Distributed data-driven UAV formation control via evolutionary games: Experimental results |
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| Authors: | J Barreiro-Gomez I Mas JI Giribet P Moreno C Ocampo-Martinez R Sánchez-Peña N Quijano |
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| Institution: | 1. NYUAD Research Institute, New York University Abu Dhabi, PO Box 129188, Abu Dhabi, United Arab Emirates;2. Engineering Division, New York University Abu Dhabi, Learning & Game Theory Laboratory (L&G-Lab), Saadiyat Campus PO Box 129188, United Arab Emirates;3. CONICET and Instituto Tecnológico de Buenos Aires, Av. Madero 399, Buenos Aires, Argentina;4. Instituto Argentino de Matemática - CONICET and Universidad de Buenos Aires, Paseo Colón 850, Buenos Aires, Argentina;5. Department of Automatic Control, Universitat Politècnica de Catalunya, Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens i Artigas, 4-6, 08028 Barcelona, Spain;6. School of Electrical and Electronic Engineering, Universidad de los Andes,Carrera 1A No 18A-10, Bogotá;1. School of Control and Computer Engineering, North China Electric Power University, 102206 Beijing, China;2. School of Transportation Science and Engineering, Beihang University, 100091 Beijing, China;1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, Shandong Province, 266590, China;2. School of Mathematical College, Chongqing Normal University, Chongqing 401331 China;3. Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan 38541, Republic of Korea;1. School of Artificial Intelligence, Shenyang University of Technology, Shenyang, China;2. College of Engineering, Qufu Normal University, Rizhao, Shandong, China;1. School of Information, Beijing Wuzi University, Beijing 101149, PR China;2. School of Automation, Beijing Institute of Technology, Beijing 100081, PR China |
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| Abstract: | This work proposes a novel data-driven distributed formation-control approach based on multi-population evolutionary games, which is structured in a leader-follower scheme. The methodology considers a time-varying communication graph that describes how the multiple agents share information to each other. We present stability guarantees for configurations given by time-varying interaction networks, making the proposed method suitable for real-world problems where communication constraints change along the time. Additionally, the proposed formation controller allows for an agent to leave or enter the group without the need to modify the behaviors of other agents in the group. This game-theoretical approach is evaluated through numerical simulations and real outdoors experimental results using a fleet of aerial autonomous vehicles, showing the control performance. |
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