<p>This paper explores a path planning method for mobile robots based on an enhanced particle swarm optimization algorithm. Based on the standard particle swarm algorithm, several strategies including population initialization based on Sobol sequence, improved inertia weight update strategy, particle update mutation strategy, improvement for acceleration factors, and combined with brain storm optimization algorithm are introduced to enhance the particle swarm algorithm, the local convergence problem of traditional particle swarm algorithm is optimized, and the global search ability of the algorithm is improved. A fitness function is designed taking into account the shortest path, obstacle avoidance, and smoothness of the path. The dilation processing is used to handle the boundaries of obstacles, providing a safe buffer zone for mobile robots and making path planning safer and more robust. Compared and analyzed with standard particle swarm optimization algorithm, sparrow search algorithm, crested porcupine optimizer, artificial bee colony algorithm, and A* algorithm, related simulation experiments have shown that path planning performance in complex environments is superior to other methods, effectively improving the obstacle avoidance ability and efficiency of mobile robots. Compared with other algorithms, for two maps, the mean path length of the proposed algorithm decreased by 3.9177% to 20.1501% and 2.8446% to 10.1472%, respectively. The proposed strategy not only reduces path length, but also lowers the risk of collision between mobile robots and obstacles in the complex environment, providing effective technical support for the practical application of mobile robots in various scenarios.</p>