The paper proposes an improved ant colony algorithm with potential field heuristic for the path planning of mobile robots in the global static environment. The algorithm constructs the comprehensive heuristic information based on the initial path obatined by using the artificial potential field method and the distance between the robot and the next node. Then, the heuristic information decline coefficient is introduced to avoid the local optimization problem caused by misleading information of the traditional ant colony algorithm. Based on the zero point theorem, this paper proposes an initial pheromone unequal allocation principle. Various grid positions are endowed with different initial pheromones, which decreases the blindness of ant colony search and improves the searching efficiency of the algorithm. An iterative threshold is set to adaptively adjust pheromone volatilization coefficients. In this way, the algorithm has excellent global searching ability, and the stagnation phenomenon can be avoided. The simulation results show the feasibility and effectiveness of the proposed method.