Enhanced learning classifier system for robot navigation

Abstract

This paper describes an enhanced learning classifier system used to evolve obstacle-avoidance rules used in mobile robot navigation. The robot learns these rules via feedback from the environment, available as sonar readings. Conventional classifiers, when used in this application, show evidence of shortcomings: becoming trapped in local minima, loss of (desirable) rules, and favouring of generalized rules. Enhancements to the classification system are described and tested using a simulated robot and environment. The enhancements prove to be worthwhile in that they overcome the limitations, and can generally handle more complex situations.