Ing. Tomáš Báča, Ph.D.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This paper presents Rocket Experiment (REX) that was part of a dual-payload rocket campaign for NASA’s sounding rocket Black Brant IX with water recovery technology. This mission was a suborbital sounding rocket flight that was launched and recovered on April 4, 2018 and targeted the Vela supernova remnant. The purpose of REX was to classify the Technology Readiness Level of onboard devices designed for space applications. The devices were two wide-field X-ray telescopes consisting of a combination of Lobster-Eye (LE) optics with an uncooled Timepix detector (256 px × 256 px @ 55 μm), and additional sensors. The first telescope uses a two-dimensional combination of LE modules with a focal length of 1 m and a Field of View (FOV) of 1.0◦ × 1.2◦ and operates in the energy range of 3 – 60 keV. The second telescope was a one-dimensional LE with a focal length of 243 mm and a FOV of 2.7◦ × 8.0◦ for the energy range 3 – 40 keV. The X-ray telescopes were supplemented by a camera in the visible spectrum with 1.280 px × 1.024 px resolution, which was used to obtain images of the observed sources and to verify the resulting pointing of the rocket carrier. Other devices also include infrared array sensors and inertial measurement units tested for future small satellite missions. The data handler and communication system were built using the Robot Operating System, and both the system and the electronics were deployed and operated in-flight. The hardware was successfully recovered after the launch and the data were extracted

—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.

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.

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.

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.

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.

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.

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.

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.

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.

This paper presents the results of in-orbit commissioning of the first Czech technological CubeSat satellite of VZLUSAT-1. The 2U nanosatellite was designed and built during the 2013 to 2016 period. It was successfully launched into Low Earth Orbit of 505 km altitude on June 23, 2017, as part of international mission QB50 onboard a PSLV C38 launch vehicle. The satellite was developed in the Czech Republic by the Czech Aerospace Research Centre, in cooperation with Czech industrial partners and universities. The nanosatellite has three main payloads. The housing is made of a composite material which serves as a structural and radiation shielding material. A novel miniaturized X-Ray telescope with lobster-eye optics and an embedded Timepix detector represents the CubeSat’s scientific payload. The telescope has a wide field of view. VZLUSAT-1 also carries the FIPEX scientific instrument as part of the QB50 mission for measuring the molecular and atomic oxygen concentration in the upper atmosphere.

A novel design of x-ray optical system wide field telescope for astrophysical rocket experiments is investigated and tested in real space flight experiment. The proposed system is based on 1D and 2D modules with Schmidt Lobster Eye (LE) configuration allowing usage of multi-foil mirrors arranged to Schmidt profile.

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.

The paper summarizes the Rocket EXperiment (REX) Lobster Eye (LE) X-ray Telescope payload results. The experiment was performed by the PennState University with X-ray spectroscope on board a Water Recovery X-Ray Rocket (WRXR) launched on 4th April, 2018. The secondary payload was the REX LE X-ray Telescope. The REX LE X-ray telescope consists of two X-ray telescopes with one-dimensional (1D) and two-dimensional (2D) optics, a visible-light camera and an IR grid-eye. The primary structure consists of a metal housing for the optics and a carbon fiber baffle with the Timepix sensors mounted at the end. The observation data from the experiment are briefly presented and discussed.

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.

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.

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.

We present software for interfacing FITPix and USB Lite compatible readout electronics using Robot Operating System (ROS). ROS is widely adopted middleware for integration of sensors, processing algorithms and logic to autonomous systems such as robots, unmanned helicopters, and robotic payloads. Thanks to ROS, Timepix detectors can be deployed in automated experiments on platforms spanning from traditional desktop computers to small ARM devices such as Raspberry Pi and Odroid. Acquisition and detector settings can be controlled in Linux shell allowing deployment on headless devices. Using ROS networking capabilities, captured frames and detector control can be transmitted via a network, which allows building distributed processing pipelines. The proposed software is a lightweight package, easily connectable to existing visualization, logging, and processing software in ROS. It offers simple bindings to custom Python and C++ programs for real-time control of the acquisition or processing of the captured frames. Rospix was deployed on NASA suborbital rocket, which was successfully launched on April 4, 2018, from Kwajalein Atoll by Pennsylvania State University. Rospix provides reliable solution for mobile robots where the collected data are used in real time to guide the robot through an environment. We release Rospix using the GitHub platform and welcome the community to contribute on the first project connecting the fields of ionizing radiation imaging and mobile robotics.

