Fusion Algorithm of the Improved A* Algorithm and Segmented Bézier Curves for the Path Planning of Mobile Robots
Rongshen Lai, Zhiyong Wu, Xiangui Liu, Nianyin Zeng
Abstract
In terms of mobile robot path planning, the traditional A* algorithm has the following problems: a long searching time, an excessive number of redundant nodes, and too many path-turning points. As a result, the shortest path obtained from planning may not be the optimal movement route of actual robots, and it will accelerate the hardware loss of robots. To address the aforementioned problems, a fusion algorithm for path planning, combining the improved A* algorithm with segmented second-order Bézier curves, is proposed in this paper. On the one hand, the improved A* algorithm is presented to reduce unnecessary expansion nodes and shorten the search time, which was achieved from three aspects: (1) the traditional 8-neighborhood search strategy was adjusted to 5-neighborhood according to the orientation of the target point relative to the current node; (2) the dynamic weighting factor of the heuristic function was introduced into the evaluation function of the traditional A* algorithm; and (3) the key node extraction strategy was designed to reduce the redundant nodes of the optimal path. On the other hand, the optimal path planned by the improved A* algorithm was smoothed using segmented second-order Bézier curves. The simulation results show that the improved A* algorithm can effectively reduce the search time and redundant nodes and the fusion algorithm can reduce the path curvature and path length to a certain extent, improving path safety.