Odkaz: https://doi.org/10.1002/rob.21858
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This paper addresses the perception, control, and trajectory planning for an aerial platform to identify and land on a moving car at 15 km/hr. The hexacopter unmanned aerial vehicle (UAV), equipped with onboard sensors and a computer, detects the car using a monocular camera and predicts the car future movement using a nonlinear motion model. While following the car, the UAV lands on its roof, and it attaches itself using magnetic legs. The proposed system is fully autonomous from takeoff to landing. Numerous field tests were conducted throughout the year‐long development and preparations for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 competition, for which the system was designed. We propose a novel control system in which a model predictive controller is used in real time to generate a reference trajectory for the UAV, which are then tracked by the nonlinear feedback controller. This combination allows to track predictions of the car motion with minimal position error. The evaluation presents three successful autonomous landings during the MBZIRC 2017, where our system achieved the fastest landing among all competing teams.

Cooperative autonomous search, grasping, and delivering in a treasure hunt scenario by a team of unmanned aerial vehicles

  • DOI: 10.1002/rob.21816
  • Odkaz: https://doi.org/10.1002/rob.21816
  • Pracoviště: Centrum umělé inteligence, Multirobotické systémy
  • Anotace:
    This paper addresses the problem of autonomous cooperative localization, grasping and delivering of colored ferrous objects by a team of unmanned aerial vehicles (UAVs). In the proposed scenario, a team of UAVs is required to maximize the reward by collecting colored objects and delivering them to a predefined location. This task consists of several subtasks such as cooperative coverage path planning, object detection and state estimation, UAV self‐localization, precise motion control, trajectory tracking, aerial grasping and dropping, and decentralized team coordination. The failure recovery and synchronization job manager is used to integrate all the presented subtasks together and also to decrease the vulnerability to individual subtask failures in real‐world conditions. The whole system was developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017, where it achieved the highest score and won Challenge No. 3-Treasure Hunt. This paper does not only contain results from the MBZIRC 2017 competition but it also evaluates the system performance in simulations and field tests that were conducted throughout the year‐long development and preparations for the competition.

Cooperative Transport of Large Objects by a Pair of Unmanned Aerial Systems using Sampling-based Motion Planning

  • DOI: 10.1109/ETFA.2019.8869298
  • Odkaz: https://doi.org/10.1109/ETFA.2019.8869298
  • Pracoviště: Katedra kybernetiky, Multirobotické systémy
  • Anotace:
    Cooperative carrying of large, cable-suspended payloads by a pair of cooperating unmanned aerial vehicles (UAVs) is tackled in this paper. The proposed system, involving a sampling-based motion planning algorithm and a model predictive control-based coordination of UAVs, aims to achieve a smooth and reliable flight performance in environments with obstacles. The motion planning is designed to satisfy constraints on relative positions of UAVs, which are defined from the cooperative transport task and by onboard mutual localization, which is used for real-time estimation of states of neighboring robots carrying the object. Besides, a guiding principle with a cost-driven expansion is employed to steer the growth of a Rapidly-exploring Random Tree (RRT) to keep the coupled system of UAVs and the object as close to desired mutual positions as possible. A significant deviation of the controlled system from the desired configuration, by increasing or decreasing the relative distance between UAVs carrying the cable-suspended object, is achieved only if it is required by environment constraints (e.g. in narrow passages), while the allowed limits are always satisfied. Using the guiding principle enables us to find feasible solutions of the problem in a reasonable short time using onboard computer even in environments with a complicated structure of obstacles. The proposed system was evaluated in numerous simulations, compared with state-of-the-art solutions using statistical sets of results, and its performance and reliability were verified in experiments in real-world conditions.

Coverage Optimization in the Cooperative Surveillance Task using Multiple Micro Aerial Vehicles

  • DOI: 10.1109/SMC.2019.8914330
  • Odkaz: https://doi.org/10.1109/SMC.2019.8914330
  • Pracoviště: Katedra kybernetiky, Multirobotické systémy
  • Anotace:
    In the task of cooperative surveillance using Micro Aerial Vehicles (MAVs), MAVs cooperatively observe a given set of Areas of Interest (AoI). The missions are usually prepared in a decoupled manner: first, the sensing locations are found, followed by computations of the trajectories assuming GPS-based localization. The precision of GPS may, however, be insufficient to keep the MAVs in compact groups, which may lead to mutual collisions. To avoid the collisions between MAVs, a camera-based on-board localization has to be used. This however requires to maintain positions of the team members in the given range to enable reliable on-board localization (each MAV has to be visible from other ones). The task of the mission planning is to find an appropriate distribution of MAVs above AoIs together with feasible trajectories from a depot to reach these locations. The on-board localization constraints and MAV motion constraints have to be satisfied during the entire mission. We propose a modification of RRT (Rapidly Exploring Random Tree) for this mission planning. The algorithm first explores the state space to find suitable sensing locations together with feasible trajectories towards them. Then, the sensing locations are optimized using Particle Swarm optimization (PSO). The proposed method has been verified in numerous simulations and outdoor experiments. The achieved results exhibit significantly better performance in terms of lower computational power and complexity of solved scenarios than the state-of-the-art solutions.

Data Collection Planning with Non-zero Sensing Distance for a Budget and Curvature Constrained Unmanned Aerial Vehicle

  • DOI: 10.1007/s10514-019-09844-5
  • Odkaz: https://doi.org/10.1007/s10514-019-09844-5
  • Pracoviště: Centrum umělé inteligence, Multirobotické systémy
  • Anotace:
    Data collection missions are one of the many effective use cases of Unmanned Aerial Vehicles (UAVs), where the UAV is required to visit a predefined set of target locations to retrieve data. However, the flight time of a real UAV is time constrained, and therefore only a limited number of target locations can typically be visited within the mission. In this paper, we address the data collection planning problem called the Dubins Orienteering Problem with Neighborhoods (DOPN), which sets out to determine the sequence of visits to the most rewarding subset of target locations, each with an associated reward, within a given travel budget. The objective of the DOPN is thus to maximize the sum of the rewards collected from the visited target locations using a budget constrained path between predefined starting and ending locations. The variant of the Orienteering Problem (OP) addressed here uses curvature-constrained Dubins vehicle model for planning the data collection missions for UAV. Moreover, in the DOPN, it is also assumed that the data, and thus the reward, may be collected from a close neighborhood sensing distance around the target locations, e.g., taking a snapshot by an onboard camera with a wide field of view, or using a sensor with a long range. We propose a novel approach based on the Variable Neighborhood Search (VNS) metaheuristic for the DOPN, in which combinatorial optimization of the sequence for visiting the target locations is simultaneously addressed with continuous optimization for finding Dubins vehicle waypoints inside the neighborhoods of the visited targets. The proposed VNS-based DOPN algorithm is evaluated in numerous benchmark instances, and the results show that it significantly outperforms the existing methods in both solution quality and computational time. The practical deployability of the proposed approach is experimentally verified in a data collection scenario with a real hexarotor UAV.

Information gathering planning with Hermite spline motion primitives for aerial vehicles with limited time of flight

  • DOI: 10.1007/978-3-030-14984-0_15
  • Odkaz: https://doi.org/10.1007/978-3-030-14984-0_15
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This paper focuses on motion planning for information gathering by Unmanned Aerial Vehicle~(UAV) solved as Orienteering Problem~(OP). The considered OP stands to find a path over subset of given target locations, each with associated reward, such that the collected reward is maximized within a limited time of flight. To fully utilize the motion range of the UAV, Hermite splines motion primitives are used to generate smooth trajectories. The minimal time of flight estimate for a given Hermite spline is calculated using known motion model of the UAV with limited maximum velocity and acceleration. The proposed Orienteering Problem with Hermite splines is introduced as Hermite Orienteering Problem~(HOP) and its solution is based on Random Variable Neighborhood Search algorithm~(RVNS). The proposed RVNS for HOP combines random combinatorial state space exploration and local continuous optimization for maximizing the collected reward. This approach was compared with state of the art solutions to the OP motivated by UAV applications and showed to be superior as the resulting trajectories reached better final rewards in all testing cases. The proposed method has been also successfully verified on a real UAV in information gathering task.

Onboard Marker-Less Detection and Localization of Non-Cooperating Drones for Their Safe Interception by an Autonomous Aerial System

  • DOI: 10.1109/LRA.2019.2927130
  • Odkaz: https://doi.org/10.1109/LRA.2019.2927130
  • Pracoviště: Multirobotické systémy
  • Anotace:
    In this letter, a novel approach to fast three-dimensional (3-D) localization of flying objects for their interception by a micro aerial vehicle (MAV) is presented. The proposed method utilizes a depth image from a stereo camera to facilitate onboard detection of drones, flying in its proximity. The method does not rely on using any kind of markers, which enables localization of non-cooperating drones. This approach strongly relaxes the requirements on the drones to be detected, and the detection algorithm is computationally undemanding enough to process images online, onboard an MAV with limited computational resources. This allows using the detection system in the control feedback of an autonomous aerial intercepting system. Output of the detection algorithm is filtered by a 3-D multi-target tracking algorithm to reduce false positives, preserve temporal consistency of the detections, and to predict positions of the drones (e.g., to compensate camera and processing delays). We demonstrate the importance of the advances in flying object localization, presented in this letter, in an experiment with an intruder-interceptor scenario, which would be unfeasible using state-of-the-art detection and localization methods.

Physical Orienteering Problem for Unmanned Aerial Vehicle Data Collection Planning in Environments with Obstacles

  • DOI: 10.1109/LRA.2019.2923949
  • Odkaz: https://doi.org/10.1109/LRA.2019.2923949
  • Pracoviště: Centrum umělé inteligence, Multirobotické systémy
  • Anotace:
    This paper concerns a variant of the Orienteering Problem (OP) that arises from multi-goal data collection scenarios where a robot with a limited travel budget is requested to visit given target locations in an environment with obstacles. We call the introduced OP variant the Physical Orienteering Problem (POP). The POP sets out to determine a feasible, collision-free, path that maximizes collected reward from a subset of the target locations and does not exceed the given travel budget. The problem combines motion planning and combinatorial optimization to visit multiple target locations. The proposed solution to the POP is based on the Variable Neighborhood Search (VNS) method combined with the asymptotically optimal sampling-based Probabilistic Roadmap (PRM*) method. The VNS-PRM* uses initial low-dense roadmap that is continuously expanded during the VNS-based POP optimization to shorten paths of the promising solutions, and thus allows maximizing the sum of the collected rewards. The computational results support the feasibility of the proposed approach by a fast determination of high-quality solutions. Moreover, an experimental verification demonstrates the applicability of the proposed VNS-PRM* approach for data collection planning for an unmanned aerial vehicle in an urban-like environment with obstacles.

Position and attitude control of multi-rotor aerial vehicles: A survey

  • DOI: 10.1016/j.arcontrol.2019.08.004
  • Odkaz: https://doi.org/10.1016/j.arcontrol.2019.08.004
  • Pracoviště: Multirobotické systémy
  • Anotace:
    Motion control theory applied to multi-rotor aerial vehicles (MAVs) has gained attention with the recent increase in the processing power of computers, which are now able to perform the calculations needed for this technique, and with lower cost of sensors and actuators. Control algorithms of this kind are applied to the position and the attitude of MAVs. In this paper, we present a review of recent developments in position control and attitude control of multi-rotor aerial robots systems. We also point out the growth of related research, starting with the boom in multi-rotor unmanned aerial robotics that began after 2010, and we discuss reported field applications and future challenges of the control problem described here. The objective of this survey is to provide a unified and accessible presentation, placing the classical model of a multi-rotor aerial vehicle and the proposed control approaches into a proper context, and to form a starting point for researchers who are initiating their endeavors in linear/nonlinear position, altitude or attitude control applied to MAVs. Finally, the contribution of this work is an attempt to present a comprehensive review of recent breakthroughs in the field, providing links to the most interesting and most successful works from the state-of-the-art.

