AIN‐Based Action Selection Mechanism for Soccer Robot Systems

Abstract

Role and action selections are two major procedures of the game strategy for multiple robots playing the soccer game. 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 role‐select procedure was often designed efficiently by using the geometry approach. However, the action‐select procedure developed based on geometry approach will become a very complex task. In this paper, a novel action‐select algorithm for soccer robots is proposed by using the concepts of artificial immune network (AIN). This AIN‐based action‐select provides an efficient and robust algorithm for robot role selection. Meanwhile, a reinforcement learning mechanism is applied in the proposed algorithm to enhance the response of the adaptive immune system. Simulation and experiment are carried out to verify the proposed AIN‐based algorithm, and the results show that the proposed algorithm provides an efficient and applicable algorithm for mobile robots to play soccer game.