Swarm robotics |
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The research of swarms in Multi-robot Systems group aims to integrate principles and theoretical background of swarm behaviours with methodology/theory describing cooperative localization of autonomous robots and principles of self-organizing adaptation leading to a flexible stand-alone system. It will enable applicability of swarm robotics in realistic outdoor scenarios of surveillance and reconnaissance. Basically, we develop principles of a decentralized relative localization of neighboring particles that are integrated to swarm behaviors with an aim to keep reciprocal visibility between neighbors. This enables to employ swarms of Micro Aerial Vehicle (MAV) outside laboratories equipped by a precise positioning system.
Besides, a concept of adaptively evolving swarm behaviors is established to decrease relative localization uncertainty. To enable multi-robot applications, theoretical principles of determining desired shapes of MAV swarms are designed based on bio-inspired methods of artificial intelligence, namely Particle Swarm Optimization and Boids models. Finally, decentralized collective decision making mechanisms are established with a theory identifying necessary assumptions of the switching between different swarm behaviors. This research is aimed at a study of observed autonomous behaviors of MAV swarms. In addition to the development of the theory of coordinated motion of swarm members, our research also concerns development of the sensory equipment needed for real world swarm flights, and subsequent integration of the observational constraints it induces on the swarm control in use. The research conducted in this stream is closely coordinated with research of multi robot systems being also realised within Multi-robot Systems group. Our state-of-the-art approaches in research of swarm robotics and description of developed methods can be found in papers chronologically listed below. |
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Examples of research results |
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Decentralized Swarms of Unmanned Aerial Vehicles for Search and Rescue Operations without Explicit Communication We introduced a novel approach for decentralized swarm navigation in the direction of a candidate object of interest (OOI) based on real-time detections from onboard RGB cameras. A novel self-adaptive communication strategy secures an efficient change of swarm azimuth to a higher priority direction based on the real-time detections. We introduced a local visual communication channel that establishes a network connection between neighboring agents without explicit communication to achieve high reliability and scalability of the system. As a case study, this novel method is applied for the deployment of a UAV swarm towards detected OOI for closer inspection and verification. The results of simulations and real-world experiments have verified the intended behavior of the swarm system for the detection of true positive and false positive OOI, as well as for cooperative environment exploration.
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Swarming in forest |
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Swarming of Unmanned Aerial Vehicles by Sharing Distributed Observations of Workspace To achieve robust mutual relative localization of agents in an obstacle-rich environment such a forest, we proposed a decentralized localization approach based on a comparison of the workspace observation by onboard sensors of cooperating UAVs. UAVs share sparse local obstacle maps to estimate bearing and distance between swarm members by fitting spacialy and time-distributed scans. Moreover, we introduced fully decentralized flocking control rules adapted for deployment in such demanding conditions of real forests.
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Fully autonomous UAV swarm in a forest without GPS or communication. We leveraged LIDAR-based slam, in conjunction with our specialized relative localization sensor UVDAR to perform a de-centralized, communication-free swarm flight without the units knowing their absolute locations. The swarming and obstacle avoidance control is based on a modified Boids-like algorithm. This video is to our knowledge first demonstration of such swarm operating in obstacle-cluttered environment outside of laboratory conditions.
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Autonomous Aerial Swarming in GNSS-denied Environments with High Obstacle Density The UAV agents have no global localization system and only use on-board sensors to estimate the relative position of other agents in their local reference frame. They do not communicate any information and use a bio-inspired control law to avoid collisions with surrounding obstacles and other agents, while flocking through the environment. 3D world model of the forest is publicly available at https://nasmrs.felk.cvut.cz/index.php/s/NARxc8nEzgMice6.
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PACNav: Collective Navigation of UAV Swarms without Communication and External Localization
Persistence Administered Collective Navigation (PACNav) as an approach for achieving decentralized collective navigation of Unmanned Aerial Vehicle (UAV) swarms. 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. |
Other swarm applications |
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Multi-robot state estimation The exclusive reliance on onboard sensory equipment presents challenges in the accuracy and reliability of agent self-localization when visual-based localization techniques are used. We introduced a decentralized multi-robot lateral velocity estimation method for Unmanned Aerial Vehicles (UAVs) to improve onboard measurements in case of not sufficient GNSS information. This method relies on sharing the onboard measurements of surrounding agents, as well as the estimation of the relative motion of a focal UAV within the swarm, based on observation of coworking agents. The results have shown that a swarm of UAVs using the proposed velocity estimator can stabilize individual agents when their primary onboard localization source is not reliable enough.
