Path Planning of Mobile Robot Based On Hybrid Cascaded Genetic Algorithm

We use an improved hybrid cascade genetic algorithm for robot path planning in this paper. The map environment was modeling with grid method in this study. Simulated annealing algorithm considers that species in the evolutionary process may have a partial regression, the results do not require evolution has been increasing. Simulated annealing algorithm makes evolutionary search process to avoid falling into the local optimal solution. Genetic algorithms always assume that the optimal solution is close to the problem of local optimal solution, GA shrinking the scope of the solution space to achieve fast convergence. For slow convergence of simulated annealing and poor local search of genetic algorithms, simulated annealing algorithm and genetic algorithm hybrid, can overcome their shortcomings, The Simulation results demonstrate the improved hybrid genetic algorithm has higher convergence rate, the probability of the optimal solution accuracy has been significantly improved, and a strong map adaptability, compared with the traditional genetic algorithm.