Aiming at the problems that the human learning optimization algorithm is not efficient in searching, easy to fall into local optimum, and unable to realize dynamic obstacle avoidance, a path planning algorithm integrating improved HLO, and dynamic window is proposed. Firstly, the nonlinear increasing and decreasing probability parameters are used to improve the convergence rate of HLO, and the particle swarm algorithm is introduced to update IKD and SKD and adaptively adjust the inertia weight coefficients, to avoid falling into the local optimum. Secondly, an angular evaluation function is added to the evaluation function of the DWA algorithm to avoid the small angle with the obstacle, and the weights of the speed evaluation function and angular evaluation function are dynamically changed to adjust the speed and angle. Finally, the experiments show that the planning path length of the fusion algorithm is 4% less than the ACO algorithm and 15% less than the HLOPSO algorithm, and the other two algorithms contact with the obstacles 5 times more than the algorithm in this paper, which verifies the feasibility of the algorithm.