USV cluster collision avoidance based on particle swarm optimization algorithm

被引：3
|
作者：
Lian Q. [1 ]
Wang H. [1 ]
Yuan J. [1 ]
Gao N. [1 ]
Hu W. [1 ]
机构：
[1] College of Automation, Harbin Engineering University, Harbin
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2019年 / 41卷 / 09期
关键词：
Collaborative collision avoidance; Particle swarm optimization algorithm; Rolling optimization strategy; Sight model; Unmanned surface vessel (USV) cluster;
D O I：
10.3969/j.issn.1001-506X.2019.09.16
中图分类号：
学科分类号：
摘要：
To deal with the collaborative collision avoidance problem in path planning for unmanned surface vessel (USV) clusters, a USV cluster coordination collision avoidance method based on the rolling optimization strategy and the particle swarm optimization algorithm is proposed. Firstly, a comprehensive field of the view model is established by existing sensor parameters such as radar and photoelectric. Secondly, the standard particle swarm optimization algorithm is easy to fall into the local optimal problem. The inertia weight adjustment based on the tangent function is combined with the linear adjustment learning factor to improve the global search ability of the particle swarm optimization algorithm. At the same time, the transition angle control strategy is added to the selection of the fitness function to improve the smoothness of the path. Finally, the improved particle swarm optimization algorithm is used to plan the path in each integrated view. The simulation results show that the optimization algorithm can realize real-time collision avoidance of USV clusters, and quickly plan a smooth and secure global optimal path for USV clusters. © 2019, Editorial Office of Systems Engineering and Electronics. All right reserved.
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收藏
页码：2034 / 2040
页数：6
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