A RRT*FN Based Path Replanning Algorithm

Baiming Tong, Qingbao Liu, Chaofan Dai
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引用次数: 7

Abstract

A path replanning algorithm based on RRT*FN(Rapidly-exploring Random Tree Fixed Nodes) is proposed for online local path planning of robot. First, we propose a procedure to reuse the tree from the last planning. Second we design a strategy to balance the exploitation of the old tree and the exploration of the current environment. Finally, the RRT*FN’s strategy is adopted to control the size of the tree. Empirical studies have shown that when the positions of the starting point, the goal and the dynamic obstacles change within a certain range, the proposed algorithm can significantly improve the quality of the solution within a limited time compared to totally starting a new planning using RRT*FN. We also compared the proposed algorithm with the two related replanning algorithms, ORRT* (Online Rapidly-exploring Random Tree*) and RT-RRT* (Real Time Rapidly-exploring Random Tree). The proposed algorithm is better with respect to the time used to find the first feasible path and the cost of the first feasible path.
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基于RRT*FN的路径重规划算法
针对机器人在线局部路径规划问题,提出了一种基于快速探索随机树固定节点(RRT*FN)的路径重规划算法。首先,我们提出了一个从上次规划中重用树的过程。其次,我们设计了一种策略来平衡对老树的开发和对当前环境的开发。最后,采用RRT*FN的策略控制树的大小。实证研究表明,当起点、目标和动态障碍物的位置在一定范围内发生变化时,与使用RRT*FN完全开始一个新的规划相比,所提出的算法可以在有限时间内显著提高解决方案的质量。我们还将提出的算法与两种相关的重规划算法ORRT*(在线快速探索随机树*)和RT-RRT*(实时快速探索随机树)进行了比较。本文提出的算法在寻找第一条可行路径的时间和第一条可行路径的代价方面都有较好的改进。
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