To solve the problems of poor global path quality and the tendency of local paths to fall into local optimality when mobile robots plan in complex environments and dynamic obstacles, a fusion algorithm based on the ant colony algorithm and the artificial potential field method is proposed. Firstly, in view of the poor global search ability and slow convergence speed of the traditional ant colony algorithm, its search method is optimized, a new pheromone update rule is constructed, the revised heuristic information is introduced, and a path node optimization strategy is designed to improve its path quality and search efficiency. Secondly, the problem of unreachable target and local minimum in the traditional artificial potential field method is solved by adding the relative distance from the mobile robot to the target point into the repulsive potential field function and setting sub-target points. Finally, the improved ant colony algorithm and the improved artificial potential field method are integrated to improve the path planning performance of the fusion algorithm in complex dynamic and static environments. The parameter combination of the improved artificial potential field method is selected through simulation analysis. Compared with the traditional ant colony algorithm, the simulation results show that, the optimal path of the improved ant colony algorithm is shortened by 26.23%, the turning points of the path are reduced by 60.00%, and the search efficiency is improved by 73.75%. The improved artificial potential field method effectively solves the limitations of the traditional artificial potential field method and improves its local obstacle avoidance capability. The fusion algorithm can plan a collision-free and smooth path while maintaining compliance with the global optimal path. In actual scenarios, the experimental results show that: the path planned by the improved ant colony algorithm is shorter than that of the existing traditional algorithm. In the Gazebo physical simulation platform, the fusion algorithm can effectively avoid static obstacles, verifying its theoretical feasibility.