Aiming at the difficulty of path planning for mobile robots in complex environment, a hybrid algorithm combining ant colony algorithm and artificial potential field method for local path planning was proposed. Firstly, multi-factor heuristic function and ant travel mechanism were used to solve the problem that the path quality of traditional ant colony algorithm was poor and it was easy to fall into diagonal obstacles. Secondly, in view of the slow convergence of traditional ant colony algorithm, the adaptive volatilization coefficient and dynamic weight coefficient were designed. Then, the concepts of virtual target point, relative distance and safe distance were introduced to solve the problems of local minimum, unreachable target and excessive obstacle avoidance in traditional artificial potential field method. Finally, the turning point of the path planned by the improved ant colony algorithm was used as the local subentry point to invoke the improved artificial potential field method for secondary planning. The simulation results showed that the improved ant colony algorithm optimized the path length by 9.9% and 2.0%, the path turning times by 81.8% and 63.6%, and the convergence speed by 94.2% and 63.6% compared with that of the traditional algorithm and other literature algorithms. The improved artificial potential field method effectively solved the shortcomings of unreachable target, easy to fall into local minimum and excessive obstacle avoidance. The hybrid algorithm based on the two methods effectively combined the advantages of the two methods, and had high environmental adaptability and path planning efficiency in complex static and dynamic environments.