关键词: 移动机器人, 路径规划, 蚁群算法, B样条曲线平滑策略, 栅格地图环境

Abstract: In order to solve the problems of ant colony algorithm in global path planning under static environment, such as unable to find the shortest path, slow convergence speed, great blindness of path search and many inflection points, propose an improved ant colony algorithm. Taking the grid map as the running environment of the robot, distribute unevenly the initial pheromones, which makes the path search more inclined to the line between the starting point and the target point; the information of the current node, add the next node and the target point into the heuristic function and the dynamic adjustment factor to achieve the purpose of strong guidance in the early stage and weakening in the later stage; introduce the pseudo-random transfer strategy to reduce the blind path selection, speed up finding the shortest path; dynamic adjustment of the volatile coefficient can make the volatile coefficient larger in the early stage and smaller in the later stage, so as to avoid premature convergence of the algorithm; on the basis of the optimal solution, the introduction of B-spline curve smoothing strategy can further optimize the optimal solution, resulting in shorter and smoother path. Divide the overall improvement scheme, set the gradient comparison scheme and compare them with the traditional method one by one. Finally, the overall simulation comparison with other algorithms verifies the feasibility, effectiveness and superiority of the improved algorithm.

Key words: mobile robot, path planning, ant colony algorithm, B-spline curve smoothing strategy, grid map environment