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Fast collective evasion
The video presents a novel approach for achieving fast evasion in self-localized swarms of unmanned aerial vehicles (UAVs) threatened by an intruding moving object. 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.
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Human-swarm interaction. The intuitive control of robot swarms becomes crucial when humans are working in close proximity with the swarm in unknown environments. We introduced 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.
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Older works |
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Bio-inspired compact swarms of MAVs without communication and external localization Our research interconnects fields of swarm robotics and computer vision, and introduced use of a vision-based method UVDAR for mutual localization in swarm systems, allowing for absolute decentralization found among biological swarm systems. The developed methodology allows us to deploy real-world aerial swarming systems with robots directly localizing each other instead of communicating their states via a communication network, which is a typical bottleneck of current state of the art systems (video featured in IEEE Robotics Video Fridays).
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Decentralized self-organizing swarming of UAVs based on extended Boids model The video demonstrates a system proposed for stabilization of a swarm of unmanned and fully autonomous helicopters, using an expanded swarming model Boids. Its main focus lies in a proposal of robust and decentralized swarming behavior suited for complex environments with high density of obstacles, and its relatively straightforward integration to a swarm of real helicopters. Corresponding constraints of multi-robot systems working in real time had to be considered.
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Selected publications:Journal articles:
Jiri Horyna, Vit Kratky, Vaclav Pritzl, Tomas Baca, Eliseo Ferrante and Martin Saska. Fast Swarming of UAVs in GNSS-denied Feature-Poor Environments without Explicit Communication. IEEE Robotics and Automation Letters 9(6):5284-5291, April 2024. PDF, DOI BibTeX Jiri Horyna, Tomas Baca, Viktor Walter, Dario Albani, Daniel Hert, Eliseo Ferrante and Martin Saska. Decentralized swarms of unmanned aerial vehicles for search and rescue operations without explicit communication. Autonomous Robots, pages 1-17, 2022. PDF BibTeX Afzal Ahmad, Daniel Bonilla Licea, Giuseppe Silano, Tomas Baca and Martin Saska. PACNav: A Collective Navigation Approach for UAV Swarms Deprived of Communication and External Localization. Bioinspiration & Biomimetics 17:1-19, November 2022. URL PDF, DOI BibTeX Dario Albani, Tiziano Manoni, Martin Saska and Eliseo Ferrante. Distributed Three Dimensional Flocking of Autonomous Drones. In 2022 International Conference on Robotics and Automation (ICRA). 2022, 6904-6911. PDF, DOI BibTeX Filip Novák, Viktor Walter, Pavel Petráček, Tomáš Báča and Martin Saska. Fast collective evasion in self-localized swarms of unmanned aerial vehicles. Bioinspiration & Biomimetics 16(6):066025, November 2021. URL PDF, DOI BibTeX Pavel Petráček, Viktor Walter, Tomáš Báča and Martin Saska. Bio-Inspired Compact Swarms of Unmanned Aerial Vehicles without Communication and External Localization. Bioinspiration & Biomimetics 16(2):026009, December 2020. PDF, DOI BibTeX M Saska, D Hert, T Baca, V Kratky and T Nascimento. Formation control of unmanned micro aerial vehicles for straitened environments. Autonomous Robots 44:991-1008, 2020. PDF, DOI BibTeX V Walter, N Staub, A Franchi and M Saska. UVDAR System for Visual Relative Localization With Application to Leader–Follower Formations of Multirotor UAVs. IEEE Robotics and Automation Letters 4(3):2637-2644, July 2019. PDF, DOI BibTeX M Saska, T Baca, J Thomas, J Chudoba, L Preucil, T Krajnik, J Faigl, G Loianno and V Kumar. System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization. Autonomous Robots 41(4):919–944, 2017. PDF BibTeX M Saska, V Vonásek, J Chudoba, J Thomas, G Loianno and V Kumar. Swarm Distribution and Deployment for Cooperative Surveillance by Micro-Aerial Vehicles. Journal of Intelligent & Robotic Systems. 