Automatic generation of rules for a fuzzy robotic controller

Abstract

Fuzzy logic is a useful tool for realizing a direct mapping between perceptual situations and control commands in robotic applications that do not require internal representation or planning. It allows explicit programming and automatic learning from suitable training data to be mixed in several ways in order to produce the necessary control rules. Automatic learning enables both the reduction of the annoying and error-prone explicit programming work and the evolution in time of the controller in order to cope with dynamically changing environments. At any time an expert can verify and eventually modify the knowledge of the fuzzy system according to its personal experience. Two methods for the automatic extraction of rules from training data have been proved on a fuzzy control system for wall-following. The system is intended to grow including all the behaviors required for the safe navigation of a autonomous mobile vehicle and their arbitration. Distance measures supplied by an ultrasonic sensor ring have been chosen as sensory data. The training data have been collected during operator-driven runs of the vehicle. The same data have been used by the two methods for building rule bases that have proved effectively in driving a real AGV (a TRC Labmate base) in an indoor environment.