Real-Time Localization of Transmission Sources by a Formation of Helicopters Equipped with a Rotating Directional Antenna

  • DOI: 10.1007/978-3-030-14984-0_25
  • Odkaz: https://doi.org/10.1007/978-3-030-14984-0_25
  • Pracoviště: Katedra telekomunikační techniky, Multirobotické systémy
  • Anotace:
    This paper proposes a novel technique for radio frequency transmission sources (RFTS) localization in outdoor environments using a formation of autonomous Micro Aerial Vehicles (MAVs) equipped with a rotating directional antenna. The technique uses a fusion of received signal strength indication (RSSI) and angle of arrival (AoA) data gained from dependencies of RSSI on angle measured by each direc- tional antenna. An Unscented Kalman Filter (UKF) based approach is used for sensor data fusion and for estimation of RFTS positions during each localization step. The proposed method has been verified in simula- tions using noisy and inaccurate measurements and in several successful real-world outdoor deployments.

Real-time Localization of Transmission Sources Using a Formation of Micro Aerial Vehicles

  • DOI: 10.1109/RCAR47638.2019.9043942
  • Odkaz: https://doi.org/10.1109/RCAR47638.2019.9043942
  • Pracoviště: Fakulta elektrotechnická, Multirobotické systémy
  • Anotace:
    Techniques designed for using teams of small and therefore safe Micro Aerial Vehicles (MAVs) for realization of antenna-like sensing devices with flexible shape, which may be adaptively changed based on the currently sensed properties of the scanned medium, are presented in this paper. This approach enables using simple and cheap sensors carried onboard of MAVs and cooperatively deploying them in the environment to optimize overall sensitivity of the compound sensory array. Two case studies of a deployment of MAV teams in the application of real-time radio transmitters localization are discussed as an example. The first method relies on Kalman Filter-based fusion of distributed RSSI measurements used to estimate distances between the transmitter and multiple receivers, which results in precise, robust and fast transmitters location in the environment. The second method utilizes onboard rotating directional antennas to estimate angles between transmitters and receivers at different positions and a Weighted Robust Least Squares method to determine transmitters locations. Both algorithms were verified in a realistic simulation system under Gazebo simulator in ROS and in real experiments with a fleet of MAVs, with a focus on aspects of real-time information fusion and coordination of MAVs sharing the same workspace with small mutual distances.

Real-time model-free minimum-seeking autotuning method for unmanned aerial vehicle controllers based on fibonacci-search algorithm

  • DOI: 10.3390/s19020312
  • Odkaz: https://doi.org/10.3390/s19020312
  • Pracoviště: Multirobotické systémy
  • Anotace:
    The paper presents a novel autotuning approach for finding locally-best parameters of controllers on board of unmanned aerial vehicles (UAVs). The controller tuning is performed fully autonomously during flight on the basis of predefined ranges of controller parameters. Required controller properties may be simply interpreted by a cost function, which is involved in the optimization process. For example, the sum of absolute values of the tracking error samples or performance indices, including weighed functions of control signal samples, can be penalized to achieve very precise position control, if required. The proposed method relies on an optimization procedure using Fibonacci-search technique fitted into bootstrap sequences, enabling one to obtain a global minimizer for a unimodal cost function. The approach is characterized by low computational complexity and does not require any UAV dynamics model (just periodical measurements from basic onboard sensors) to obtain proper tuning of a controller. In addition to the theoretical background of the method, an experimental verification in real-world outdoor conditions is provided. The experiments have demonstrated a high robustness of the method to in-environment disturbances, such as wind, and its easy deployability.

Route planning for teams of unmanned aerial vehicles using Dubins vehicle model with budget constraint

  • DOI: 10.1007/978-3-030-14984-0_27
  • Odkaz: https://doi.org/10.1007/978-3-030-14984-0_27
  • Pracoviště: Multirobotické systémy
  • Anotace:
    In this paper, we propose Greedy Randomized Adaptive Search Procedure~(GRASP) with Path Relinking extension for a solution of a novel problem formulation, the Dubins Team Orienteering Problem with Neighborhoods~(DTOPN). The DTOPN is a variant of the Orienteering Problem~(OP). The goal is to maximize collected reward from a close vicinity of given target locations, each with predefined reward, using multiple curvature-constrained vehicles, such as fixed-wing aircraft or VTOL UAVs with constant forward speed, each limited by route length. This makes it a very useful routing problem for scenarios using multiple UAVs for data collection, mapping, surveillance, and reconnaissance. The proposed method is verified on existing benchmark instances and by real experiments with a group of three fully-autonomous hexarotor UAVs that were used to compare the DTOPN with similar problem formulations and show the benefit of the introduced DTOPN.

Timepix Radiation Detector for Autonomous Radiation Localization and Mapping by Micro Unmanned Vehicles

  • DOI: 10.1109/IROS40897.2019.8968514
  • Odkaz: https://doi.org/10.1109/IROS40897.2019.8968514
  • Pracoviště: Multirobotické systémy
  • Anotace:
    A system for measuring radiation intensity and for radiation mapping by a micro unmanned robot using the Timepix detector is presented in this paper. Timepix detectors are extremely small, but powerful 14x14 mm, 256x256 px CMOS hybrid pixel detectors, capable of measuring ionizing alpha, beta, gamma radiation, and heaving ions. The detectors, developed at CERN, produce an image free of any digital noise thanks to per-pixel calibration and signal digitization. Traces of individual ionizing particles passing through the sensors can be resolved in the detector images. Particle type and energy estimates can be extracted automatically using machine learning algorithms. This opens unique possibilities in the task of flexible radiation detection by very small unmanned robotic platforms. The detectors are well suited for the use of mobile robots thanks to their small size, lightweight, and minimal power consumption. This sensor is especially appealing for micro aerial vehicles due to their high maneuverability, which can increase the range and resolution of such novel sensory system. We present a ROS-based readout software and real-time image processing pipeline and review options for 3-D localization of radiation sources using pixel detectors. The provided software supports off-the-shelf FITPix, USB Lite readout electronics with Timepix detectors.

Unsupervised learning‐based flexible framework for surveillance planning with aerial vehicles

  • DOI: 10.1002/rob.21823
  • Odkaz: https://doi.org/10.1002/rob.21823
  • Pracoviště: Centrum umělé inteligence, Multirobotické systémy
  • Anotace:
    The herein studied problem is motivated by practical needs of our participation in the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 in which a team of unmanned aerial vehicles (UAVs) is requested to collect objects in the given area as quickly as possible and score according to the rewards associated with the objects. The mission time is limited, and the most time‐consuming operation is the collection of the objects themselves. Therefore, we address the problem to quickly identify the most valuable objects as surveillance planning with curvature‐constrained trajectories. The problem is formulated as a multivehicle variant of the Dubins traveling salesman problem with neighborhoods (DTSPN). Based on the evaluation of existing approaches to the DTSPN, we propose to use unsupervised learning to find satisfiable solutions with low computational requirements. Moreover, the flexibility of unsupervised learning allows considering trajectory parametrization that better fits the motion constraints of the utilized hexacopters that are not limited by the minimal turning radius as the Dubins vehicle. We propose to use Bézier curves to exploit the maximal vehicle velocity and acceleration limits. Besides, we further generalize the proposed approach to 3D surveillance planning. We report on evaluation results of the developed algorithms and experimental verification of the planned trajectories using the real UAVs utilized in our participation in MBZIRC 2017.

UVDAR System for Visual Relative Localization with application to Leader-Follower Formations of Multirotor UAVs

  • DOI: 10.1109/LRA.2019.2901683
  • Odkaz: https://doi.org/10.1109/LRA.2019.2901683
  • Pracoviště: Multirobotické systémy
  • Anotace:
    A novel onboard relative localization method, based on ultraviolet light, used for real-time control of a leader-follower formation of multirotor UAVs is presented in this paper. A new smart sensor, UVDAR, is employed in an innovative way, which does not require communication and is extremely reliable in real-world conditions. This innovative sensing system exploits UV spectrum and provides relative position and yaw measurements independently of environment conditions such as changing illumination and presence of undesirable light sources and their reflections. The proposed approach exploits this retrieved information to steer the follower to a given 3D position and orientation relative to the leader, which may be considered as the main building block of any multi-UAV system operating with small mutual distances among team-members. The proposed solution was verified in demanding outdoor conditions, validating usage of UVDAR in real flight scenario and paving the way for further usage of UVDAR for practical multi-UAV formation deployments.

Variable Neighborhood Search for the Set Orienteering Problem and its application to other Orienteering Problem variants

  • DOI: 10.1016/j.ejor.2019.01.047
  • Odkaz: https://doi.org/10.1016/j.ejor.2019.01.047
  • Pracoviště: Centrum umělé inteligence, Multirobotické systémy
  • Anotace:
    This paper addresses the recently proposed generalization of the Orienteering Problem (OP), referred to as the Set Orienteering Problem (SOP). The OP stands to find a tour over a subset of customers, each with an associated profit, such that the profit collected from the visited customers is maximized and the tour length is within the given limited budget. In the SOP, the customers are grouped in clusters, and the profit associated with each cluster is collected by visiting at least one of the customers in the respective cluster. Similarly to the OP, the SOP limits the tour cost by a given budget constraint, and therefore, only a subset of clusters can usually be served. We propose to employ the Variable Neighborhood Search (VNS) metaheuristic for solving the SOP. In addition, a novel Integer Linear Programming (ILP) formulation of the SOP is proposed to find the optimal solution for small and medium-sized problems. Furthermore, we show other OP variants that can be addressed as the SOP, i.e., the Orienteering Problem with Neighborhoods (OPN) and the Dubins Orienteering Problem (DOP). While the OPN extends the OP by collecting a profit within the neighborhood radius of each customer, the DOP uses airplane-like smooth trajectories to connect individual customers. The presented computational results indicate the feasibility of the proposed VNS algorithm and ILP formulation, by improving the solutions of several existing SOP benchmark instances and requiring significantly lower computational time than the existing approaches.

Vision techniques for on-board detection, following and mapping of moving targets

  • DOI: 10.1002/rob.21850
  • Odkaz: https://doi.org/10.1002/rob.21850
  • Pracoviště: Katedra kybernetiky, Centrum umělé inteligence, Multirobotické systémy
  • Anotace:
    This article presents computer vision modules of a multi-unmanned aerial vehicle (UAV) system, which scored gold, silver, and bronze medals at the Mohamed bin Zayed International Robotics Challenge (MBZIRC) 2017. This autonomous system, which was running completely on-board and in real-time, had to address two complex tasks in challenging outdoor conditions. In the first task, an autonomous UAV had to find, track, and land on a human-driven car moving at $15$~$km/h$ on a figure-eight-shaped track. During the second task, a group of three UAVs had to find small colored objects in a wide area, pick them up, and deliver them into a specified drop-off zone. The computer vision modules presented here achieved computationally efficient detection, accurate localization, robust velocity estimation, and reliable future position prediction of both the colored objects and the car. These properties had to be achieved in adverse outdoor environments with changing light conditions. Lighting varied from intense direct sunlight with sharp shadows cast over the objects by the UAV itself, to reduced visibility caused by overcast to dust and sand in the air. The results presented in this paper demonstrate good performance of the modules both during testing, which took place in the harsh desert environment of the central area of United Arab Emirates, as well as during the contest, which took place at a racing complex in the urban, near-sea location of Abu Dhabi. The stability and reliability of these modules contributed to the overall result of the contest, where our multi-UAV system outperformed teams from world-leading robotic laboratories in two challenging scenarios.

