MOBILE ROBOT NAVIGATION BASED ON IMPROVED GENETIC ALGORITHM AND FUZZY LOGICAL CONTROL

Abstract

In this paper we propose a learning mechanism for mobile robot navigation. The robot is controlled by a dynamic set of fuzzy rules and the rule set is learned using genetic algorithm. We use messy genetic algorithm to reduce the size and complexity of chromosome and niche genetic algorithm to increase the learning speed. We also take the kinematics model of wheeled mobile robot into account and use the speed of wheels directly as the output of fuzzy rules. After the rules have been learnt in simulated environment, they are tested in the globe vision system designed by us. Experimental results prove the learning mechanism is correct and feasible.