Robot path planning based on Ant colony Algorithm

Abstract

In view of the problems of ant colony algorithm in global path planning under static environment, such as slow convergence speed, great blindness of path search and easy access to local optimal solutions, an improved ant colony algorithm is proposed. Taking the rasterization map as the running environment of the robot, the initial pheromones were distributed unevenly, so that the path search tended to be near the line between the starting point and the target point. Pseudo random strategy was introduced on path selection probability to reduce the blindness of path selection and speed up finding the shortest path. The volatilization coefficient was adjusted dynamically to make the volatilization coefficient larger in the early stage and smaller in the later stage, so as to avoid premature convergence of the algorithm. Finally, the path planning results before and after the improvement were discussed, and the influence of important parameters on the results of ant colony algorithm was analyzed.