Fast Mutual Relative Localization of UAVs using Ultraviolet LED Markers

  • DOI: 10.1109/ICUAS.2018.8453331
  • Odkaz: https://doi.org/10.1109/ICUAS.2018.8453331
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This paper proposes a new methodology for outdoor mutual relative localization of UAVs equipped with active ultraviolet markers and a suitable camera with specialized bandpass filters. Mutual relative localization is a crucial tool for formation preservation, swarming and cooperative task completion in scenarios in which UAVs share working space in small relative distances. In most current systems of compact UAV swarms the localization of particular UAVs is based on the data obtained from motion capture systems for indoor experiments or on precise RTK-GNSS data outdoor. Such an external infrastructure is unavailable in most of real multi-UAV applications and often cannot be pre-installed. To account for such situations, as well as to make the system more autonomous, reliance on onboard sensors only is desirable. In the proposed approach, we rely on ultraviolet LED markers, that emit light in frequencies that are less common in nature than the visible light or infrared radiation, especially in high intensities. Additionally, common camera sensors are sensitive to ultraviolet light, making the addition of a filter the only necessary modification, keeping the platform low-cost, which is one of the key requirements on swarm systems. This also allows for a smaller size of the markers to be sufficient, without burdening the processing resources. Thus the proposed system aspires to be an enabling technology for deployment of large swarms of possibly micro-scale aerial vehicles in real-world conditions and without any dependency on an external infrastructure.

Increasing Diversity of Solutions in Sampling-based Path Planning

  • DOI: 10.1145/3297097.3297114
  • Odkaz: https://doi.org/10.1145/3297097.3297114
  • Pracoviště: Multirobotické systémy
  • Anotace:
    Sampling-based path planning algorithms like Rapidly-exploring Random Trees (RRT) are widely used to solve path planning problems for robots with many Degrees Of Freedom (DOF). Although the sampling-based planners are stochastic and provide different solutions in every trial, many resulting trajectories can be quite similar, e.g., because they are close to each other. Computation of di verse paths, i.e., paths leading through different parts of the configuration space, is important for online navigation of mobile robots or for finding solutions leading through different narrow passages of the configuration space. In this paper, we propose an extension of the RRT algorithm to find diverse paths in the configuration space iteratively. The idea of the proposed method is to prohibit the exploration of selected regions of the configuration space. The prohibited regions are defined using the waypoints of paths computed in the previous iterations. We show that the proposed method finds more diverse trajectories than can be achieved by repeated computations of a single sampling-based planner.

Localization, Grasping, and Transportation of Magnetic Objects by a team of MAVs in Challenging Desert like Environments

  • DOI: 10.1109/LRA.2018.2800121
  • Odkaz: https://doi.org/10.1109/LRA.2018.2800121
  • Pracoviště: Centrum umělé inteligence, Multirobotické systémy
  • Anotace:
    Autonomous Micro Aerial Vehicles have the potential to assist in real life tasks involving grasping and transportation, but not before solving several difficult research challenges. In this work, we address the design, control, estimation, and planning problems for cooperative localization, grasping, and transportation of objects in challenging outdoor scenarios. We demonstrate an autonomous team of MAVs able to plan safe trajectories for manipulation of ferrous objects, while guaranteeing inter-robot collision avoidance and automatically creating a map of the objects in the environment. Our solution is predominantly distributed, allowing the team to pick and transport ferrous disks to a final destination without collisions. This result is achieved using a new magnetic gripper with a novel feedback approach, enabling the detection of successful grasping. The gripper design and all the components to build a platform are clearly provided as open-source hardware for reuse by the community. Finally, the proposed solution is validated through experimental results where difficulties include inconsistent wind, uneven terrain, and sandy conditions.

Model Predictive Trajectory Tracking and Collision Avoidance for Reliable Outdoor Deployment of Unmanned Aerial Vehicles

  • Pracoviště: Multirobotické systémy
  • Anotace:
    We propose a novel approach for optimal trajectory tracking for unmanned aerial vehicles (UAV), using a linear model predictive controller (MPC) in combination with non-linear state feedback. The solution relies on fast onboard simulation of the translational dynamics of the UAV, which is guided by a linear MPC. By sampling the states of the virtual UAV, we create a control command for fast non-linear feedback, which is capable of performing agile maneuvers with high precision. In addition, the proposed pipeline provides an interface for a decentralized collision avoidance system for multi-UAV scenarios. Our solution makes use of the long prediction horizon of the linear MPC and allows safe outdoors execution of multi-UAV experiments without the need for in-advance collision-free planning. The practicality of the tracking mechanism is shown in combination with priority-based collision resolution strategy, which performs sufficiently in experiments with up to 5 UAVs. We present a statistical and experimental evaluation of the platform in both simulation and real-world examples, demonstrating the usability of the approach.

Mutual Localization of UAVs based on Blinking Ultraviolet Markers and 3D Time-Position Hough Transform

  • DOI: 10.1109/COASE.2018.8560384
  • Odkaz: https://doi.org/10.1109/COASE.2018.8560384
  • Pracoviště: Multirobotické systémy
  • Anotace:
    A novel vision-based approach for indoor/outdoor mutual localization on Unmanned Aerial Vehicles (UAVs) with low computational requirements and without external infrastructure is proposed in this paper. The proposed solution exploits the low natural emissions in the near-Ultra-Violet (UV) spectrum to avoid major drawbacks of the visible spectrum. Such approach provides much better reliability while being less computationally intensive. Working in near-UV requires active markers, which can be leveraged by enriching the information content through blinking patterns encoded marker-ID. In order to track the markers motion and identify their blinking frequency, we propose an innovative use of three dimensional Hough Transform, applied to stored position-time points. The proposed method was intensively tested onboard multi-UAV systems in real-world scenarios that are very challenging for visible-spectrum methods.The results of our methods in terms of robustness, reliability and precision, as well as the low requirement on the system deployment, predestine this method to be an enabling technology for using swarms of UAVs.

Self-Localization of Unmanned Aerial Vehicles Based on Optical Flow in Inboard Camera Image

  • Autoři: Ing. Viktor Walter, Novák, T., doc. Ing. Martin Saska, Dr. rer. nat.,
  • Publikace: Modelling and Simulation for Autonomous Systems (MESAS 2017). Cham: Springer International Publishing AG, 2018. p. 106-132. Lecture Notes in Computer Vision. vol. 10756. ISSN 0302-9743. ISBN 978-3-319-76071-1.
  • Rok: 2018
  • DOI: 10.1007/978-3-319-76072-8_8
  • Odkaz: https://doi.org/10.1007/978-3-319-76072-8_8
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This paper proposes and evaluates the implementation of a self-localization system intended for use in Unmanned Aerial Vehicles. Accurate localization is necessary for UAVs for efficient stabilization, navigation and collision avoidance. Conventionally, this requirement is fulfilled using external hardware infrastructure, such as Global Navigation Satellite System or visual motion-capture system. These approaches are, however, not applicable in environments where deployment of cumbersome motion capture equipment is not feasible, as well as in GNSS-denied environments. Systems based on Simultaneous Localization and Mapping (SLAM) require heavy and expensive onboard equipment and high amounts of data transmissions for sharing maps between UAVs. Availability of a system without these drawbacks is crucial for deployment of tight formations of multiple fully autonomous micro UAVs for both outdoor and indoor missions. The project was inspired by the often used sensor PX4FLOW Smart Camera. The aim was to develop a similar sensor, without the drawbacks observed in its use, as well as to make the operation of it more transparent and to make it independent of a specific hardware. Our proposed solution requires only a lightweight camera and a single-point range sensor. It is based on optical flow estimation from consecutive images obtained from downward-facing camera, coupled with a specialized RANSAC-inspired post-processing method that takes into account flight dynamics. This filtering makes it more robust against imperfect lighting, homogenous ground patches, random close objects and spurious errors. These features make this approach suitable even for coordinated flights through demanding forest-like environment. The system is designed mainly for horizontal velocity estimation, but specialized modifications were also made for vertical speed and yaw rotation rate estimation. These methods were tested in a simulator and subsequently in real-world conditions.

Autonomous Landing On A Moving Car With Unmanned Aerial Vehicle

  • DOI: 10.1109/ECMR.2017.8098700
  • Odkaz: https://doi.org/10.1109/ECMR.2017.8098700
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This paper presents an implementation of a system that is autonomously able to find, follow and land on a car moving at \jed{15}{km/h}. Our solution consists of two parts, the image processing for fast onboard detection of landing platform and the MPC tracker for trajectory planning and control. This approach is fully autonomous using only the onboard computer and onboard sensors with differential GPS. Besides the description of the solution, we also present experimental results obtained at MBZIRC 2017 international competition.

Coherent swarming of unmanned micro aerial vehicles with minimum computational and communication requirements

  • DOI: 10.1109/ECMR.2017.8098702
  • Odkaz: https://doi.org/10.1109/ECMR.2017.8098702
  • Pracoviště: Multirobotické systémy
  • Anotace:
    An algorithm designed for stabilization and control of large groups of micro aerial vehicles (MAVs) - multirotor helicopters - without any explicit communication is proposed in this paper. The presented algorithm enables a swarm of MAVs to maintain its coherence and perform a compact motion in complex environments while avoiding obstacles with only very limited computational and sensory requirements. The method is very robust to incomplete sensory information, it enables a fully distributed applicability, and it is highly scalable. Increasing amount of MAVs even improves the required coherence behaviour. Numerous simulations in different environments were conducted to verify the algorithm, show its potential, and explore its various configurations.

Documentation of Dark Areas of Large Historical Buildings by a Formation of Unmanned Aerial Vehicles using Model Predictive Control

  • Pracoviště: Multirobotické systémy
  • Anotace:
    A system designed for a unique multi-robot application of closely flying formations of Unmanned Aerial Vehicles (UAVs) in indoor areas is described in this paper. The proposed solution is aimed as a tool for historians and restorers working in large historical buildings such as churches to provide an access to areas that are difficult to reach by humans. In these objects, it is impossible to keep a large scaffolding for a long time due to regular services, which is necessary for studying a long-term influence of restorations works, and some parts of the churches were even not reached by people for decades and need to be inspected. To provide the same documentation and inspection techniques that are used by the experts in lower easily accessible parts of the buildings, we employ a formation of autonomous UAVs, where one of the robots is equipped by a visual sensor and the others by source of light, which provides the required flexibility for control of lightening. The described system in its full complexity has been implemented with achieved robustness and reliability required by deployment in real missions. The technology demonstration has been provided with real UAVs in historical objects to help restorers and conservationists with achieved valuable results used in plans of restoration works. In these missions, UAVs were autonomously hovering at designated locations to be able to demonstrate usefulness of such robotic lightening approach.

