Learning Fuzzy Reactive Behaviors in Autonomous Robots

Abstract

This paper is concerned with the learning of basic behaviors in autonomous robots. In this way, we present a method for the adaptation of basic reactive behaviors implemented as fuzzy controllers applying a genetic algorithm to the evolution of the fuzzy rule system. In this sense, we show our experiments in the evolution of control rules based on symbolic concepts represented as linguistic labels. The rules will be formulated in a fuzzy way and in order to test the rules obtained in each generation of the genetic algorithm a real robot has been used. The individual with the best performance is chosen to generate a new population: the elite strategy. All the new individuals were tested in the same real environment. In conclusion, the individuals of the last generation offer a set of rules that provides better performance than the ones designed by a non-expert designer.