84(1):469–492, 2016. PDF BibTeX M Saska, V Vonasek, T Krajnik and L Preucil. Coordination and Navigation of Heterogeneous MAV–UGV Formations Localized by a ‘hawk-eye’-like Approach Under a Model Predictive Control Scheme. International Journal of Robotics Research 33(10):1393–1412, September 2014. PDF BibTeX M Saska, T Krajnik, V Vonasek, Z Kasl, V Spurny and L Preucil. Fault-Tolerant Formation Driving Mechanism Designed for Heterogeneous MAVs-UGVs Groups. Journal of Intelligent and Robotic Systems 73(1-4):603–622, January 2014. URL PDF BibTeX M Saska, V Vonásek, J Chudoba, J Thomas, G Loianno and V Kumar. Swarm Distribution and Deployment for Cooperative Surveillance by Micro-Aerial Vehicles. Journal of Intelligent & Robotic Systems. 84(1):469–492, 2016. PDF BibTeX M Saska, T Baca, J Thomas, J Chudoba, L Preucil, T Krajnik, J Faigl, G Loianno and V Kumar. System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization. Autonomous Robots 41(4):919–944, 2017. PDF BibTeX M Saska, J S Mejia, D M Stipanovic, V Vonasek, K Schilling and L Preucil. Control and Navigation in Manoeuvres of Formations of Unmanned Mobile Vehicles. European Journal of Control 19(2):157–171, March 2013. PDF BibTeX M Saska, V Vonasek and L Preucil. Trajectory Planning and Control for Airport Snow Sweeping by Autonomous Formations of Ploughs. Journal of Intelligent and Robotic Systems 72(2):239–261, 2013. PDF BibTeX M Hess, M Saska and K Schilling. Application of Coordinated Multi Vehicle Formations for Snow Shoveling on Airports. Inteligent Service Robotics 2(4):205 – 217, 2009. PDF BibTeX WoS conference papers:
Jiri Horyna, Vit Kratky, Eliseo Ferrante and Martin Saska. Decentralized multi-robot velocity estimation for UAVs enhancing onboard camera-based velocity measurements. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2022. PDF, DOI BibTeX Martin Krizek, Jiri Horyna and Martin Saska. Swarming of Unmanned Aerial Vehicles by Sharing Distributed Observations of Workspace. In 2022 International Conference on Unmanned Aircraft Systems (ICUAS). June 2022. PDF, DOI BibTeX Akash Chaudhary, Tiago Nascimento and Martin Saska. Controlling a Swarm of Unmanned Aerial Vehicles Using Full-Body k-Nearest Neighbor Based Action Classifier. In 2022 International Conference on Unmanned Aircraft Systems (ICUAS). June 2022, 544–551. PDF, DOI BibTeX A Dmytruk, T Nascimento, A Ahmad, T Báča and M Saska. Safe Tightly-Constrained UAV Swarming in GNSS-denied Environments. In 2021 International Conference on Unmanned Aircraft Systems (ICUAS). 2021, 1391–1399. PDF, DOI BibTeX Thulio Amorim, Tiago Nascimento, Pavel Petracek, Giulia Masi, Eliseo Ferrante and Martin Saska. Self-Organized UAV Flocking Based on Proximal Control. In 2021 International Conference on Unmanned Aircraft Systems (ICUAS) (). July 2021, 1374–1382. PDF, DOI BibTeX A Ahmad, V Walter, P Petracek, M Petrlik, T Baca, D Zaitlik and M Saska. Autonomous Aerial Swarming in GNSS-denied Environments with High Obstacle Density. In 2021 IEEE International Conference on Robotics and Automation (ICRA). June 2021, 570–576. PDF, DOI BibTeX A Ahmad, V Vonásek and M Saska. Cooperative path planning for multiple MAVs operating in unknown environments. In 2020 International Conference on Unmanned Aircraft Systems (ICUAS), Athens, Greece. September 2020, 661–667. PDF, DOI BibTeX D Brandtner and M Saska. Coherent swarming of unmanned micro aerial vehicles with minimum computational and communication requirements. In ECMR. 2017. PDF BibTeX M Saska, T Báča and D Heřt. Formations of Unmanned Micro Aerial Vehicles Led by Migrating Virtual Leader. In 14th International Conference on Control, Automation, Robotics and Vision (ICARCV). 