Documentation of large historical buildings by UAV formations - scene perception-driven motion planning and predictive control

  • Pracoviště: Multirobotické systémy
  • Anotace:
    A model predictive control and motion planning algorithm designed for autonomous documentation of large historical buildings by a formation of unmanned Aerial Vehicles (UAVs) is proposed in this paper. In the proposed approach, a self-stabilized formation of multi-rotor helicopters is employed for filming in dark conditions, where one of the UAVs carries the camera and the neighboring UAVs a source of light. This setup is inspired by two techniques often used by historians and restorers. The first one so-called Three point lighting approach [1,4], is a filming technique in which 1-3 sources of light are used in different locations relatively to the camera optical axis. The method enables to create the illusion of a three-dimensional subject in a two-dimensional image and to illuminate the subject being shot (such as sculptures in historical buildings) while controlling the shading and shadows produced by lighting. This is essential for the presentation of historical monuments in interiors to the broad public, as it removes the boring flatness from images and videos, and it adds value to the analysis of gathered results by historians. The second technique frequently used by restorers employs a strong side light for illumination of flat objects, such as walls with parget and mosaics. In this method, the strong light needs to be placed as close as possible to the scanned plain, which makes shadows in the image in a case of a roughness of the surface. Restorers and conservationists can detect from such illuminated pictures if a tile in the mosaic is not fixed properly or if a painting is affected by a humidity indicated by buckling of the wall surface.

Dubins Orienteering Problem

  • DOI: 10.1109/LRA.2017.2666261
  • Odkaz: https://doi.org/10.1109/LRA.2017.2666261
  • Pracoviště: Katedra kybernetiky, Centrum umělé inteligence
  • Anotace:
    In this paper, we address the Orienteering Problem (OP) for curvature constrained vehicle. For a given set of target locations, each with associated reward, the OP stands to find a tour from a prescribed starting location to a given ending location such that it maximizes collected rewards while the tour length is within a given travel budget constraint. The addressed generalization of the Euclidean OP is called the Dubins Orienteering Problem (DOP) in which the reward collecting tour has to satisfy the limited turning radius of the Dubins vehicle. The DOP consists not only of selecting the most valuable targets and determination of the optimal sequence to visit them, but it also involves the determination of the vehicle’s heading angle at each target location. The proposed solution is based on the Variable neighborhood search technique, and its feasibility is supported by an empirical evaluation in existing OP benchmarks. Moreover, an experimental verification in a real practical scenario further demonstrates the necessity of the proposed direct solution of the Dubins Orienteering Problem.

Dubins Orienteering Problem with Neighborhoods

  • DOI: 10.1109/ICUAS.2017.7991350
  • Odkaz: https://doi.org/10.1109/ICUAS.2017.7991350
  • Pracoviště: Centrum umělé inteligence, Multirobotické systémy
  • Anotace:
    In this paper, we address the Dubins Orienteering Problem with Neighborhoods (DOPN) a novel problem derived from the regular Orienteering Problem (OP). In the OP, one tries to find a maximal reward collecting path through a subset of given target locations, each with associated reward, such that the resulting path length does not exceed the specified travel budget. The Dubins Orienteering Problem (DOP) requires the reward collecting path to satisfy the curvature-constrained model of the Dubins vehicle while reaching precise positions of the target locations. In the newly introduced DOPN, the resulting path also respects the curvature constrained Dubins vehicle as in the DOP; however, the reward can be collected within a close distant neighborhood of the target locations. The studied problem is inspired by data collection scenarios for an Unmanned Aerial Vehicle (UAV), that can be modeled as the Dubins vehicle. Furthermore, the DOPN is a useful problem formulation of data collection scenarios for a UAV with the limited travel budget due to battery discharge and in scenarios where the sensoric data can be collected from a proximity of each target location. The proposed solution of the DOPN is based on the Variable Neighborhood Search method, and the presented computational results in the OP benchmarks support feasibility of the proposed approach.

Motion Planning with Motion Primitives for Industrial Bin Picking

  • DOI: 10.1109/ETFA.2017.8247759
  • Odkaz: https://doi.org/10.1109/ETFA.2017.8247759
  • Pracoviště: Multirobotické systémy
  • Anotace:
    In the bin picking problem, the task is to automatically unload objects from a container using a robotic manipulator. The task is often approached by organizing the objects into a predictable pattern, e.g., a workpiece carrier, in order to simplify all integral subtasks like object recognition, motion planning and grasping. In such a case, motion planning can even be solved offline as it is ensured that the objects are always at the same positions at known times. However, there is a growing demand for non-structured bin picking, where the objects can be placed randomly in the bins. This arises from recent trends of transforming classical factories into smart production facilities allowing small lot sizes at the efficiency of mass production. The demand for fast and highly flexible handling and manipulation abilities of industrial robots requires to solve all the bin picking methods, including motion planning, online. In this paper, we propose a novel technique for fast sampling-based motion planning of robotic manipulators using motion primitives. Motion primitives are short trajectories that boost search of the configuration space and consequently speed up the planning phase. The proposed work has been verified in a simulation and on a prototype of a bin picking system.

On solution of the Dubins touring problem

  • DOI: 10.1109/ECMR.2017.8098685
  • Odkaz: https://doi.org/10.1109/ECMR.2017.8098685
  • Pracoviště: Centrum umělé inteligence, Multirobotické systémy
  • Anotace:
    The Dubins traveling salesman problem (DTSP) combines the combinatorial optimization of the optimal sequence of waypoints to visit the required target locations with the continuous optimization to determine the optimal headings at the waypoints. Existing decoupled approaches to the DTSP are based on an independent solution of the sequencing part as the Euclidean TSP and finding the optimal headings of the waypoints in the sequence. In this work, we focus on the determination of the optimal headings in a given sequence of waypoints and formulate the problem as the Dubins touring problem (DTP). The DTP can be solved by a uniform sampling of possible headings; however, we propose a new informed sampling strategy to find approximate solution of the DTP. Based on the presented results, the proposed algorithm quickly converges to a high-quality solution, which is less than 0.1% from the optimum. Besides, the proposed approach also improves the solution of the DTSP, and its feasibility has been experimentally verified in a real practical deployment.

Reactive Dubins Traveling Salesman Problem for Replanning of Information Gathering by UAVs

  • DOI: 10.1109/ECMR.2017.8098704
  • Odkaz: https://doi.org/10.1109/ECMR.2017.8098704
  • Pracoviště: Multirobotické systémy
  • Anotace:
    We introduce a novel online replanning method for robotic information gathering by Unmanned Aerial Vehicles~(UAVs) called Reactive Dubins Traveling Salesman Problem~(RDTSP). The considered task is the following: a set of target locations are to be visited by the robot. From an initial information gathering plan, obtained as an offline solution of either the Dubins Traveling Salesman Problem~(DTSP) or the Coverage Path Planning~(CPP), the proposed RDTSP ensures robust information gathering in each given target location by replanning over possible missed target locations. Furthermore, a simple decision making is a part of the proposed RDTSP to determine which target locations are marked as missed and also to control the appropriate time instant at which the repair plan is inserted into the initial path. The proposed method for replanning is based on the Variable Neighborhood Search metaheuristic which ensures visiting of all possibly missed target locations by minimizing the length of the repair plan and by utilizing the preplanned offline solution of the particular information gathering task. The novel method is evaluated in a realistic outdoor robotic information gathering experiment with UAV for both the Dubins Traveling Salesman Problem and the Coverage Path Planning scenarios.

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

  • DOI: 10.1007/s10514-016-9567-z
  • Odkaz: https://doi.org/10.1007/s10514-016-9567-z
  • Pracoviště: Katedra kybernetiky, Centrum umělé inteligence
  • Anotace:
    A complex system for control of swarms of micro aerial vehicles (MAV), in literature also called as unmanned aerial vehicles (UAV) or unmanned aerial systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenar- ios. The proposed visual-based stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAVswarmstabilizationanddeploymentinsurveillancesce- narios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).

Vision-based high-speed autonomous landing and cooperative objects grasping - towards the MBZIRC competition

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    The aim of this paper is to present a system being de- veloped for the Mohamed Bin Zayed International Robotics Challenge 2017 (MBZIRC) by a team of Czech Technical University in Prague, University of Pennsylvania and Univer- sity of Lincoln. The system designed for autonomous landing on a moving vehicle and autonomous collecting of color objects by a team of unmanned aerial vehicles - helicopters (UAVs) will be described with latest results achieved in real- scale outdoor scenarios. Both of these challenges require flying in high speed and strongly rely on vision control feedback and therefore the proposed system and designed approaches of autonomous flying with visual servoing should be within interest of participants of the Vision-based High Speed Autonomous Navigation of UAVs workshop. In our workshop presentation, we would like to introduce descrip- tion and results of computer vision approaches composed from a set of state-of-the-art techniques in a unique way to recognize reliably the landing pattern on the moving vehi- cle and color objects randomly distributed in a 100x100m workspace.

Complex manoeuvres of heterogeneous MAV-UGV formations using a model predictive control

  • DOI: 10.1109/MMAR.2016.7575274
  • Odkaz: https://doi.org/10.1109/MMAR.2016.7575274
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A problem of motion planning and coordination of compact formations of ground and aerial robots will be tackled in this paper. The scenarios when the formation composed from Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs), in particular Micro Aerial Vehicles (MAVs), has to reverse the direction of movement to fulfil task of collision free motion to a target zone will be solved. The presented motion planning and stabilization approach provides an effective technique to enable deployment of closely cooperating teams of robots in outdoor as well as indoor environment. The formation to target region problem is solved using a Model Predictive Control (MPC) methodology and the formation driving concept is based on a virtual-leader-follower approach. The mentioned MPC based process is used for trajectory planning and control of a virtual leader and also for control and stabilization of followers (MAVs and UGVs). The proposed approach is verified with numerous simulations and hardware experiments.

Embedded Model Predictive Control of Unmanned Micro Aerial Vehicles

  • DOI: 10.1109/MMAR.2016.7575273
  • Odkaz: https://doi.org/10.1109/MMAR.2016.7575273
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    We propose a lightweight embedded system for stabilization and control of Unmanned Aerial Vehicles (UAVs) and particularly Micro Aerial Vehicles (MAVs). The system relies solely on onboard sensors to localize the MAV, which makes it suitable for experiments in GPS-denied environments. The system utilizes predictive controllers to find optimal control actions for the aircraft using only onboard computational resources. To show the practicality of the proposed system, we present several indoor and outdoor experiments with multiple quadrotor helicopters which demonstrate its capability of trajectory tracking and disturbance rejection.

Exploration and Mapping Technique Suited for Visual-features Based Localization of MAVs

  • DOI: 10.1007/s10846-016-0358-8
  • Odkaz: https://doi.org/10.1007/s10846-016-0358-8
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    An approach for long term localization, stabilization, and navigation of micro-aerial vehicles (MAVs) in unknown environment is presented in this paper. The proposed method relies strictly on onboard sensors of employed MAVs and does not require any external positioning system. The core of the method consists in extraction of information from pictures consequently captured using a camera carried by the particular MAV. Visual features are obtained from images of the surface under the MAV, and stored into a map that is represented by these features. The position of the MAV is then obtained through matching with previously stored features. An important part of the proposed system is a novel approach for exploration and mapping of the workspace of robots. This method enables efficient exploring of the unknown environment, while keeping the iteratively built map of features consistent. The proposed algorithm is suitable for mapping of surfaces, both outdoor and indoor, with various density of the image features. The sufficient precision and long term persistence of the method allows its utilization for stabilization of large MAV groups that work in formations with small relative distances between particular vehicles. Numerous experiments with quadrotor helicopters and various numerical simulations have been realized for verification of the entire system and its components.

