Bidirectional Extended Random Tree Path Planning Algorithm Guided by Improved Artificial Potential Field Method
Aiming at the requirements of strong real-time path planning,high route smoothness,accurate and complete obstacle avoidance for ground mobile robots in complex environments,a bidirectional extended random tree algorithm(APF-BI-RRT∗)guided by improved artificial Potential Field method(APF)was proposed based on the fast extended random tree algorithm(RRT).The algorithm first expanded two random trees from the starting point and target point respectively in each iteration to speed up the algorithm convergence speed.Sec-ondly,in the growth direction of the algorithm random tree,the target bias strategy was introduced to optimize the selection of random child nodes,and artificial potential field components were added to the random tree and obstacles to limit the randomness of path direction selection.The improved algorithm overcome the problem of local optimal value or unreachable target caused by excessive gravitational and repulsive forces.Finally,a sam-pling optimization and key node smoothing strategy was applied on the formed sawtooth planning path to further shorten and smooth the total distance of the original path.Comparative experimental results showed that the proposed algorithm not only overcome the problem of blind node expansion of traditional random tree algorithm,but also took into account the efficiency and smoothness of path generation.Compared with the target bias RRT algorithm,the proposed algorithm reduced the planned path length by about 9.7%and the running time by a-bout 65.3%.The number of algorithm iterations was reduced by about 78.2%.
improved artificial potential field methodbidirectional fast expanding random treepath planningcurve sampling optimization
万方数据
维普
