In this work, we propose an improved artificially weighted spanning tree coverage (IAW-
STC) algorithm for distributed coverage path planning of multiple flying robots. The proposed approach is suitable for environment exploration in cluttered regions, where unexpected obstacles can appear. In addition, we present an online re-planner smoothing algorithm with unexpected detected obstacles. To validate our approach, we performed simulations and real robot experiments. The results showed that our proposed approach produces sub-regions with less redundancy than its previous version.
Estimating the Loss of Effectiveness of UAV Actuators in the Presence of Aerodynamic Effects
Landing an unmanned aerial vehicle unmanned aerial vehicle (UAV) on top of an unmanned surface vehicle (USV) in harsh open waters is a challenging problem, owing to forces that can damage the UAV due to a severe roll and/or pitch angle of the USV during touchdown. To tackle this, we propose a novel model predictive control (MPC) approach enabling a UAV to land autonomously on a USV in these harsh conditions. The MPC employs a novel objective function and an online decomposition of the oscillatory motion of the vessel to predict, attempt, and accomplish the landing during near-zero tilt of the landing platform. The nonlinear prediction of the motion of the vessel is performed using visual data from an onboard camera. Therefore, the system does not require any communication with the USV or a control station. The proposed method was analyzed in numerous robotics simulations in harsh and extreme conditions and further validated in various real-world scenarios.
MRS Drone: A Modular Platform for Real-World Deployment of Aerial Multi-Robot Systems
This paper presents a modular autonomous Unmanned Aerial Vehicle (UAV) platform called the Multi-robot System (MRS) Drone that can be used in a large range of indoor and outdoor applications. The MRS Drone features unique modularity changes in actuators, frames, and sensory configuration. As the name suggests, the platform is specially tailored for deployment within a MRS group. The MRS Drone contributes to the state-of-the-art of UAV platforms by allowing smooth real-world deployment of multiple aerial robots, as well as by outperforming other platforms with its modularity. For real-world multi-robot deployment in various applications, the platform is easy to both assemble and modify. Moreover, it is accompanied by a realistic simulator to enable safe pre-flight testing and a smooth transition to complex real-world experiments. In this manuscript, we present mechanical and electrical designs, software architecture, and technical specifications to build a fully autonomous multi UAV system. Finally, we demonstrate the full capabilities and the unique modularity of the MRS Drone in various real-world applications that required a diverse range of platform configurations.
Controlling a Swarm of Unmanned Aerial Vehicles Using Full-Body k-Nearest Neighbor Based Action Classifier
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.
MRS Modular UAV Hardware Platforms for Supporting Research in Real-World Outdoor and Indoor Environments
Side-Pull Maneuver: A Novel Control Strategy for Dragging a Cable-Tethered Load of Unknown Weight Using a UAV
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.
A Multi-Layer Software Architecture for Aerial Cognitive Multi-Robot Systems in Power Line Inspection Tasks
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
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
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
Embedded Fast Nonlinear Model Predictive Control for Micro Aerial Vehicles
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.
Multi-Robot Sensor Fusion Target Tracking with Observation Constraints
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.
Safe Documentation of Historical Monuments by an Autonomous Unmanned Aerial Vehicle
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
—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
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.
Fast Nonlinear Model Predictive Control for Very-Small Aerial Vehicles
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
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.
UAV Vision-Based Nonlinear Formation Control Applied to Inspection of Electrical Power Lines
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.
Position and attitude control of multi-rotor aerial vehicles: A survey
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.