基于贡献模型的多机器人多目标观测方法∗

摘要

How to reduce the overlap observation phenomena and improve the average observation rate at the same time is a complicated problem of cooperative multi-robot observation of multiple moving targets. An approach based on contribution for cooperative multi-robot observation of multiple moving targets ( C-CMOMMT) is proposed. Each robot is endowed by the C-CMOMMT algorithm with a contribution value derived from the number of assigned targets to it. Robots with low contribution receive strengthened repulsive forces from all others. Besides, the operating distances of all repulsive forces are expanded, and robots with high contribution receive weakened attractive forces from low-weighted targets. With these three methods the overlap observation phenomena are reduced. To decrease the target loss, robots with high contribution receive feeble repulsive forces, and thus the side effects become weak. Consequently, the robots are decentralized and the overlap observing phenomena are dwindled. The average observation rate, the standard deviation and entropy of the positions of mobile robots are introduced to systematically evaluate the performance and the degree of overlap observing. Results show that C-CMOMMT improves the average observation rate and dwindles the overlap observing phenomena and it is more effective than A-CMOMMT and B-CMOMMT.%如何在减少重叠观测现象的同时提高平均观测率是多机器人多目标观测的一个难题。文中提出基于贡献模型的多机器人多目标观测方法( C-CMOMMT),将机器人所观测的目标数记为贡献值,增加贡献值低的机器人所受的排斥力,扩大排斥力的作用距离,减小权重小的目标对贡献值高的机器人的吸引力,从而减少重叠观测现象。同时降低贡献值高的机器人所受到的排斥力,减轻排斥力的副作用,减少目标丢失现象,因此提高整体的平均观测率。为更系统地评价观测性能,建立由平均观测率、位置标准差和位置熵这3个因素构成的综合评价体系。仿真实验表明,相比A-CMOMMT和B-CMOMMT,C-CMOMMT可提高平均观测率,减少重叠观测现象,体现出较好的可行性和高效性。