Self-piloting path planning method based on improved RTT* algorithm
Zhiyuan Chang, Linxuan He, Yanhua Shao, Xiaoqiang Zhang, Yin Liang, Hongyu Chu · 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022) · 2022
The path planning algorithm is important for the safety and stability of self-piloting. In this paper, target bidirectionalRRT*(TB-RRT*) path planning algorithm based on target gravity and improved metric function is proposed to address the problems of low search efficiency, high randomness, slow convergence, and unsmooth path of the rapidly-exploring random tree star (RRT*) algorithm. Firstly, the algorithm introduces the target gravity and dynamically adjusts the sampling step to improve the search efficiency of the algorithm and reduce the randomness of the search tree growth; through the bidirectional tree growth strategy, the convergence speed of the algorithm is improved. Secondly, the smoothness of the planned paths is improved by considering both Euclidean distance and pinch angle effects on path planning with an improved metric function. Finally, the smoothing path with optimal path cost is obtained by the path selection method. Simulation results show that the improved TB-RRT* algorithm reduces the path length by 12.8%, the running time by 56.9%, and the number of sampled nodes by 43.8%.