Coordinative behavior in evolutionary multi-agent-robot system

A new strategy for motion planning of multiple robots as a multi-agent system is proposed. The system has a decentralized configuration. All the robots cannot communicate globally, but some robots can communicate locally and coordinate to avoid conflicts for public resources. In such systems, it is difficult for each robot to plan its motion effectively while considering other robots, because the robots cannot predict the motion of other robots as an unknown environment. Therefore, each robot only determines its motion selfishly while considering a known environment. In the proposed approach, each robot plans its motion while considering the known environment and using empirical knowledge. The robot considers its unknown environment including the other robots in the empirical knowledge. A genetic algorithm is applied to optimize the planning of the motion of each robot. Through iterations, each robot acquires knowledge empirically using fuzzy logic For an illustration, this paper deals with path planning of multiple mobile robots and performs simulations