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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.

This paper introduces a system designed for tight collaboration between Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) in harsh maritime conditions characterized by large waves. This onboard UAV system aims to enhance collaboration with USVs for following and landing tasks under such challenging conditions. The main contribution of our system is the novel mathematical USV model, describing the movement of the USV in 6 degrees of freedom on a wavy water surface, which is used to estimate and predict USV states. The estimator fuses data from multiple global and onboard sensors, ensuring accurate USV state estimation. The predictor computes future USV states using the novel mathematical USV model and the last estimated states. The estimated and predicted USV states are forwarded into a trajectory planner that generates a UAV trajectory for following the USV or landing on its deck, even in harsh environmental conditions. The proposed approach was verified in numerous simulations and deployed to the real world, where the UAV was able to follow the USV and land on its deck repeatedly.

This paper introduces an innovative methodology for object manipulation on the surface of water through the collaboration of an Unmanned Aerial Vehicle (UAV) and an Unmanned Surface Vehicle (USV) connected to the object by tethers. We propose a novel mathematical model of a robotic system that combines the UAV, USV, and the tethered floating object. A novel Model Predictive Control (MPC) framework is designed for using this model to achieve precise control and guidance for this collaborative robotic system. Extensive simulations in the realistic robotic simulator Gazebo demonstrate the system's readiness for real-world deployment, highlighting its versatility and effectiveness. Our multi-robot system overcomes the state-of-the-art single-robot approach, exhibiting smaller control errors during the tracking of the floating object's reference. Additionally, our multi-robot system demonstrates a shorter recovery time from a disturbance compared to the single-robot approach.

A unique approach for the mid-air autonomous aerial interception of non-cooperating UAV by a flying robot equipped with a net is presented in this paper. A novel interception guidance method dubbed EPN is proposed, designed to catch agile maneuvering targets while relying on onboard state estimation and tracking. The proposed method is compared with state-of-the-art approaches in simulations using 100 different trajectories of the target with varying complexity comprising almost 14 hours of flight data, and EPN demonstrates the shortest response time and the highest number of interceptions, which are key parameters of agile interception. To enable robust transfer from theory and simulation to a real-world implementation, we aim to avoid overfitting to specific assumptions about the target, and to tackle interception of a target following an unknown general trajectory. Furthermore, we identify several often overlooked problems related to tracking and estimation of the target's state that can have a significant influence on the overall performance of the system. We propose the use of a novel state estimation filter based on the IMM filter and a new measurement model. Simulated experiments show that the proposed solution provides significant improvements in estimation accuracy over the commonly employed KF approaches when considering general trajectories. Based on these results, we employ the proposed filtering and guidance methods to implement a complete autonomous interception system, which is thoroughly evaluated in realistic simulations and tested in real-world experiments with a maneuvering target going far beyond the performance of any state-of-the-art solution.

A decentralized swarm approach for the fast cooperative flight of Unmanned Aerial Vehicles (UAVs) in feature-poor environments without any external localization and communication is introduced in this paper. A novel model of a UAV neighborhood is proposed to achieve robust onboard mutual perception and flocking state feedback control, which is designed to decrease the inter-agent oscillations common in standard reactive swarm models employed in fast collective motion. The novel swarming methodology is supplemented with an enhanced Multi-Robot State Estimation (MRSE) strategy to increase the reliability of the purely onboard localization, which may be unreliable in real environments. Although MRSE and the neighborhood model may rely on information exchange between agents, we introduce a communication-less version of the swarming framework based on estimating communicated states to decrease dependence on the often unreliable communication networks of large swarms. The proposed solution has been verified by a set of complex real-world experiments to demonstrate its overall capability in different conditions, including a UAV interception-motivated task with a group velocity reaching the physical limits of the individual hardware platforms., Comment: Accepted to IEEE RA-L on March 22, 2024