The VZLUSAT-1 satellite, the first Czech CubeSat, was successfully launched on June 23, 2017, to a 510 km Sun-synchronous low-Earth orbit. It carries several scientific payloads including a Timepix detector as focal plane imager for the X-Ray telescope onboard. The Timepix detector contributes significantly to the satellite data collection, with more than 25 000 sampling acquisitions in the first year of deployment. Despite limitations of the satellite attitude control system, necessary for capturing X-Ray images of the Sun, the Timepix detector allows measuring the space radiation environment along the satellite orbit. As of September 2018, we conducted 33 whole-Earth mappings, recording radiation doses around the planet. Further, we show data from scans of the South Atlantic Anomaly and polar radiation horns, where the location and acquisition time were tailored to minimize event pile-up and particle track overlap. Since October 2017, the optics segment of the onboard X-Ray telescope was deployed, which exposed the Timepix detector unshielded to free open space. This change produced entirely new observations namely of low energy charged particles and a significant increase of measured particle flux. We also registered the effects of exposing the sensor to direct intense sunlight. We will summarize on the actual performance of the custom readout interface, which exceeds expectations in the constrained environment of the low-cost and low-powered CubeSat nanosatellite.

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.

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.

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.

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.

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).

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.

In the upcoming generation of small satellites there is a great potential for testing new sensors, processes and technologies for space and also for the creation of large in situ sensor networks. It plays a significant role in the more detailed examination, modelling and evaluation of the orbital environment. Scientific payloads based on the CubeSat technology are also feasible and the miniature X-ray telescope described in this paper may serve as an example. One of these small satellites from CubeSat family is a Czech CubeSat VZLUSAT-1, which is going to be launched during QB50 mission in 2017. This satellite has dimensions of 100 mm × 100 mm × 230 mm. The VZLUSAT-1 has three main payloads. The tested Radiation Hardened Composites Housing (RHCH) has ambitions to be used as a structural and shielding material to protect electronic devices in space or for constructions of future manned and unmanned spacecraft as well as Moon or Martian habitats. The novel miniaturized X-ray telescope with a Lobster Eye (LE) optics represents an example of CubeSat’s scientific payload. The telescope has a wide field of view and such systems may be essential in detecting the X-ray sources of various physical origin. VZLUSAT-1 also carries the FIPEX payload which measures the molecular and atomic oxygen density among part of the satellite group in QB50 mission. The VZLUSAT-1 is one of the constellation in the QB50 mission that create a measuring network around the Earth and provide multipoint, in-situ measurements of the atmosphere. This paper presents the VZLUSAT-1 satellite including the details about subsystems and payloads. The spacecraft was built between 2011 and 2015. In 2017, our VZLUSAT-1 team has finished the testing phase on a protoflight model and the VZLUSAT-1 is ready to be launched on a circular polar orbit at altitude 500 km ± 20 km.

