Multi Robots Cooperative Based on Action Selection Level
基于动作选择级的多机器人协作

In a multi robots environment, the overlap of actions selected by each robot makes the acquisition of cooperation behaviors less efficient. In this paper an approach is proposed to determine the action selection priority level based on which the cooperative behaviors can be readily controlled. First, eight levels are defined for the action selection priority, which can be correspondingly mapped to eight subspaces of actions. Second, using the local potential field method, the action selection priority level for each robot is calculated and thus its action subspace is obtained. Then, Reinforcement learning (RL) is utilized to choose a proper action for each robot in its action subspace. Finally, the proposed method has been implemented in a soccer game and the high efficiency of the proposed scheme was verified by the result of both the computer simulation and the real experiments.