This article presents Persistence Administered Collective Navigation (PACNav) as an approach for achieving decentralized collective navigation of Unmanned Aerial Vehicle (UAV) swarms. The technique is inspired by the flocking and collective navigation behavior observed in natural swarms, such as cattle herds, bird flocks, and even large groups of humans. PACNav relies solely 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, which allow each swarm member to analyze the motion of others. PACNav is grounded on two main principles: (1) UAVs with little variation in motion direction exhibit high path persistence and are considered reliable leaders by other UAVs; (2) groups of UAVs that move in a similar direction demonstrate high path similarity, and such groups are assumed to contain a reliable leader. The proposed approach also incorporates a reactive collision avoidance mechanism to prevent collisions with swarm members and environmental obstacles. The method is validated through simulated and real-world experiments conducted in a natural forest., Comment: 2 pages, Accepted for discussion at the workshop session "Breaking Swarm Stereotypes" at ICRA'24 in Yokohama, Japan

This paper presents a modular autonomous Unmanned Aerial Vehicle (UAV) platform called the Multi-robot Systems (MRS) Drone that can be used in a large range of indoor and outdoor applications. The MRS Drone features unique modularity with respect to 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., Comment: 49 pages, 39 figures, accepted for publication to the Journal of Intelligent & Robotic Systems

A new robust and accurate approach for the detection and localization of flying objects with the purpose of highly dynamic aerial interception and agile multi-robot interaction is presented in this paper. The approach is proposed for use onboard an autonomous aerial vehicle equipped with a 3D LiDAR sensor providing input data for the algorithm. It relies on a novel 3D occupancy voxel mapping method for the target detection and a cluster-based multiple hypothesis tracker to compensate uncertainty of the sensory data. When compared to state-of-the-art methods of onboard detection of other flying objects, the presented approach provides superior localization accuracy and robustness to different environments and appearance changes of the target, as well as a greater detection range. Furthermore, in combination with the proposed multi-target tracker, sporadic false positives are suppressed, state estimation of the target is provided and the detection latency is negligible. This makes the detector suitable for tasks of agile multi-robot interaction, such as autonomous aerial interception or formation control where precise, robust, and fast relative localization of other robots is crucial. We demonstrate the practical usability and performance of the system in simulated and real-world experiments., Comment: 15 pages, 13 figures

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 UVDAR 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, requires 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 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 at https://github.com/ctu-mrs.

This paper 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 meters on a single battery in a demanding unknown cave environment.

Digital documentation of large interiors of historical buildings is an exhausting task since most of the areas of interest are beyond typical human reach. We advocate the use of autonomous teams of multi-rotor Unmanned Aerial Vehicles (UAVs) to speed up the documentation process by several orders of magnitude while allowing for a repeatable, accurate, and condition-independent solution capable of precise collision-free operation at great heights. The proposed multi-robot approach allows for performing tasks requiring dynamic scene illumination in large-scale real-world scenarios, a process previously applicable only in small-scale laboratory-like conditions. Extensive experimental analyses range from single-UAV imaging to specialized lighting techniques requiring accurate coordination of multiple UAVs. The system's robustness is demonstrated in more than two hundred autonomous flights in fifteen historical monuments requiring superior safety while lacking access to external localization. This unique experimental campaign, cooperated with restorers and conservators, brought numerous lessons transferable to other safety-critical robotic missions in documentation and inspection tasks., Comment: Published as Early Access on February 28, 2023. Supported by multimedia available at http://mrs.felk.cvut.cz/ram2022dronument

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., Comment: 17 pages, 16 figures, journal paper

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., Comment: 10 pages, 17 figures, conference

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., Comment: 8 pages, 12 figures, journal paper