2016. PDF BibTeX M Saska. MAV-swarms: unmanned aerial vehicles stabilized along a given path using onboard relative localization. In International Conference on Unmanned Aircraft Systems (ICUAS). 2015. PDF BibTeX M Saska, J Vakula and L Preucil. Swarms of Micro Aerial Vehicles Stabilized Under a Visual Relative Localization. In ICRA2014: Proceedings of 2014 IEEE International Conference on Robotics and Automation. 2014, 3570–3575. URL PDF BibTeX M Saska, J Chudoba, L Preucil, J Thomas, G Loianno, A Tresnak, V Vonasek and V Kumar. Autonomous Deployment of Swarms of Micro-Aerial Vehicles in Cooperative Surveillance. In Proceedings of 2014 2014 International Conference on Unmanned Aircraft Systems (ICUAS) 1. 2014, 584–595. URL PDF BibTeX M Saska. MAV-swarms: unmanned aerial vehicles stabilized along a given path using onboard relative localization. In International Conference on Unmanned Aircraft Systems (ICUAS). 2015. PDF BibTeX M Saska, J Langr and L Preucil. Plume Tracking by a Self-stabilized Group of Micro Aerial Vehicles. In Modelling and Simulation for Autonomous Systems 1. 2014, 44–55. PDF BibTeX J Faigl, T Krajnik, J Chudoba, L Preucil and M Saska. Low-Cost Embedded System for Relative Localization in Robotic Swarms. In ICRA2013: Proceedings of 2013 IEEE International Conference on Robotics and Automation. 2013, 985–990. PDF BibTeX M Saska, T Baca and D Hert. Formations of Unmanned Micro Aerial Vehicles Led by Migrating Virtual Leader. In 14th International Conference on Control, Automation, Robotics and Vision (ICARCV). 2016. PDF BibTeX M Saska, Z Kasl and L Preucil. Motion Planning and Control of Formations of Micro Aerial Vehicles. In Proceedings of The 19th World Congress of the International Federation of Automatic Control. 2014, 1228–1233. PDF BibTeX M Saska, T Krajnik, V Vonasek, P Vanek and L Preucil. Navigation, Localization and Stabilization of Formations of Unmanned Aerial and Ground Vehicles. In Proceedings of 2013 International Conference on Unmanned Aircraft Systems. 2013, 831–840. URL PDF BibTeX M Saska, V Vonasek, T Krajnik and L Preucil. Coordination and Navigation of Heterogeneous UAVs-UGVs Teams Localized by a Hawk-Eye Approach. In Proceedings of 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems 1. 2012, 2166–2171. PDF BibTeX M Saska. Large sensors with adaptive shape realised by selfstabilised compact groups of micro aerial vehicles. In International Symposium on Robotic Research. 2017. PDF BibTeX V Vonasek, M Saska and L Preucil. Motion Planning for a Cable Driven Parallel Multiple Manipulator Emulating a Swarm of MAVs. In Robot Motion and Control 2013. 2013. PDF BibTeX M Saska, V Spurny and L Preucil. Trajectory Planning and Stabilization for Formations Acting in Dynamic Environments. In Progress in Artificial Intelligence. 2013. PDF BibTeX M Saska, M Macas, L Preucil and L Lhotska. Robot Path Planning using Particle Swarm Optimization of Ferguson Splines. In ETFA 2006. 2006. PDF BibTeX M Saska, V Vonasek and L Preucil. Roads Sweeping by Unmanned Multi-vehicle Formations. In IEEE International Conference on Robotics and Automation (ICRA). 2011. PDF BibTeX M Saska, V Vonasek and L Preucil. Control of ad-hoc formations for autonomous airport snow shoveling. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2010. PDF BibTeX M Saska, J S Mejia, D M Stipanovic and K Schilling. Control and Navigation of Formations of Car-Like Robots on a Receding Horizon. In IEEE Multi-conference on Systems and Control. 2009. PDF BibTeX |
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