Research on Path Planning for Mobile Robot Based on Improved Genetic Algorithm

Abstract

Premature and lower convergent speed is two puzzling problems in applying genetic algorithm,a genetically simulated annealing algorithm of optimum path planning for mobile robots is proposed.Changing of two-dimensional codes into one-dimensional codes is adopted to simplify the encoding path.An initialization population was produced based on genetic algorithm,and the fitness value of each path is evaluated.An efficient temperature updating function was devised through a series crossover and mutation.And by adopting the random moving rule of Metropolis algorithm,a global optimal path was obtained from the starting point to the target point.Finally,the feasibility and efficiency of this algorithm are verified in the Matlab environmen.The simulation results demonstrate that the proposed algorithm has achieved considerable improvements in convergence speed,search quality and the best path compared to the basic genetic algorithm.