This paper presents a novel approach for autonomous cooperating UAVs in search and rescue operations in subterranean domains with complex topology. The proposed system was ranked second in the Virtual Track of the DARPA SubT Finals as part of the team CTU-CRAS-NORLAB. In contrast to the winning solution that was developed specifically for the Virtual Track, the proposed solution also proved to be a robust system for deployment onboard physical UAVs flying in the extremely harsh and confined environment of the real-world competition. The proposed approach enables fully autonomous and decentralized deployment of a UAV team with seamless simulation-to-world transfer, and proves its advantage over less mobile UGV teams in the flyable space of diverse environments. The main contributions of the paper are present in the mapping and navigation pipelines. The mapping approach employs novel map representations -- SphereMap for efficient risk-aware long-distance planning, FacetMap for surface coverage, and the compressed topological-volumetric LTVMap for allowing multi-robot cooperation under low-bandwidth communication. These representations are used in navigation together with novel methods for visibility-constrained informed search in a general 3D environment with no assumptions about the environment structure, while balancing deep exploration with sensor-coverage exploitation. The proposed solution also includes a visual-perception pipeline for on-board detection and localization of objects of interest in four RGB stream at 5 Hz each without a dedicated GPU. Apart from participation in the DARPA SubT, the performance of the UAV system is supported by extensive experimental verification in diverse environments with both qualitative and quantitative evaluation., Comment: Submitted to Field Robotics Special Issue: DARPA Subterranean Challenge, Advancement and Lessons Learned from the Finals

We present a field report of CTU-CRAS-NORLAB team from the Subterranean Challenge (SubT) organised by the Defense Advanced Research Projects Agency (DARPA). The contest seeks to advance technologies that would improve the safety and efficiency of search-and-rescue operations in GPS-denied environments. During the contest rounds, teams of mobile robots have to find specific objects while operating in environments with limited radio communication, e.g. mining tunnels, underground stations or natural caverns. We present a heterogeneous exploration robotic system of the CTU-CRAS-NORLAB team, which achieved the third rank at the SubT Tunnel and Urban Circuit rounds and surpassed the performance of all other non-DARPA-funded teams. The field report describes the team's hardware, sensors, algorithms and strategies, and discusses the lessons learned by participating at the DARPA SubT contest., Comment: This paper have already been accepted to be published Filed Robotics special issue about DARPA SubT challange

This paper presents a system for autonomous cooperative wall building with a team of Unmanned Aerial Vehicles (UAVs). The system was developed for Challenge 2 of the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. The wall-building scenario of Challenge 2 featured an initial stack of bricks and wall structure where the individual bricks had to be placed by a team of three UAVs. The objective of the task was to maximize collected points for placing the bricks within the restricted construction time while following the prescribed wall pattern. The proposed approach uses initial scanning to find a priori unknown locations of the bricks and the wall structure. Each UAV is then assigned to individual bricks and wall placing locations and further perform grasping and placement using onboard resources only. The developed system consists of methods for scanning a given area, RGB-D detection of bricks and wall placement locations, precise grasping and placing of bricks, and coordination of multiple UAVs. The paper describes the overall system, individual components, experimental verification in demanding outdoor conditions, the achieved results in the competition, and lessons learned. The presented CTU-UPenn-NYU approach achieved the overall best performance among all participants to won the MBZIRC competition by collecting the highest number of points by correct placement of a high number of bricks., Comment: Submitted to Robotics and Autonomous Systems

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 x 256 px @ 55 um), 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 x 1.2 deg and operates in the energy range of 3 - 60 keV. The second telescope was a one-dimensional LE with a focal length of 250 mm and a FOV of 2.7 x 8.0 deg for the energy range 3 - 40 keV. The X-ray telescopes were supplemented by a camera in the visible spectrum with 1,280 x 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., Comment: Submitted to Acta Astronautica

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., Comment: Accepted to 2021 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE

We present a multirotor Unmanned Aerial Vehicle control (UAV) 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 shaped the system into the form presented here. In addition, the system was utilized during the participation of our team from the CTU in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions, and also in the DARPA SubT challenge. Each time, our team was able to secure top places among the best competitors from all over the world. On each occasion, the challenges has motivated the team to improve the system and to gain a great amount of high-quality experience within tight deadlines., Comment: 28 pages, 20 figures, accepted to Journal of Intelligent & Robotic Systems (JINT), for the provided open-source software see http://github.com/ctu-mrs, erratum for eq. 3, 15, 19, 24