An improved ant colony algorithm is proposed for solving the disadvantages， including slow convergence speed, local optimum，etc., of the basic ant colony algorithm in mobile robot path planning. The path planning grid model is established in a static environment. Dynamically adjusting the parameter pheromone heuristic factor and the expected heuristic factor, adaptively changing volatile factor can expand the scope of the search and avoid getting into the local optimum at the initial time. When the robot gets into the concave obstacle and in a complex environment under the condition of low searching efficiency, the algorithm can also have better convergence. The simulation results show that the algorithm in the grid map can quickly avoid obstacles and find the optimal solution.