Optimal formation of robots by convex hull and particle swarm optimization

Jun Liu, Hongbin Ma*, Xuemei Ren, Mengyin Fu

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

9 引用 (Scopus)

摘要

Formation control problem has been extensively investigated in the literature of multi-agent systems, robotics, and control, etc. Our previous work mainly concentrated on the theoretic study of line formation with three robots, however, it is hard to handle with the formation problem whose number of robots is strictly larger than 3. In order to effectively overcome this problem, this paper incorporates convex hull and the standard PSO algorithm to design the typical formation of several robots. Firstly, on the basis of convex hull of robots, objective function, corresponding to several constraints, is given by the new convex hull method. Secondly, the standard PSO algorithm is adopted to search for the desired positions of several robots to minimize the objective function and satisfy the formation constraints. To demonstrate the effectiveness of the proposed algorithm, numerical results, regarding the formation of several ships in the realistic ocean, mainly concentrate on triangle formation, diamond formation and regular polygon formation.

源语言英语
主期刊名Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
104-111
页数8
DOI
出版状态已出版 - 2013
活动2013 3rd IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 - Singapore, 新加坡
期限: 16 4月 201319 4月 2013

出版系列

姓名Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013

会议

会议2013 3rd IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
国家/地区新加坡
Singapore
时期16/04/1319/04/13

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