Formations of unmanned micro aerial vehicles led by migrating virtual leader

  • DOI: 10.1109/ICARCV.2016.7838801
  • Odkaz: https://doi.org/10.1109/ICARCV.2016.7838801
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A novel approach for control and motion planning of formations of multiple unmanned micro aerial vehicles (MAVs), also referred to as unmanned aerial vehicles (UAVs) - multirotor helicopters, in cluttered GPS-denied environments is presented in this paper. The proposed method enables autonomously to design complex maneuvers of a compact MAV team in a virtual-leaderfollower scheme. The feasibility of obtained results of the motion planning approach and the required stability of the formation is achieved by migrating the virtual leader along a hull surrounding the formation. This enables us to suddenly change formation motion in all directions, independently of actual orientation of the formation.

Fusion of Monocular Visual-Inertial Measurements for Three Dimensional Pose Estimation

  • Autoři: Perez-Paina, R, Paz, C, Kulich, M., doc. Ing. Martin Saska, Dr. rer. nat., Araguás, G.
  • Publikace: Modelling and Simulation for Autonomous Systems. Basel: Springer, 2016. p. 242-260. vol. 9991. ISSN 0302-9743. ISBN 978-3-319-47604-9.
  • Rok: 2016
  • DOI: 10.1007/978-3-319-47605-6_20
  • Odkaz: https://doi.org/10.1007/978-3-319-47605-6_20
  • Pracoviště: Multirobotické systémy
  • Anotace:
    This work describes a novel fusion schema to estimate the pose of a UAV using inertial sensors and a monocular camera. The visual motion algorithm is based on the plane induced homography using so called spectral features. The algorithm is able to operate with images presenting small amount of corner-like features, which gives more robustness to the state estimation. The key contribution of the paper is the use of this visual algorithm in a fusion schema with inertial sensors, exploiting the complementary properties of these two sensors. Results are presented in simulation with six degrees of freedom motion that satisfies dynamic constraints of a quadcopter. Virtual views are generated from this simulated motion cropped from a real floor image. Simulation results show that the presented algorithm would have enough precision to be used in an on-board algorithm to control the UAV in hovering operations.

Predictive control and stabilization of nonholonomic formations with integrated spline-path planning

  • DOI: 10.1016/j.robot.2015.09.004
  • Odkaz: https://doi.org/10.1016/j.robot.2015.09.004
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A path planning in the space of multinominals integrated into a model predictive control mechanism for driving formations of autonomous mobile robots is presented in this paper. The proposed approach is designed to stabilize the formations in desired shapes, and to navigate the group into a final position in a partly known environment with dynamic obstacles. In addition, the system provides inter-vehicle coordination and collision avoidance in the event of failure of a team member. The method is aimed at reaching the final position of the formation in the desired shape, but it enables to change temporarily this shape if it is enforced by the environment (in narrow corridors, on response to an impending collision with obstacles and faulty team members, etc.). This autonomous emergent behaviour increases the robustness of the system and its usability. It enables a proper compromise to be found between the formation driving requirement and the effort to fulfil the mission objective, i.e., to move the group from the current state into the required position. In this paper, the convergence of the method and the requirements for stability are shown on the basis of the results of the Lyapunov theorems of stability. These theoretical achievements imply constraints on the applicability of the system, which are verified in numerical simulations and in various tests with real autonomous robots. The performances of the entire system and of independent sub-systems in various formation driving scenarios are also shown in these tests.

Swarm Distribution and Deployment for Cooperative Surveillance by Micro-Aerial Vehicles

  • DOI: 10.1007/s10846-016-0338-z
  • Odkaz: https://doi.org/10.1007/s10846-016-0338-z
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    The task of cooperative surveillance of pre- selected Areas of Interest (AoI) in outdoor environ- ments by groups of closely cooperating Micro Aerial Vehicles (MAVs) is tackled in this paper. In the coop- erative surveillance mission, finding distributions of the MAVs in the environment to properly cover the AoIs and finding feasible trajectories to reach the obtained surveillance locations from the initial depot are crucial tasks that have to be fulfilled. In addition, motion constraints of the employed MAVs, environ- ment constraints (e.g. non-fly zones), and constraints imposed by localization of members of the groups need to be satisfied in the planning process. We for- mulate the task of cooperative surveillance as a single high-dimensional optimization problem to be able to integrate all these requirements. Due to numerous con- straints that have to be satisfied, we propose to solve the problem using an evolutionary-based optimiza- tion technique. An important aspect of the proposed method is that the cooperating MAVs are localized relatively to each other, rather than using a global localization system. This increases robustness of the system and its deploy-ability in scenarios, in which compact shapes of the MAV group with short relative distances are required.

Automatic learning of motion primitives for modular robots

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    Modular robots consist of many mechatronic modules that can be connected into various shapes and therefore adapted for a given task and environment. Motion of the robots can be achieved by locomotion generators that control joints connecting the modules. A robot can be equipped with several locomotion generators that provide basic motion primitives. A sequence of motion primitives is found using a motion planner in order to visit a given goal position. Each primitive needs to be prepared by an optimization process where parameters of a locomotion generator are tuned. Due to the possibility to create robots of various shapes, it is not easy to estimate in advance what kind of motions a robot can perform. Moreover, motion capabilities can also be influenced by failures of individual modules. Human operators may prefer motions like ‘crawl-forward’ or ‘move-left’ but such primitives might not be achievable by all robots. In this paper, we discuss how to automatically learn motion primitives that are suitable for a given robot and task. Experimental verification in simulation as well as with physical robots is presented.

Compact groups of micro aerials vehicles stabilized using onboard visual relative localization

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A system for stabilization of Micro Aerial Vehicles (MAVs), quad-rotor helicopters, flying in a compact group with an ability of fast response to changes detected in surrounding environment is presented in this paper. The core of the proposed method lies in utilization of onboard monocular cameras for visual relative localization of neighbouring MAVs. The localization cameras, which are carried by all MAVs of the group, provide an estimate of relative positions and orientations of neighbours in a limited range and viewing angle (see [1], [2] for details on the localization system). This setup allows us to gain information on close proximity of each MAV in the group in a similar way as it is done in swarms of animals in nature. The operational space of these onboard sensors is similar to sense organs of birds in flocks and fish in schools and also these sensors have similar properties. Both species gain quite precise information on relative position of the neighbors with fast update rate, but they have only a rough guess on motion prediction of the neighbors, similarly as it is provided by the onboard localization in our system.

Embedded Model Predictive Control of Micro Aerial Vehicles for Multi-robotic Swarms and Formations

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    We propose a lightweight and embedded system for stabilization and control of micro aerial vehicles (MAVs). The system relies solely on onboard sensors and computational power, which makes it suitable for experiments with multiple unmanned helicopters in GPS-denied environment. It allows the MAV to track desired trajectories in 3D space and to maintain groups of visually-localized MAVs flying in compact groups with small relative distances. The system utilizes predictive controllers to find optimal control actions for the aircraft

Experimental platform of self-stabilized micro aerial vehicles designed for research of flocking behaviour in nature

  • Autoři: doc. Ing. Martin Saska, Dr. rer. nat.,
  • Publikace: sas Proceedings of IEEE ICRA workshop: Robotics-inspired biology. Piscataway: IEEE Robotics and Automation Society, 2015, Available from: http://people.seas.harvard.edu/~gravish/ICRA2015/PDF/Saska_ICRA_2015.pdf
  • Rok: 2015
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    In this poster, we present a complex ready-to-fly platform of multiple self-stabilized fully autonomous Micro Aerial Vehicles (MAVs) designed for support of research of flocking behavioural patterns in nature and for inspiration in finding new biological hypothesis in this field. Flocks of birds, schools of fish, and swarms of insect can be usually only passively observed in nature and it is very difficult or impossible to change capabilities of all swarm particles simultaneously (for example their sensing and communication capabilities). Targeted manipulations of the artificial system (controlled swarm behaviour) can be an efficient tool for studying influence of such capabilities of individuals on arising group behaviour. Setting of communication bandwidth to several defined values allows investigations on correlations between the amount of information exchange and group size or between the amount of information exchange and observed emergent swarm intelligence. Another example can be influence of variable range of sense organs of swarm entities on the swarm stability

High-level Motion Planning for CPG-driven Modular Robots

  • DOI: 10.1016/j.robot.2015.01.006
  • Odkaz: https://doi.org/10.1016/j.robot.2015.01.006
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    Modular robots may become candidates for search and rescue operations or even for future space missions, as they can change their structure to adapt to terrain conditions and to better fulfill a given task. A core problem in such missions is the ability to visit distant places in rough terrain. Traditionally, the motion of modular robots is modeled using locomotion generators that can provide various gaits, e.g. crawling or walking. However, pure locomotion generation cannot ensure that desired places in a complex environment with obstacles will in fact be reached. These cases require several locomotion generators providing motion primitives that are switched using a planning process that takes the obstacles into account. In this paper, we present a novel motion planning method for modular robots equipped with elementary motion primitives. The utilization of primitives significantly reduces the complexity of the motion planning which enables plans to be created for robots of arbitrary shapes. The primitives used here do not need to cope with environmental changes, which can therefore be realized using simple locomotion generators that are scalable, i.e., the primitives can provide motion for robots with many modules. As the motion primitives are realized using locomotion generators, no reconfiguration is required and the proposed approach can thus be used even for modular robots without self-reconfiguration capabilities. The performance of the proposed algorithm has been experimentally verified in various environments, in physical simulations and also in hardware experiments.

MAV-swarms: unmanned aerial vehicles stabilized along a given path using onboard relative localization

  • Autoři: doc. Ing. Martin Saska, Dr. rer. nat.,
  • Publikace: Proceedings of 2015 International Conference on Unmanned Aircraft Systems. Piscataway: Institute of Electrical and Electronics Engineers, 2015. p. 894-903. ISBN 9781479960101.
  • Rok: 2015
  • DOI: 10.1109/ICUAS.2015.7152376
  • Odkaz: https://doi.org/10.1109/ICUAS.2015.7152376
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A novel approach for stabilization and navigation of swarms of Micro Aerial Vehicles (MAVs) along a predefined path through a complex environment with obstacles is introduced in this paper. The method enables to control large MAV swarms (in literature also called UAV swarms) based only on onboard sensors and without any inter-vehicle communication. The proposed method relies on visual localization modules carried by all MAVs, which provide estimation of the relative positions of neighbours in the swarm. Guess on the positions of the neighbouring MAVs and information on the relative positions of obstacles are integrated into swarm stabilization via Reynolds’ Boids model. The performance of the complex system is shown in various numerical simulations and in experiments with a fleet of MAVs in the paper. Presented experimental results with the multi-MAV swarm were conducted in indoor and outdoor environment, and without using any external global localization system such as Vicon motion capture system or GPS localization.

Motion planning with adaptive motion primitives for modular robots

  • DOI: 10.1016/j.asoc.2015.05.002
  • Odkaz: https://doi.org/10.1016/j.asoc.2015.05.002
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    This paper presents a novel motion planning algorithm for modular robots moving in environments with diverse terrain conditions. This requires the planner to generate a suitable control signal for all actuators, which can be computationally intensive. To decrease the complexity of the planning task, the concept of motion primitives is used. The motion primitives generate simple motions like ‘crawl-forward’ or ‘turn-left’ and the motion planner constructs a plan using these primitives. To preserve the efficiency and robustness of the planner on varying terrains, a novel schema called RRT-AMP (Rapidly Exploring Random Trees with Adaptive Motion Primitives) for adapting the motion primitives is introduced. The adaptation procedure is integrated into the planning process, which allows the planner simultaneously to adapt the primitives and to use them to obtain the final plan. Besides adaptation in changing environments, RRT-AMP can adapt motion primitives if some module fails. The methods is experimentally verified with robots of different morphologies to show its adaptation and planning abilities in complex environments.

