目标围捕任务中搜索与预包围阶段的群机器人行为学习

doi:10.16451/j.cnki.issn1003-6059.201804008

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Abstract A strategy for navigation-type collective behaviour learning is developed for swarm robotic coordination in a target search task. Sub-swarms are formed by utilizing the method of dynamic self-organizing task allocation with closed-loop adjusting function, and then a social learning particle swarm optimization based robotic learning strategy is introduced into sub-swarms. In the sub-swarm, all robots are sorted in descending order by the cognition ability of each robot to its common desired target. The robots with better perception of the target are regarded as the behaviour demonstrators. Then, one of the behaviour demonstrators is selected randomly by each robot to learn in every dimension of the working space. Thus, the learning behaviour vector of each robot can be constructed for decision making on its future moving behaviour. The results show that the robot can coordinate with each other and the search efficiency is improved without the global social experience learning.

Key words： Swarm Robotics Particle Swarm Optimization Coordinated Control Swarm Intelligence Target Search

Received: 18 January 2018

ZTFLH:

TP 242.6

Fund:Supported by National Natural Science Foundation of China(No.61472269), Research Project of Shanxi Scholarship Council of China(No.2016-091)

Corresponding Authors: XUE Songdong(Corresponding author), Ph.D., associate professor. His research interests include swarm robotic coordinated control.

About author:: ZHANG Yunzheng, Ph.D. candidate. His research interests include intelligent computing and swarm robotic coordinated control;ZENG Jianchao, Ph.D., professor. His research interests include intelligent computing and swarm robotic coordinated control.

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	XUE Songdong
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Cite this article:

XUE Songdong,ZHANG Yunzheng,ZENG Jianchao. Swarm Robotic Behaviour Learning in Search and Pre-Surround Stage for Targets Trapping Task[J]. , 2018, 31(4): 370-378.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201804008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I4/370

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