Path Planning of Mobile Robot Using Optimized ACA

In order to solve the problems that the ant colony algorithm easily falls into local optimum and has a slow convergence speed when it is used in path planning for mobile robot, the paper proposed an improved ant colony algorithm which was used in path planning for mobile robot in the static environment. The proposed algorithm improved the transition rule of node state to increase the probability of searching optimal path, designed an improved the heuristic function to improve the searching efficiency of the algorithm, and updated the pheromone avoid falling into local optimum. The simulation results show that the proposed algorithm has faster convergence speed under the condition of the same result of path planning when compared to the traditional algorithm in the same environment. The improved algorithm obtains the optimal path in the environments of different complexity levels and it shows the efficiency and feasibility of the improved algorithm, which has good optimization ability and can be applied to path planning for mobile robot in the environments of different complexity levels.