An adaptive bidirectional quick optimal Rapidly-exploring Random Tree algorithm for path planning - Huang, Zhuo; Gao, Yang; Guo, Jian; Qian, Chen; Chen, Qingwei (Engineering applications of artificial intelligence, 2024)
In recent decades, sampling-based algorithms have played an important role in path planning. As an extended algorithm of Rapidly-exploring Random Tree(RRT), the optimal RRT(RRT*) has advantages in asymptotic optimality. However, it still suffers from a slow convergence rate and expensive initial solution heavily. Besides, it cannot provide an effective path planning solution when dealing with narrow passages. To overcome this problem, an adaptive bidirectional quick RRT*(ABQ-RRT*) method is proposed for Unmanned Aerial Vehicle(UAV) in narrow passages. Firstly, the cost function has been redefined by taking into account both the Euclidean distance and the turning angle of the UAV. Secondly, the number of failures in the collision detection is considered and an adaptive goal-biased sampling strategy is adopted to obtain higher-quality sampling points. Thirdly, by local sampling near the obstacles, the type of the local environment is quickly judged. Also, the repulsion field is superimposed to effectively avoid obstacles. Then, during the process of selecting the optimal parent node from the set of candidate nodes, the sorted possible parents consider both the neighborhood nodes and their ancestors to obtain better paths. At last, the effectiveness of ABQ-RRT* is confirmed by comparing it with several other improved RRT algorithms through simulations.