Aiming at the problems of poor global search ability, low initialization pheromone, poor convergence, and weak optimization ability when ant colony algorithm is applied to robot path planning, a multi-factor improved ant colony algorithm is proposed. The ant colony algorithm is optimized by changing the distribution of the initial pheromone concentration, changing the heuristic function, adopting the ant regression strategy, and introducing the ant optimization sorting method. Using MATLAB software to simulate the improved ant colony algorithm and hexapod robot experiments, the results show that the improved algorithm has better path and fewer iterations, which improves the robustness and optimization ability of the algorithm.