Vít Krátký

In this letter, we present a distributed algorithm for flocking in complex environments that operates at constant altitude, without explicit communication, no a priori information about the environment, and by using only on-board sensing and computation capabilities. We provide sufficient conditions to guarantee collision avoidance with obstacles and other robots without exceeding a desired maximum distance from a predefined set of neighbors (flocking or proximity maintenance constraint) during the mission. The proposed approach allows to operate in crowded scenarios and to explicitly deal with tracking errors and on-board sensing errors. The algorithm was verified through simulations with varying number of UAVs and also through numerous real-world experiments in a dense forest involving up to four UAVs.

This paper shows how different types of novel aerial robots with new functionalities can cooperate in the inspection and maintenance (I&M) of power lines, one of the largest and most essential civil infrastructures in any country. This study relies on the results from the AERIAL-CORE research and innovation project. The paper describes an I&M validation scenario and evaluation metrics for three linked operation domains: 1) long-range inspection for the detection of possible damages on power lines in a post-storm scenario, 2) aerial manipulation for the installation of devices on power lines, and 3) aerial co-working to help human operators in their activities at height. It presents the demonstration of ten different aerial robots in a real scenario with 10 km of power lines. The platforms include morphing-wing and VTOL (vertical take-off and landing) UAVs (unmanned aerial vehicles), multi-rotors, and aerial manipulators. These platforms, custom-developed or commercially available, are evaluated in the three application domains, describing the new functionalities implemented for each case. The paper ends with guidelines, design principles, and lessons learned for future developments derived from the final demonstration of the project.

This letter presents a formation control approach for contactless gesture-based Human-Swarm Interaction (HSI) between a team of multi-rotor Uncrewed Aerial Vehicles (UAVs) and a human worker. The approach is designed to monitor the safety of human workers, particularly those operating at heights. In the proposed dynamic formation scheme, one UAV acts as the formation leader, equipped with sensors for detecting human workers and recognizing gestures. The follower UAVs maintain a predetermined formation relative to the worker's position, providing additional perspectives of the monitored scene. Hand gestures enable the human worker to specify movement and action commands for the UAV team and to initiate other mission-related tasks without requiring additional communication channels or specific markers. Combined with a novel unified human detection and tracking algorithm, a human position estimation method, and a gesture detection pipeline, the proposed approach represents the first instance of an HSI system incorporating all these modules onboard real-world UAVs. Simulations and field experiments involving three UAVs and a human worker in a mock-up scenario demonstrate the effectiveness and responsiveness of the proposed approach.

In this paper, we introduce an algorithm designed to address the problem of time-optimal formation reshaping in three-dimensional environments while preventing collisions between agents. The utility of the proposed approach is particularly evident in mobile robotics, where agents benefit from being organized and navigated in formation for a variety of real-world applications requiring frequent alterations in formation shape for efficient navigation or task completion. Given the constrained operational time inherent to battery-powered mobile robots, the time needed to complete the formation reshaping process is crucial for their efficient operation, especially in case of multi-rotor Unmanned Aerial Vehicles (UAVs). The proposed Collision-Aware Time-Optimal formation Reshaping Algorithm (CAT-ORA) builds upon the Hungarian algorithm for the solution of the robot-to-goal assignment implementing the inter-agent collision avoidance through direct constraints on mutually exclusive robot-goal pairs combined with a trajectory generation approach minimizing the duration of the reshaping process. Theoretical validations confirm the optimality of CAT-ORA, with its efficacy further showcased through simulations, and a real-world outdoor experiment involving 19 UAVs. Thorough numerical analysis shows the potential of CAT-ORA to decrease the time required to perform complex formation reshaping tasks by up to 49%, and 12% on average compared to commonly used methods in randomly generated scenarios.

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 streams 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.

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 letter. 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.

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

In recent decades, robotic systems have been used for an increasing number of applications, often involving multiple robots. Although multi-robot systems (MRS) provide benefits like redundancy, robustness, and fault tolerance, they come with numerous challenges. These challenges present new research possibilities and are of great interest to the IEEE Robotics and Automation Society (RAS). The IEEE RAS Summer School on MRS aims to gather the knowledge and expertise of the robotics community and bridge the gap between theory and practice.

Digital documentation of large interiors of historical buildings is an exhausting task as most of the areas of interest are beyond typical human reach. We advocate the use of fully autonomous teams of cooperating multirotor 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. In particular, we present a universal autonomy for unmanned aerial vehicles (UAVs) cooperating aerially within a team while documenting the interiors of historical buildings for the purposes of restoration planning and documentation works as well as for assessing the structural state of aging historical sites. We show that the proposed approach of active multirobot cooperation enables performing documentation tasks requiring dynamic scene illumination in large-scale real-world scenarios, a process previously applicable only manually in areas easily accessible by humans.