An Improved Genetic Algorithm of Optimum Path Planning for Mobile Robots

Abstract

An improved genetic algorithm of optimum path planning for mobile robots is proposed in this paper. An obstacle avoidance algorithm is introduced to generate the initial population in order to improve the path planning efficiency. Domain heuristic knowledge based crossover, mutation, refinement and deletion operators are specifically designed to fit path planning for mobile robots. Furthermore, a fuzzy logic control algorithm is integrated to self-adaptively adjust the probabilities of crossover and mutation in the genetic algorithm. Simulation studies for both static and dynamic environments are carried out, and the simulation results show that the proposed genetic algorithm exhibits improved search speed, high search quality and enhanced self adaptability