In a small-size robot soccer game, the game strategy is implemented by two major procedures, namely, Role Selection Mechanism (RSM) and Action Select Mechanism (ASM). In role-select procedure, a formation is planned for the soccer team and a role is assigned to each individual robot. In action-select procedure, each robot executes an action provided by an action selection mechanism to fulfill its role-playing. The RSM was often designed efficiently by using the geometry approach. However, the ASM developed based on geometry approach will become a very complex procedure. In this paper, a novel ASM for soccer robots is proposed by using the concepts of artificial immune network (AIN). This AIN-based ASM provides an efficient and robust algorithm for robot role select. Meanwhile, a reinforcement learning mechanism is applied in the proposed ASM to enhance the response of the adaptive immune system. Simulation and experiment are carried out in this paper to verify the proposed AIN-based ASM and the results show that the proposed algorithm provide an efficient and applicable algorithm for mobile robots to play soccer game.
Relation:
淡江理工學刊=Tamkang Journal of Science and Engineering 11(4), pp.415-424