A Practical Multirobot Localization System

  • DOI: 10.1007/s10846-014-0041-x
  • Odkaz: https://doi.org/10.1007/s10846-014-0041-x
  • Pracoviště: Katedra kybernetiky, Katedra počítačů
  • Anotace:
    We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with millimeter precision. In addition, we present the method’s mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera’s intrinsic parameters and hardware’s processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at http://purl.org/robotics/whycon; so, it can be used as an enabling technology for various mobile robotic problems.

Autonomous Deployment of Swarms of Micro-Aerial Vehicles in Cooperative Surveillance

  • Autoři: doc. Ing. Martin Saska, Dr. rer. nat., Chudoba, J., Přeučil, L., Thomas, J., Loianno, G., Třešňák, A., Ing. Vojtěch Vonásek, Ph.D., Kumar, V.
  • Publikace: Proceedings of 2014 2014 International Conference on Unmanned Aircraft Systems (ICUAS). Danvers: IEEE Computer society, 2014. p. 584-595. ISBN 978-1-4799-2376-2.
  • Rok: 2014
  • DOI: 10.1109/ICUAS.2014.6842301
  • Odkaz: https://doi.org/10.1109/ICUAS.2014.6842301
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    An algorithm for autonomous deployment of groups of Micro Aerial Vehicles (MAVs) in the cooperative surveillance task is presented in this paper. The algorithm enables to find a proper distributions of all MAVs in surveillance locations together with feasible and collision free trajectories from their initial position. The solution of the MAV-group deployment satisfies motion constraints of MAVs, environment constraints (non-fly zones) and constraints imposed by a visual onboard relative localization. The onboard relative localization, which is used for stabilization of the group flying in a compact formation, acts as an enabling technique for utilization of MAVs in situations where an external local system is not available or lacks the sufficient precision.

Coordination and Navigation of Heterogeneous MAV–UGV Formations Localized by a ‘hawk-eye’-like Approach Under a Model Predictive Control Scheme

  • DOI: 10.1177/0278364914530482
  • Odkaz: https://doi.org/10.1177/0278364914530482
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    An approach for coordination and control of 3D heterogeneous formations of unmanned aerial and ground vehicles under hawk-eye like relative localization is presented in this paper. The core of the method lies in the use of visual top-view feedback from flying robots for the stabilization of the entire group in a leader-follower formation. We formulate a novel Model Predictive Control (MPC) based methodology for guiding the formation. The method is employed to solve the trajectory planning and control of a virtual leader into a desired target region. In addition, the method is used for keeping the following vehicles in the desired shape of the group. The approach is designed to ensure direct visibility between aerial and ground vehicles, which is crucial for the formation stabilization using the hawk-eye like approach. The presented system is verified in numerous experiments inspired by search and rescue applications, where the formation acts as a searching phalanx. In addition, stability and convergence analyses are provided to explicitly determine the limitations of the method in real-world applications.

Fast On-Board Motion Planning for Modular Robots

  • DOI: 10.1109/ICRA.2014.6907008
  • Odkaz: https://doi.org/10.1109/ICRA.2014.6907008
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    Modular robots, which are systems made of many robotic modules, can utilize various types of locomotion. Different approaches can be used to generate these basic motion skills — motion primitives. To move in a complex environment, several motion primitives are needed and a mechanism to switch them is required. This can be realized using a high-level motion planning. To enable autonomous operation of modular robots equipped with limited computational resources, it is necessary to generate the motion plans on-board, i.e., without external computers. In this paper, we propose a novel simplified motion model of a modular robot, which allows the robot to employ the motion planner as a fast on-board replanner. The proposed approach has been verified both in simulations as well as with real robots.

Fault-Tolerant Formation Driving Mechanism Designed for Heterogeneous MAVs-UGVs Groups

  • DOI: 10.1007/s10846-013-9976-6
  • Odkaz: https://doi.org/10.1007/s10846-013-9976-6
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A fault-tolerant method for stabilization and navigation of 3D heterogeneous formations is proposed in this paper. The presented Model Predictive Control (MPC) based approach enables to deploy compact formations of closely cooperating autonomous aerial and ground robots in surveillance scenarios without the necessity of a precise external localization. Instead, the proposed method relies on a top-view visual relative localization provided by the micro aerial vehicles flying above the ground robots and on a simple yet stable visual based navigation using images from an onboard monocular camera. The MPC based schema together with a fault detection and recovery mechanism provide a robust solution applicable in complex environments with static and dynamic obstacles. The core of the proposed leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along a desired path that has to be followed by the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The uninterrupted visibility is crucial for the employed top-view localization and therefore for the stabilization of the group. The proposed formation driving method and the fault recovery mechanisms are verified by simulations and hardware experiments presented in the paper.

Localization and stabilization of micro aerial vehicles based on visual features tracking

  • DOI: 10.1109/ICUAS.2014.6842304
  • Odkaz: https://doi.org/10.1109/ICUAS.2014.6842304
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    This article presents a method for long-term autonomous micro-aerial vehicle (MAV) localization and position stabilization. The proposed method extends MAV proprietary stabilization based on inertial sensor or optical flow processing, without use of an external positioning system. The method extracts visual features from the images captured by a down-looking camera mounted under the MAV and matching these to previously observed features. Due to its precision and reliability, the method is well suited for stabilization of MAVs acting in closely cooperating compact teams with small mutual distances between team members. Performance of the proposed method is demonstrated by experiments on a quad-copter equipped with all necessary sensors and computers for the autonomous operation.

Motion Planning and Control of Formations of Micro Aerial Vehicles

  • Autoři: doc. Ing. Martin Saska, Dr. rer. nat., Kasl, Z., Přeučil, L.
  • Publikace: Proceedings of The 19th World Congress of the International Federation of Automatic Control. Pretoria: IFAC, 2014, pp. 1228-1233. ISSN 1474-6670. ISBN 978-3-902823-62-5.
  • Rok: 2014
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A model predictive control based algorithm for maintenance of leader-follower formations of micro-scale aerial vehicles is proposed in this paper. The approach is designed for stabilization of teams of unmanned quadrotor helicopters and for their motion planning into a distant target region. The presented method of the model predictive control with a planning horizon enables integration of an obstacle avoidance function into the local control of the formation as well as into the global plan of formation movement. Deployment of the method in real-world scenarios, with particular interest in failure recovery and inter-vehicle avoidance, is verified in various simulations.

Plume Tracking by a Self-stabilized Group of Micro Aerial Vehicles

  • Autoři: doc. Ing. Martin Saska, Dr. rer. nat., Langr, J., Přeučil, L.
  • Publikace: Modelling and Simulation for Autonomous Systems. Cham: Springer, 2014. p. 44-55. Lecture Notes in Computer Science. ISSN 0302-9743. ISBN 978-3-319-13822-0.
  • Rok: 2014
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A cooperative odor plume tracking approach designed for use with groups of micro-scale, autonomous helicopters in GNSS-denied environment is proposed in this paper. The designed method is based on a particle swarm optimization enhanced for efficient and fast cooperative searching for gas sources. The possibility of MAVs deployment in GNSS denied environment is enabled by employed visual relative localization using onboard monocular cameras and identification patterns. In addition to constraints given by the relative localization (necessity of direct visibility and limited range of the system), MAV motion constraints and non-colliding multi-robot coordination are satisfied in the method. The developed method has been verified using a numerical model of smoke plume in various simulations and real experiments with a fleet of MAVs.

Rapidly Exploring Random Trees-Based Initialization of MPC Technique Designed for Formations of MAVs

  • Autoři: Kasl, Z., doc. Ing. Martin Saska, Dr. rer. nat., Přeučil, L.
  • Publikace: Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics. Porto: SciTePress - Science and Technology Publications, 2014, pp. 436-443. ISBN 978-989-758-040-6.
  • Rok: 2014
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    Motion planning techniques suited for initialization of Model Predictive Control based methodology applied for complex manoeuvring and stabilization of formations of Micro Aerial Vehicles is proposed in this paper. Two approaches to initialization of the formation driving method will be described, experimentally verified, evaluated and compared. The first proposed method is based on multiobjective optimization of the trajectory guess obtained by a Rapidly Exploring Random Trees technique. It represents an easy to implement and robust method suited for off-line initialization of the formation driving algorithm. The second proposed method is based on sequential processing of parts of the obtained trajectory. This method is well scalable and thus applicable in large workspaces with complex obstacles. In addition, the second method enables a significant reduction of computational time as is shown by comparison of series of simulations in different environments.

Swarms of Micro Aerial Vehicles Stabilized Under a Visual Relative Localization

  • Autoři: doc. Ing. Martin Saska, Dr. rer. nat., Vakula, J., Přeučil, L.
  • Publikace: ICRA2014: Proceedings of 2014 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2014. p. 3570-3575. ISSN 1050-4729. ISBN 978-1-4799-3684-7.
  • Rok: 2014
  • DOI: 10.1109/ICRA.2014.6907374
  • Odkaz: https://doi.org/10.1109/ICRA.2014.6907374
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A stabilization and control technique developed for steering swarms of unmanned micro aerial vehicles is proposed in this paper. The presented approach based on a visual relative localization of swarm particles is designed for utilization of multi-robot teams in real-world dynamic environments. The core of the swarming behaviour is inspired by Reynold’s BOID model proposed for 2D simulations of schooling behaviour of fish. The idea of the simple BOID model, with three simple rules: Separation, Alignment and Cohesion, is extended for swarms of quadrotors in this paper. The proposed solution integrates the swarming behaviour with the relative localization and with a stabilization and control mechanism, which respects fast dynamics of unmanned quadrotors. The proposed method aspires to be an enabling technique for deployment of swarms of micro aerial vehicles outside laboratories that are equipped with precise positioning systems. The swarming behaviour as well as the possibility of swarm stabilization with the visual relative localization in the control feedback are verified by simulations and partly by an experiment with quadrotors in this paper.

Ad-hoc Heterogeneous (MAV-UGV) Formations Stabilized Under a Top-View Relative Localization

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A leader-follower formation driving algorithm developed for control of heterogeneous groups of unmanned micro aerial and ground vehicles stabilized under a top-view relative localization is presented in this paper. The core of the proposed method lies in a novel avoidance function, in which the entire 3D formation is represented by a convex hull projected along a desired path to be followed by the group. Such a representation of the formation provides non-collision trajectories of the robots and respects requirements of the direct visibility between the team members in environment with static as well as dynamic obstacles, which is crucial for the top-view localization. The algorithm is suited for utilization of a simple yet stable visual based navigation of the group (referred to as GeNav), which together with the on-board relative localization enables deployment of large teams of micro-scale robots in environments without any available global localization system. We formulate a novel Model Predictive Control (MPC) based concept that enables to respond to the changing environment and that provides a robust solution with team members' failure tolerance included. The performance of the proposed method is verified by numerical and hardware experiments inspired by reconnaissance and surveillance missions.

