Adaptive behavior of fuzzy system optimized by genetic algorithm

The problem of automatically adapting the behavior of a mobile robot in a changing environment is recognized as a very difficult task. Towards a promising approach to this problem, we have developed a genetic fuzzy controller for a mobile robot, and showed the potential by applying to a simulated robot called Khepera. The robot gets input from eight infrared sensors and operates two motors according to the fuzzy inference based on the sensory input. This paper attempts to analyze the adaptive behaviors of the controller by using automata, which indicates the emergence of several strategies to make the robot to navigate the complex space without bumping against walls and obstacles.