In order to improve the optimization ability of path planning,a hybrid path planning algorithm is proposed.Firstly,the improved Particle Swarm Optimization (PSO) algorithm is used to search the path,and then the hill climbing algorithm is used to refine the path.In the PSO algorithm,Tent chaotic mapping is used to initialize the particle population,inertia weights are randomly updated,and the learning factors are dynamically adjusted asynchronously.After the search of the improved PSO algorithm,the hill climbing algorithm is used for further optimizing.The feasibility and effectiveness of our algorithm are verified by the simulation and comparison experiments in four kinds of terrain scenarios with different complexity levels.The algorithm combines global search with local search and improves the overall search performance.