Control and Navigation in Manoeuvres of Formations of Unmanned Mobile Vehicles

  • DOI: 10.1016/j.ejcon.2012.10.003
  • Odkaz: https://doi.org/10.1016/j.ejcon.2012.10.003
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    This paper proposes a method for controlling formations of autonomous nonholonomic vehicles in order to reach a desired target region. The approach is based on utilization of pairs of virtual leaders whose control inputs are obtained in a single optimization process using model predictive control (MPC) methodology. The obtained solution of the optimization includes both a complete plan for the formation including the overall structure of robots’ workspace and control inputs for each vehicle. This ensures collision-free trajectories between the robots as well as dynamic obstacles. The proposed method enables to autonomously design arbitrary manoeuvres, like reverse driving or rotations of compact formations of car-like robots. Such a complicated behavior is illustrated by simulations and by experiments. Furthermore, the requirements that guarantee convergence of the group to the target region are formulated.

Global Motion Planning for Modular Robots with Local Motion Primitives

  • DOI: 10.1109/ICRA.2013.6630912
  • Odkaz: https://doi.org/10.1109/ICRA.2013.6630912
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    The ability to move in complex environments is a key property required for deployment of modular robots in challenging applications like search & rescue missions or space exploration. Wide range of motion types like crawling or walking can be achieved using Central Pattern Generators producing periodic control signals. Although these motions can be very effective to steer robots in their vicinity or in a given direction, they need to be switched to reach a far position in the environment. This paper presents a novel modification of Rapidly Exploring Random Tree (RRT) algorithm for modular robots. For efficient exploration of the configuration space, predefined motion primitives are used. While the motion primitives provide effective local motions, the RRT-based planner switches them in order to reach the desired global goal.

Low-Cost Embedded System for Relative Localization in Robotic Swarms

  • DOI: 10.1109/ICRA.2013.6630694
  • Odkaz: https://doi.org/10.1109/ICRA.2013.6630694
  • Pracoviště: Katedra kybernetiky, Katedra počítačů
  • Anotace:
    In this paper, we present a small, light-weight, low-cost, fast and reliable system designed to satisfy requirements of relative localization within a swarm of micro aerial vehicles. The core of the proposed solution is based on off-the-shelf components consisting of the Caspa camera module and Gumstix Overo board accompanied by a developed efficient image processing method for detecting black and white circular patterns. Although the idea of the roundel recognition is simple, the developed system exhibits reliable and fast estimation of the relative position of the pattern up to 30 fps using the full resolution of the Caspa camera. Thus, the system is suited to meet requirements for a vision based stabilization of the robotic swarm. The intent of this paper is to present the developed system as an enabling technology for various robotic tasks.

Motion Planning for a Cable Driven Parallel Multiple Manipulator Emulating a Swarm of MAVs

  • DOI: 10.1109/RoMoCo.2013.6614577
  • Odkaz: https://doi.org/10.1109/RoMoCo.2013.6614577
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    The Cable-Driven Parallel Multiple Manipulator is a supporting tool for development of control and navigation strategies of a swarm of Micro Aerial Vehicles (MAV). A crucial part of the system is a motion planning required to move the effectors representing individual MAVs to positions determined by a high-level algorithm controlling behavior of the swarm. Due to many degrees of freedom of such a system, finding feasible trajectories requires search in a high-dimensional configuration space, which is solved using a planner based on Rapidly Exploring Random Tree algorithm (RRT).

Navigation, Localization and Stabilization of Formations of Unmanned Aerial and Ground Vehicles

  • DOI: 10.1109/ICUAS.2013.6564767
  • Odkaz: https://doi.org/10.1109/ICUAS.2013.6564767
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A leader-follower formation driving algorithm developed for control of heterogeneous groups of unmanned micro aerial and ground vehicles stabilized under a top-view relative localization is presented in this paper. The core of the proposed method lies in a novel avoidance function, in which the entire 3D formation is represented by a convex hull projected along a desired path to be followed by the group. Such a representation of the formation provides non-collision trajectories of the robots and respects requirements of the direct visibility between the team members in environment with static as well as dynamic obstacles, which is crucial for the top-view localization. The algorithm is suited for utilization of a simple yet stable visual based navigation of the group (referred to as GeNav), which together with the on-board relative localization enables deployment of large teams of micro-scale robots in environments without any available global localization system. We formulate a novel Model Predictive Control (MPC) based concept that enables to respond to the changing environment and that provides a robust solution with team members' failure tolerance included. The performance of the proposed method is verified by numerical and hardware experiments inspired by reconnaissance and surveillance missions.

Trajectory Planning and Control for Airport Snow Sweeping by Autonomous Formations of Ploughs

  • DOI: 10.1007/s10846-013-9829-3
  • Odkaz: https://doi.org/10.1007/s10846-013-9829-3
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    This article presents a control approach that enables an autonomous operation of fleets of unmanned snow ploughs at large airports. The proposed method is suited for the special demands of tasks of the airport snow shovelling. The robots have to keep a compact formation of variable shapes during moving into the locations of their deployment and for the autonomous sweeping of runways surfaces. These tasks are solved in two independent modes of the airport snow shoveling. The moving and the sweeping modes provide a full-scale solution of the trajectory planning and coordination of vehicles applicable in the specific airport environment. Nevertheless, they are suited for any multi-robot application that requires complex manoeuvres of compact formations in dynamic environment. The approach encapsulates the dynamic trajectory planning and the control of the entire formation into one merged optimization process via a novel Model Predictive Control (MPC) based methodology. The obtained solution of the optimization includes a complete plan for the formation. It respects the overall structure of the workspace and actual control inputs for each vehicle to ensure collision avoidance and coordination of team members. The presented method enables to autonomously design arbitrary manoeuvres, like reverse driving or turning of compact formations of car-like robots, which frequently occur in the airport sweeping application. Examples of such scenarios verifying the performance of the approach are shown in simulations and hardware experiments in this article. Furthermore, the requirements that guarantee a convergence of the group to a desired state are formulated for the formation acting in the sweeping and moving modes.

Trajectory Planning and Stabilization for Formations Acting in Dynamic Environments

  • Autoři: doc. Ing. Martin Saska, Dr. rer. nat., Spurný, V., Přeučil, L.
  • Publikace: Progress in Artificial Intelligence. Heidelberg: Springer, 2013, pp. 319-330. LNAI 8154. ISSN 0302-9743. ISBN 978-3-642-40668-3.
  • Rok: 2013
  • DOI: 10.1007/978-3-642-40669-0_28
  • Odkaz: https://doi.org/10.1007/978-3-642-40669-0_28
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A formation driving mechanism suited for utilization of multi-robot teams in highly dynamic environments is proposed in this paper. The presented approach enables to integrate a prediction of behaviour of moving objects in robots' workspace into a formation stabilization and navigation framework. It will be shown that such an inclusion of a model of the surrounding environment directly into the formation control mechanisms facilitates avoidance manoeuvres in a case of fast dynamic objects approaching in a collision course. Besides, the proposed model predictive control based approach enables to stabilize robots in a compact formation and it provides a failure tolerance mechanism with an inter collision avoidance. The abilities of the algorithm are verified via numerous simulations and hardware experiments with the main focus on evaluation of performance of the algorithm with different sensing capabilities of the robotic system.

Cooperative Micro UAV-UGV Autonomous Indoor Surveillance

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    In this paper, we present a heterogenous UGV-UAV system cooperatively solving tasks of periodical surveillance in indoor environments. In the proposed scenario, the UGV is equipped with an interactive helipad and it acts as a carrier of the UAV. The UAV is a light-weight quadro-rotor helicopter equipped with two cameras, which are used to inspect locations inaccessible for the UGV. The paper is focused on the most crucial aspects of the proposed UAV-UGV periodical surveillance that are visual navigation, localization and autonomous landing that need to be done periodically. We propose two concepts of mobile helipads employed for correction of imprecise landing of the UAV. Beside the description of the visual navigation, relative localization and both helipads, a study of landing performance is provided. The performance of the complex system is proven by an experiment of autonomous periodical surveillance in a changing environment with presence of people.

Coordination and Navigation of Heterogeneous UAVs-UGVs Teams Localized by a Hawk-Eye Approach

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A navigation and stabilization scheme for 3D heterogeneous (UAVs and UGVs) formations acting under a hawk-eye like relative localization is presented in this paper. We formulate a novel Model Predictive Control (MPC) based concept for formation driving in a leader-follower constellation into a required target region. The formation to target region problem in 3D is solved using the MPC methodology for both: i) the trajectory planning and control of a virtual leader, and ii) the control and stabilization of followers - UAVs and UGVs. The core of the method lies in a novel avoidance function based on a model of the formation respecting requirements of the direct visibility between the team members in environment with obstacles, which is crucial for the hawk-eye localization.

Low Cost MAV Platform AR-Drone in Experimental Verifications of Methods for Vision Based Autonomous Navigation

  • DOI: 10.1109/IROS.2012.6386277
  • Odkaz: https://doi.org/10.1109/IROS.2012.6386277
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    Several navigation tasks utilizing a low-cost Micro Aerial Vehicle (MAV) platform AR-drone are presented in this paper to show how it can be used in an experimental verification of scientific theories and developed methodologies. An important part of this paper is an attached video showing a set of such experiments. The presented methods rely on visual navigation and localization using on-board cameras of the AR-drone employed in the control feedback. The aim of this paper is to demonstrate flight performance of this platform in real world scenarios of mobile robotics.

Predictive Control of Unmanned Formations

  • Autoři: doc. Ing. Martin Saska, Dr. rer. nat., Přeučil, L.
  • Publikace: Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics. Setúbal: INSTICC Press, 2012, pp. 403-406. ISBN 978-989-8565-22-8.
  • Rok: 2012
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A receding horizon control based approach for guiding of autonomous formations of nonholonomic robots in a leader-follower constellation is proposed in this paper. The presented method ensures dynamic obstacle avoidance, formation coordination as well as failure tolerance. The robustness of the algorithm is verified by numerical multi-robot experiments. Besides, effects of system's parameters on the algorithm performance are investigated.

Techniques for Modeling Simulation Environments for Modular Robotics

  • DOI: 10.3182/20120215-3-AT-3016.00037
  • Odkaz: https://doi.org/10.3182/20120215-3-AT-3016.00037
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    In modular robotics, complex structures can be formed from basic modules to solve tasks which would be difficult for a single robot. The development of techniques for adaptation and evolution of multi-robot organisms is the subject of Symbrion project. In the project, the bio-inspired evolutionary algorithms are massively simulated prior to run them on a real hardware. It is crucial to evolve behaviors of the robots in a simulation, that is close to a real world. Hence, accurate and efficient representation of an environment in the simulation is needed. Here, the environment is modeled using set of 3D objects (usually triangle meshes). The robots learn simple motion primitives or complex movement patterns during many runs of the evolution. The learned skills are then used during experiments with a real hardware. In this paper, we present methods for building 3D model of a real arena using a laser rangefinder. The resulting 3D models consist of triangles. They can be constructed in various level of details using state-of-the-art methods for 3D reconstruction. We will show, how the size of the models influences the speed of the simulation.

Bringing Reality to Evolution of Modular Robots: Bio-Inspired Techniques for Building a Simulation Environment in the SYMBRION Project

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    Two neural network (NN) based techniques for building a simulation environment, which is employed for evolution of modular robots in the Symbrion project, are presented in this paper. The methods are able to build models of real world with variable accuracy and amount of stored information depending upon the performed tasks and evolutionary processes. In this paper, the presented algorithms are verified via experiments in scenarios designed to demonstrate the process of autonomous creation of complex artificial organisms. Performance of the methods is compared in experiments with real data and their employability in modular robotics is discussed. Beside these, the entire process of environment data acquisition and pre-processing during the real evolutionary experiments in the Symbrion project will be briefly described.

