In order to overcome the traditional ant colony algorithm easy to drop into local optimum, and improve the environmental adaptability and convergence speed of the path planning algorithm, an improved ant colony algorithm based on self-adaption threshold has been proposed in this paper. In the early stages of the optimization process, it uses self-adaption threshold to intervene the optimization process to avoid it dropping into local optimum. With the increase of the number of iterations, the threshold continues the impact on the optimization process, until the optimization process is guided by pheromone and heuristic information completely. The simulation experiments demonstrate the feasibility and effectiveness of the optimization algorithm. Compared with existing ant colony algorithms, the proposed algorithm can plan an optimal path quickly in different environments with satisfactory convergence speed and environment adaptability.