一种基于改进动态联盟算法的多机器人协作捕猎方法

An Improved Dynamic Alliance Algorithm for Cooperative Hunting by Multi-robots

多机器人协作捕猎,不仅需要解决目标搜索、追赶和避障等基本问题,还需要设计最优任务分配机制,构建高效追捕联盟,以便快速地捕获猎物。综合考虑机器人和目标之间的各种相关属性,定义量化标准,构造辅助决策矩阵,建立动态联盟。然后将基于生物刺激神经网络的追赶策略用于联盟形成后的追捕过程中。仿真实验表明该算法具有较高的围捕效率。

To conduct the cooperative hunting by multi-robots, the robots not only need to take into account basic problems （such as searching, pursuing, and collision avoidance）, but also need to design the optimal task allocation mechanism and construct an efficient hunting alliance in order to capture the evaders quickly. Various related properties between the robots and targets are considered, and the quantitative criteria are defined. An assistant decision matrix is structured to establish the dynamic alliance. Then a pursuing strategy based on biologically inspired neural network is introduced to realize the hunting process after forming the dynamic alliance. The experimental results of simulation show the efficiency of the proposed approach.