Formation Coordination with Path Planning in Space of Multinomials

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A formation driving approach based on spline path planning and vehicles coordination using the model predictive control is investigated in this paper. The proposed system enables to control all members of the team as well as to navigate the formation itself fully autonomously in a dynamic environment. Both necessary sub-tasks, robots' control and path planning for the formation, are formulated as optimization problems solved by various numerical methods in the paper. Applicability and robustness of all approaches and the entire system itself are verified by simulations as well as by real robotic experiments.

Motion planning and coordination of heterogenerous formations of mobile robots and unmanned aerial vehicles

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    The formations of mobile robots can accomplish more challenging tasks than a single robot. However current methods for formation control rely on accurate localization of the robots within the formation. In this paper we propose a localization and navigation system for a formation of autonomous robots. In our method the robots within the formation are localized using camera data from a helicopter flying above the formation. The proposed localization system has been experimentally verified in a search & rescue scenario.

Roads Sweeping by Unmanned Multi-vehicle Formations

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A system for autonomous roads sweeping by applying formations of mobile robots is presented in this paper. The proposed approach based on Receding Horizon Control solves the formation navigation, planning and stabilization in real-world environments with static and dynamic obstacles. The formations employed for sweeping are built up ad-hoc, taking into account length of robots' effectors (e.g. shovels, sweepers) and width of the working area. Presented method enables to smoothly merge smaller teams with the view of sweeping the larger roads (e.g. runways, highways). The formations can operate in two modes: sweeping and moving. In the sweeping mode, the formations are guided with an aim to effectively cover the cleaning roads, while in the moving mode, the planning system emphasizes the effort to reach a desired target. Furthermore, the moving mode enables to autonomously design complex formation maneuvers, as is reverse driving or turning on spot.

Airport snow shoveling

  • DOI: 10.1109/IROS.2010.5653747
  • Odkaz: https://doi.org/10.1109/IROS.2010.5653747
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    In this paper, we present results of a feasibility study of airport snow shoveling with multiple formations of autonomous snowplow robots. The main idea of the approach is to form temporary coalitions of vehicles, whose size depends on the width of the roads to be cleaned. We propose to divide the problem of snow shoveling into the subproblems of task allocation and motion coordination. For the task allocation we designed a multi-agent method applicable in the dynamic environment of airports. The motion coordination part focuses on generating trajectories for the vehicle formations based on the output of the task allocation module. Furthermore, we have developed a novel approach of formation stabilization into variable shapes depending on the width of runways.

Control of ad-hoc formations for autonomous airport snow shoveling

  • Autoři: doc. Ing. Martin Saska, Dr. rer. nat., Ing. Vojtěch Vonásek, Ph.D., Přeučil, L.
  • Publikace: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010. Taipei: IEEE Industrial Electronics Society, 2010, pp. 4995-5000. ISSN 2153-0858. ISBN 978-1-4244-6675-7. Available from: http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5650712&queryText%3DControl+of+ad-hoc+formations+for+autonomous+
  • Rok: 2010
  • DOI: 10.1109/IROS.2010.5650712
  • Odkaz: https://doi.org/10.1109/IROS.2010.5650712
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    In this paper, we present a framework that applies multiple groups of autonomous snowploughs for efficiently removing the snow from airfields. The proposed approach includes formation stabilization into variable shapes depending on the width of runways. The paper is focused on trajectory planning and control during splitting and coupling of formations for cleaning smaller auxiliary roads surrounding main runways. We propose a general method using a receding horizon control for iterative formation assignment. The algorithm is adapted for the kinematics of car-like robots and can be utilized in arbitrary static and dynamic airport assemblage. The proposed approach has been verified by simulations and by hardware experiments.

Formace mobilnich robotů

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    Zkusme si nejdříve položit otázku, proč vlastně udržovat mobilní roboty ve formaci. Pomineme-li skutečnost, že roboti tvořící během svého pohybu předem zadané obrazce působí esteticky a pro nezasvěcené i inteligentně, koordinovaný pohyb robotů umožní provádět úkony, které jsou samostatně se pohybujícím robotem neuskutečnitelné.

Navigation and Formation Control Employing Complementary Virtual Leaders for Complex Maneuvers

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    Complex maneuvers of formations of car-like autonomous vehicles are investigated in this paper. The proposed algorithm provides a complete plan for the formation to solve desired tasks and actual control inputs for each robot to ensure collision-free trajectories with respect to neighboring robots as well as dynamic obstacles. The method is based on utilization of complementary virtual leaders whose control inputs are obtained in one merged optimization process using receding horizon control methodologies. The functionality of the system, which enables reverse driving and arbitrary rotations of formations of nonholonomic robots, is verified by simulations of multi-robot tasks and by hardware experiments.

SyRoTek - A Robotic System for Education

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    This paper presents insight to ideas and the current state of the project SyRoTek (System for a robotic e-learning) that aims to create a platform for students' practical verification of gained knowledge in the fields of Robotics and Artificial Intelligence. A set of real mobile robots is being developed in order to provide remote access to real hardware for enrolled students. The advantage of the real system over a pure virtual simulated environment is in realistic confrontation with noise and uncertainty that is an indivisible part of the real world. In such a system, students can acquire in deep understanding of main studied principles in an attractive form, as students (especially future engineers) like to control real things. Robots are designed with special attention to long-term and heavy duty usage. Moreover, support for semi-autonomous evaluation of students' solution of their assignments is a part of the system.

SyRoTek - A Robotic System for Education

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    This paper presents insight to ideas and the current state of the project SyRoTek (System for a robotic e-learning) that aims to create a platform for students' practical verification of gained knowledge in the fields of Robotics and Artificial Intelligence. A set of real mobile robots is being developed in order to provide remote access to real hardware for enrolled students. The advantage of the real system over a pure virtual simulated environment is in realistic confrontation with noise and uncertainty that is an indivisible part of the real world. In such a system, students can acquire in deep understanding of main studied principles in an attractive form, as students (especially future engineers) like to control real things. Robots are designed with special attention to long-term and heavy duty usage. Moreover, support for semi-autonomous evaluation of students' solution of their assignments is a part of the system.

Application of Coordinated Multi-Vehicle Formations for Snow Shoveling on Airports

  • DOI: 10.1007/s11370-009-0048-5
  • Odkaz: https://doi.org/10.1007/s11370-009-0048-5
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    In this paper, we present a framework that applies multiple groups of autonomous snowplow robots for efficiently removing the snow from airfields. The main idea is to form temporary coalitions of vehicles, whose size depends on the width of the roads to clean. The robots of a coalition then arrange in formation and accomplish assigned sweeping tasks. In the paper the problem of snow shoveling is divided into the subproblems of task allocation and motion coordination. For the task allocation we propose a multi-agent method designed for the dynamic environment of airports. The motion coordination part focuses on generating trajectories for the vehicle formations based on the output of the task allocation module. Furthermore a specific feedback controller is introduced that achieves optimal breadthwise road coverage even in sharp turns. All components as well as the complete system have been verified in various simulations.

Coordination of mobile robot group for unknown environment mapping

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    This contribution describes robotic system for cooperative outdoor exploration. In our case, we use outdoor robotic platforms P3AT communicating over a wireless network each equipped with sweeping laser rangefinder, monocular camera, GPS and compass. Information obtained via global positioning system, compass and odometry are fused by kalman methods to provide position estimation to support global localization. Downward inclined laser rangefinder is used for reliable detection of dynamic obstacles and recognition of traversable terrain. The information about texture of traversable terrain and obstacles is then transferred to the vision system and a local map is created. This local map in form of occupancy grid is utilized for fast collision avoidance algorithms. As the robot moves, local maps are compiled to create a global one, where frontier regions can be detected and distributed among individual robots. Laser rangefinder can be swept to obtain above-ground 3D data.

Elliptic Net - A Path Planning Algorithm for Dynamic Environments

  • Autoři: doc. Ing. Martin Saska, Dr. rer. nat., Kulich, M., Přeučil, L.
  • Publikace: ICINCO 2006 - PROCEEDINGS. Setúbal: Institute for Systems and Technologies of Information, Control and Communication, 2006. pp. 372-377. ISBN 972-8865-61-9.
  • Rok: 2006
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    Robot path planning and obstacle avoidance problems play an important role in mobile robotics. The standard algorithms assume that a working environment is static or changing slowly. Moreover, computation time and time needed for realization of the planned path is usually not crucial. The paper describes a novel algorithm that is focused especially to deal with these two issues: the presented algorithm - Elliptic Net is fast and robust and therefore usable in highly dynamic environments. The main idea of the algorithm is to cover an interesting part of the working environment by a set of nodes and to construct a graph where the nodes are connected by edges. Weights of the edges are then determined according to their lengths and distance to obstacles. This allows to choose whether a generated path will be safe (far from obstacles), short, or weigh these two criterions.

Formation Driving Using Particle Swarm Optimization and Reactive Obstacle Avoidance

  • Autoři: Hess, M., doc. Ing. Martin Saska, Dr. rer. nat., Schiling, K.
  • Publikace: Proceedings - First IFAC Workshop on Multivehicle Systems. Lisboa: Instituto Suparior Téchnico Av. Rovisco Pais, 2006, pp. 32-37. ISBN 85-87978-12-8.
  • Rok: 2006
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    This paper presents a new leader following approach for a formation of carlike robots. The path planning problem is solved for the leader with optimized spline functions, which are generated by an evolutionary technique called particle swarm optimization. Because of the expansion of the formation, a collision free path is not guaranteed for the following vehicles. In our approach the followers reactively avoid collisions with path-blocking obstacles and also with other robots within the formation. The developed algorithms have been tested intensively in various simulations.

Reasoning and planning for robotsoccer

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    This paper presents architecture of system G-Bots for robotic soccer. Apart from our open architecture description, we focus on new approaches applied in reasoning and planning system components.

Robot Path Planning using Particle Swarm Optimization of Ferguson Splines

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    Robot path planning problem is one of most important task mobile robots. This paper proposes an original approach using a path description by string of cubic splines. Such path is easy executable and natural for car-like robot. Furthermore, it is possible to ensure smooth derivation in connections of particular splines. In this case, the path planning is equivalent to optimization of parameters of splines. An evolutionary technique called Particle Swarm Optimization (PSO) was used hereunder due to its' relatively fast convergence and global search character. Various settings of PSO parameters were tested and the best setting was compared to two classical mobile robot path planning algorithms.

Transformed Net - Collision Avoidance Algorithm for Robotic Soccer

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    Fast and robust obstacle avoidance plays an important role for design of a successful robot soccer team, although not all teams use it nowadays. The standard algorithms assume that a working environment is static or changing slowly. Moreover, computation time and time needed for realization of the planned path is usually not crucial. Speed of robot soccer players (which act as obstacles) can be several meters per second, what requires low reaction time. One criterion for obstacle avoidance is therefore to plan a path far enough from opponent robots to guarantee that their trajectories will not collide with the planned one. This is in contradiction to a primary goal of robot soccer - to reach the desired position as fast as possible. An obstacle avoidance algorithm suited for robot soccer should find acceptable compromise between these two antagonistic requirements.

Za stránku zodpovídá: Ing. Mgr. Radovan Suk