The path planning is the key technology of AGV path finding. This paper uses an improved ant colony algorithm to plan the path of an AGV. For avoiding the defects of traditional ant colony algorithm such as low smoothness of route and local optimal solution, the transition probability and pheromone update method are improved. Various actual turning situations are analyzed in the transition probability, the basis for defining the smoothing factor is provided by the Bezier curve, and a random selection operator is formed for updating local pheromone by extracting characteristic information of iterative process. The simulation results in different environments prove that the smoothing factor plays an important role in optimizing the smoothness of the path and the diversity of the constructed solutions, and the random selection operator is effective in solving the contradiction of the local optimal solution and in finding the optimal solution.