Kinematic Constrained Bi-directional RRT with Efficient Branch Pruning for robot path planning
Kinematic Constrained Bi-directional RRT with Efficient Branch Pruning for robot path planning
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DOI：
10.1016/j.eswa.2020.114541
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发表时间：
2021-01-10
影响因子：
8.5
通讯作者：
Meng, Max Q. -H.
中科院分区：
文献类型：
--
作者：
Wang, Jiankun;Li, Baopu;Meng, Max Q. -H.
在本文中，我们提出了一种新颖的路径规划算法，称为具有高效分支修剪的运动学约束双向快速探索随机树（KB-RRT*），该算法是在双向快速探索随机树（Bi-RRT）方法的基础上设计的。 KB-RRT* 适用于差动驱动移动机器人的路径规划。通过结合运动学约束，KB-RRT* 可以避免树不必要的生长，并快速为智能体找到可行的路径。此外，所提出的高效分支剪枝策略有助于生成的状态找到更好的父状态并删除其周围成本较高的边。因此，KB-RRT*实现了快速、高质量的路径规划。数值模拟结果表明，与传统的路径规划算法相比，所提出的KB-RRT*可以获得更好的性能。
In this paper, we present a novel path planning algorithm called Kinematic Constrained Bi-directional Rapidly exploring Random Tree with Efficient Branch Pruning (KB-RRT*), which is designed on the basis of Bi-directional Rapidly-exploring Random Tree (Bi-RRT) method. The KB-RRT* is suitable for path planning of a differential drive mobile robot. By incorporating the kinematic constraints, the KB-RRT* can avoid unnecessary growth of the tree and quickly find a feasible path for the agent. In addition, the proposed efficient branch pruning strategy helps the generated state find a better parent state and delete the edges with high cost around it. Therefore, the KB-RRT* achieves fast and high-quality path planning. The results of numerical simulations reveal that the proposed KB-RRT* can achieve better performance compared with the conventional path planning algorithms.