生物启发式神经网络的多机器人协作围捕研究  

Research on multi-robot cooperative roundup based on biological heuristic neural network

作　　者：陈志 邹爱成 Chen Zhi;Zou Aicheng(School of Mechanical Engineering,Guilin University of Aerospace Technology,Guilin 541004,China)

机构地区：[1]桂林航天工业学院机械工程学院,桂林541004

出　　处：《电子测量技术》2021年第10期82-90,共9页Electronic Measurement Technology

基　　金：国家自然科学基金项目(51965014);广西自然科学基金项目(2018JJA160218);广西高校中青年教师科研基础能力提升项目(2020KY21022)资助。

摘　　要：针对未知动态环境中多机器人协作围捕的时间长、成功率低的问题,提出了一种基于生物启发神经网络的新型多机器人协作围捕方法。首先,构建了多机器人协作围捕模型,利用动态联盟策略实现多机器人的联动;其次,构建基于生物启发神经网络的追踪策略,动态指导联盟所有机器人进行追踪;最后,采用编队策略实现目标的围捕。实验结果表明,所提出的方法在单目标、多目标、部分机器人故障、不同形状障碍物、不同规则环境等情况下平均捕获时间分别为12.7、22.3、34.2、17.7和28.5 s,平均捕获成功率为97.4%;与其他多机器人协作围捕算法相比,所提出的算法在捕获时间和捕获成功率上具有较大优势。Aiming at the problem of long time and low success rate of multi-robot cooperative rounding in unknown dynamic environment, a new multi-robot cooperative rounding method based on biologically inspired neural network is proposed. First, a multi-robot collaborative rounding model is built, and the dynamic alliance strategy is used to realize the linkage of multiple robots. Second, a tracking strategy based on biologically inspired neural networks is constructed to dynamically guide all robots in the alliance to track. Finally, a formation strategy is used to achieve the target rounding. The experimental results show that the average capture time of the proposed method is 12.7, 22.3, 34.2, 17.7 and 28.5 s under the conditions of single target, multiple targets, partial robot failures, obstacles of different shapes, and different regular environments. The average capture success rate is 97.4%, compared with other multi-robot cooperative hunting algorithms, the algorithm proposed has advantages in capture time and capture success rate.

关 键 词：生物启发 神经网络 动态联盟 多机器人 协作围捕 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]