Improved genetic algorithms based optimum path planning for mobile robot

Abstract

Improved genetic algorithms incorporate other techniques, methods or algorithms to optimize the performance of genetic algorithm. In this paper, improved genetic algorithms of optimum path planning for mobile robot navigation are proposed. An Obstacle Avoidance Algorithm (OAA) and a Distinguish Algorithm (DA) are introduced to generate the initial population in order to improve the path planning efficiency to select only the feasible paths during the evolution of genetic algorithm. Domain heuristic knowledge based crossover, mutation, refinement and deletion operators are specifically designed to fit path planning for mobile robots. Proposed genetic algorithms feature unique, simple path representations, and simple but effective evaluation methods. Simulation studies and real time implementations are carried out to verify and validate the effectiveness of the proposed algorithms.