Abstract: Aiming at the path planning problem of mobile robots, an improved sparrow search algorithm combining multiple strategies is proposed. Firstly, the population initialization introduces improved Tent chaotic mapping and reverse learning to make the population distribution more uniform and diverse. Secondly, in order to enhance the algorithm's ability to escape from local optimal solutions, the Levy flight strategy and the osprey optimization algorithm guide the population position update mechanism, improving the search efficiency and global search capability of the algorithm. Finally, a new solution is generated through adaptive variation perturbation, and inferior solutions are accepted by incorporating the randomness advantage of the simulated annealing algorithm, thereby improving the global search ability. This approach compensates for the shortcomings of the sparrow search algorithm and enhances the search performance of the algorithm. To verify the effectiveness of the improved algorithm, the standard test function is used to analyze and compare the algorithm performance, demonstrating that the performance of the improved sparrow search algorithm is significantly improved. A raster map model is established, and the improved algorithm is applied to the path planning of mobile robots. The experimental results show that the path planning length is shorter, and the search efficiency is higher.