Robot Path Planning Based on Improved Ant Colony Algorithm

In the robot path planning, the basic ant colony algorithm is used to find the optimal path, there are some questions of long search time, low efficiency, and easily falling into local optimum. In this paper, the ant colony algorithm is improved for these problems. The introduction of artificial potential field method as the main means of path planning puts forward the principle of unbalanced initial pheromone. Different grid positions assign different initial pheromone and join pheromone trajectory smoothing strategy. Comparing the two kinds of ant colony algorithm and carrying on the simulation analysis, the improved ant colony algorithm is better than the basic ant colony mainly embodied in algorithm in searching ability, more efficient in algorithm and shorter the searching path. The experimental results show that the improved algorithm can improve the efficiency of the algorithm and restrain the algorithm from falling into the local optimum and realize the optimal path search of the robot so that the robot can quickly avoid the obstacle safely reaching the target point.