Behavior-based learning fuzzy rules for mobile robots

Abstract

This paper describes a method to build a learning system for mobile robots based on the fuzzy control technique. Range sensors are analyzed and used. The learning system generates a set of fuzzy rules based on the robot's behaviors. Two examples illustrate the learning capabilities: 1) learn-to-avoid-obstacle, and 2) learn-to-detect-landmark behaviors. Reinforcement learning is applied to correct the fuzzy rules. Fuzzy controllers are used to control the robot using a set of fuzzy rules. The results of learn-to-avoid behavior indicate a good learning performance in dynamic environments. The results of learn-to-detect-landmark behavior show the robot can detect landmarks correctly from 68.43% to 90.09%.