Aiming at the problems of traditional particle swarm optimization(PSO)algorithm in path planning in complex environments

such as low search efficiency

a tendency to fall into local optimum and poor dynamic obstacle avoidance effect

a new algorithm which fuses improved PSO and dynamic window approach(DWA)is proposed.Firstly

the traditional PSO algorithm is improved with parameter self-adaptation to improve the search efficiency.Secondly

the idea of the simulated annealing(SA)algorithm is introduced to prevent the algorithm from falling into local optima.At the same time

the fitness function is optimized to improve the smoothness of the path.Finally

the key nodes of the global optimal path are fused into the improved DWA algorithm to achieve dynamic obstacle avoidance.Simulation results show that compared with other traditional algorithms

the fusion algorithm generates shorter planned path

more reasonable path distribution

and more significant dynamic obstacle avoidance effect.