Coordinative behavior by genetic algorithm and fuzzy in evolutionary multi-agent system

A strategy for motion planning of multiple robots as a multi-agent system is proposed. All the robots cannot communicate globally, but some robots can communicate locally and coordinate to avoid competition for public resources. In such systems, it is difficult for each robot to plan its motion effectively, while considering other robots. Therefore, each robot determines its motion selfishly, planning its motion while considering the known environment and using empirical knowledge. The robot also considers its unknown environment, which includes the other robots, in the empirical knowledge. The genetic algorithm is used to optimize the motion of the planning. Each robot iteratively acquires knowledge of its unknown environment, expressed by fuzzy logic, and the system behaves efficiently as an evolutionary process. As an illustration, path planning by multiple mobile robots is considered