一种多移动机器人协作围捕策略

提出一种在连续未知环境中实现多移动机器人协作围捕移动目标的整体方案.围捕包括包围目标和靠近目标,包围目标行为由强化学习算法实现.用状态聚类减小状态空间,利用Q学习算法获得Q值表,根据学习后的Q值表选择动作.对各种行为的输出进行加权求和获得综合行为,实现对移动目标的围捕.仿真实验获得了在不同条件下的围捕结果.结果表明,环境、hunter与prey的速度关系以及prey的逃跑策略对围捕效果都有影响.

Strategy of Cooperative Hunting by Multiple Mobile Robots

A general scheme of cooperative hunting for a moving target by multiple mobile robots in continuous unknown environments is presented. Hunting consists of encircling the target and closing to it, and the encircling behavior is realized with reinforcement learning algorithm. States are clustered in order to reduce the state space, Q learning algorithm is used to get the table of Q values, then the available action is selected according to the Q value table. Hunting of mobile target is realized with synthesized behavior, obtained by summarizing the outputs of all behaviors weighted. Hunting effects in different conditions are verified by simulation, and the results show that environments, velocity relationships between hunter and prey, and the escaping strategies of prey all have their effects on the result.