Aiming at the problems of low search efficiency of human learning optimization algorithm, easy to fall into local optimum, and unable to achieve dynamic obstacle avoidance, a path planning algorithm integrating improved human learning optimization algorithm and dynamic window algorithm was proposed. Firstly, the nonlinear increasing and decreasing probability parameters are used to improve the convergence rate of HLO. Introduction of particle swarm algorithms to update personal and social knowledge databases. The inertia weight coefficients are adjusted adaptively to avoid falling into the local optimum. weights to adjust the speed and angle; finally, the improved algorithm was applied to the path planning of the automated guided vehicle, and simulation experiments show that the fusion algorithm plans path lengths that by 4% less than the ant colony algorithm paths, and by 15% less than the hybrid human learning optimization and particle swarm algorithms, and that the other two algorithms make contact with the obstacles by five times as many times as the improved algorithm, which reduces the length of the path and number of transitions and improves Smoothness of the path. It avoids obstacles in T-shaped and complex map environments to verify the feasibility of the proposed algorithm.