caa a22 4500 605514828 CHVBK 20210128100706.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s10458-014-9272-2 doi (NATIONALLICENCE)springer-10.1007/s10458-014-9272-2 Flocking of partially-informed multi-agent systems avoiding obstacles with arbitrary shape [Elektronische Daten] [Jiaojie Li, Wei Zhang, Housheng Su, Yupu Yang] In this paper, we study the flocking problem of multi-agent systems with obstacle avoidance, in the situation when only a fraction of the agents have information on the obstacles. Obstacles of arbitrary shape are allowed, no matter if their boundary is smooth or non-smooth, and no matter it they are convex or non-convex. A novel geometry representation rule is proposed to transfer obstacles to a dense obstacle-agents lattice structure. Non-convex regions of the obstacles are detected and supplemented using a geometric rule. The uninformed agents can detect a section of the obstacles boundary using only a range position sensor. We prove that with the proposed protocol, uninformed agents which maintain a joint path with any informed agent can avoid obstacles that move uniformly and assemble around a point along with the informed agents. Eventually all the assembled agents reach consensus on their velocity. In the entire flocking process, no distinct pair of agents collide with each other, nor collide with obstacles. The assembled agents are guaranteed not to be lost in any non-convex region of the obstacles within a distance constraint. Numerical simulations demonstrate the flocking algorithm with obstacle avoidance both in 2D and 3D space. The situation when every agent is informed is considered as a special case. The Author(s), 2014 Obstacle avoidance nationallicence Geometry representation nationallicence Flocking nationallicence Distributed control nationallicence Multi-agent system nationallicence Li Jiaojie Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240, Shanghai, China aut Zhang Wei Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240, Shanghai, China aut Su Housheng School of Automation, Key Laboratory of Image Information Processing and Intelligent Control (Huazhong University of Science and Technology), Ministry of Education, National Key Laboratory of Science and Technology on Multispectral Information Processing, Huazhong University of Science and Technology, Luoyu Road 1037, 430074, Wuhan, China aut Yang Yupu Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240, Shanghai, China aut Autonomous Agents and Multi-Agent Systems Springer US; http://www.springer-ny.com 29/5(2015-09-01), 943-972 1387-2532 29:5<943 2015 29 10458 https://doi.org/10.1007/s10458-014-9272-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10458-014-9272-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Li Jiaojie Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240, Shanghai, China aut NATIONALLICENCE 700 1- Zhang Wei Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240, Shanghai, China aut NATIONALLICENCE 700 1- Su Housheng School of Automation, Key Laboratory of Image Information Processing and Intelligent Control (Huazhong University of Science and Technology), Ministry of Education, National Key Laboratory of Science and Technology on Multispectral Information Processing, Huazhong University of Science and Technology, Luoyu Road 1037, 430074, Wuhan, China aut NATIONALLICENCE 700 1- Yang Yupu Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240, Shanghai, China aut NATIONALLICENCE 773 0- Autonomous Agents and Multi-Agent Systems Springer US; http://www.springer-ny.com 29/5(2015-09-01), 943-972 1387-2532 29:5<943 2015 29 10458