This paper presents a Lobster Eye (LE) X-ray telescope developed for the Water Recovery X-ray Rocket (WRX-R) experiment. The primary payload of the rocket experiment is a soft X-ray spectroscope developed by the Pennsylvania State University (PSU), USA. The Czech team participates by hard LE X-ray telescope as a secondary payload. The astrophysical objective of the rocket experiment is the Vela Supernova of size about 8deg × 8deg. In the center of the nebula is a neutron star with a strong magnetic field, roughly the mass of the Sun and a diameter of about 20 kilometers forming the Vela pulsar. The primary objective of WRX-R is the spectral measurement of the outer part of the nebula in soft X-ray and FOV of 3.25deg × 3.25deg. The secondary objective (hard LE X-ray telescope) is the Vela neutron star observation. The hard LE telescope consists of two X-ray telescopes with the Timepix detector. First telescope uses 2D LE Schmidt optics (2D-LE-REX) with focal length over 1m and 4 Timepix detectors (2 × 2 matrix). The telescope FOV is 1.5deg × 1.5deg with spectral range from 3keV to 60keV. The second telescope uses 1D LE Schmidt optics (1D-LE-REX) with focal length of 25cm and one Timepix detector. The telescope is made as a wide field with FOV 4.5deg × 3.5deg and spectral range from 3keV to 40keV. The rocket experiment serves as a technology demonstration mission for the payloads. The LE X-ray telescopes can be in the future used as all‐sky monitor/surveyor. The astrophysical observation can cover the hard X-ray observation of astrophysical sources in time-domain, the GRBs surveying or the exploration of the gravitational wave sources.

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.

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.

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.

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.

We present the application of a Timepix detector on the VZLUSAT-1 nanosatellite. Timepix is a compact pixel detector (256×256 square pixels, 55×55 μm each) sensitive to hard X-ray radiation. It is suitable for detecting extraterrestrial X-rays due to its low noise characteristics, which enables measuring without special cooling. This project aims to verify the practicality of the detector in conjunction with 1-D Lobster-Eye optics to observe celestial sources between 5 and 20 keV. A modified USB interface (developed by IEAP at CTU in Prague) is used for low-level control of the Timepix. An additional 8-bit Atmel microcontroller is dedicated for commanding the detector and to process the data onboard the satellite. We present software methods for onboard post-processing of captured images, which are suitable for implementation under the constraints of the low-powered embedded hardware. Several measuring modes are prepared for different scenarios including single picture exposure, solar UV-light triggered exposure, and long-term all-sky monitoring. The work has been done within Medipix2 collaboration. The satellite is planned for launch in April 2017 as a part of the QB50 project with an end of life expectancy in 2019.

The CubeSat mission with the demonstrator of miniaturized X-ray telescope is presented. The paper presents one of the mission objectives of using the instrument for remote sensing of the Terrestrial Gamma-ray Flashes (TGFs). TGFs are intense sources of gamma-rays associated with lightning bolt activity and tropical thunderstorms. The measurement of TGFs exists and was measured by sounding rockets, high altitude balloons or several satellite missions. Past satellite missions were equipped with different detectors working from 10 keV up to 10 MeV. The RHESSI mission spectrum measurement of TGFs shows the maximum counts per second around 75 keV. The used detectors were in general big in volume and cannot be utilized by the CubeSat mission. The presented CubeSat is equipped with miniaturized X-ray telescope using the Timepix non-cooled pixel detector. The detector works between 3 and 60 keV in counting mode (dosimetry) or in spectrum mode with resolution 5 keV. The wide-field X-ray »Lobster-eye» optics/collimator (depending on energy) is used with a view angle of 3 degrees for the source location definition. The UV detectors with FOV 30 degrees and 1.5 degrees are added parallel with the optic as a part of the telescope. The telescope is equipped with software distinguishing between the photons and other particles. Using this software the TGF's detection is possible also in the field of South Atlantic anomaly. For the total ionization dose, the additional detector is used based on Silicone (12-60 keV) and CdTe (20 keV - 1 MeV). The presented instruments are the demonstrators suitable also for the astrophysical, sun and moon observation. The paper shows the details of TGF's observation modes, detectors details, data processing and handling system and mission. The CubeSat launch is planned to summer 2016. © Copyright SPIE. Downloading of the abstract is permitted for personal use only.

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.

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

We discuss image proceesing and imaging onboard VZLUSAT-1 nanosatelite. Several observation modes are presented. We present results gathered during experiments at University of Iowa and discuss possible stellar object candidates for orbital imaging.

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.

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.