Mobile robot learning by evolution of fuzzy controller

Abstract

In this paper we describe an evolutionary fuzzy system to control a mobile robot effectively, and apply it to the simulated mobile robot called Khepera. The system gets input from eight infrared sensors and operates two motors according to fuzzy inference based on the sensory input. In order to robustly determine the shape and number of membership functions in fuzzy rules, genetic algorithm has been utilized. This approach reduces the burden of human operators to decide the structure of fuzzy rules. With the simulation of Khepera robot, we confirm that the evolutionary approach might find out a set of optimal fuzzy rules which make the robot to reach the goal point, as well as to solve autonomously several subproblems such as obstacle avoidance and passing-by narrow corridors.