In order to solve the problem that mobile robots fall into local convergence and cannot achieve the optimal path in the path planning of ant colony algorithm, this paper proposes an improved variable-step ant colony algorithm to enable it to achieve the path with fewer convergence iterations optimal. According to the relevant characteristics of ant colony algorithm that applied in path planning, it optimizes the allocation of pheromone, reduces the impact of local pheromone content on the algorithm, avoids the ant colony from falling into the local optimum when searching the path, adds the weighting factor in the transition probability formula and increases the probability of the mobile robot moving in the direction of the end point, it effectively reduces the number of ant colony convergence iterations, changes the mobile robot’s moving step length, enables it to move freely and without collision within 360 °, and effectively shortens the path length. The simulation results show that: in the simple environment, the convergent iteration times and the optimal path length of the improved variable step ant colony algorithm are 2 times and 28. 042 m respectively, while the convergent iteration times and the optimal path length of the traditional ant colony algorithm are 25 times and 29. 213 m respectively. In the complex environment, the convergence iteration times and the optimal path length of the improved variable step ant colony algorithm are 2 times and 43. 960 2 m respectively, and the convergence iteration times and the optimal path length of the improved potential field ant colony algorithm are 16 times and 45. 112 7 m respectively. The simulation results demonstrate the effectiveness and superiority of the improved variable step ant colony algorithm.