ERRRT-A*:适用于大规模场景的快速路径规划算法

Lixin Zhang, Hongtao Yin, Ang Li, Longbiao Hu
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引用次数: 0

摘要

在大规模场景中,如何在确保路径长度最短的前提下快速获取路径是一个关键问题。快速探索随机树(RRT)具有快速探索状态空间的特点,但往往难以获得短路径。为了克服这一问题,本文提出了一种基于等距保留策略和 A* 局部搜索(ERRT-A*)的改进 RRT 算法。首先,使用 RRT 进行大步全局快速探索,以获得近似路径。然后,使用等距保留策略丢弃大部分点,保留少量关键点。最后,使用 A* 在每个线段之间进行搜索,以获得新路径。在不同大小的地图上,ERRRT-A* 算法与其他常用算法在路径长度和规划时间方面进行了比较。仿真结果表明,与其他算法相比,该算法在大规模场景中实现了快速规划,同时获得了较短的路径长度,能有效平衡路径长度和规划时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ERRRT-A*: A fast path planning algorithm suitable for large-scale scenes
In large-scale scenes, how to quickly obtain paths while ensuring the shortest possible path length is a key issue. Rapidly-exploring Random Tree (RRT) have the characteristic of quickly exploring the state space, but it is often difficult to obtain a short path. To overcome this problem, this paper proposes an improved RRT algorithm based on equidistance retention strategy and A* local search(ERRRT-A*). First, RRT is used for large-step global fast exploration to obtain approximate paths. Then, an equidistance retention strategy is used to discard most of the points and retain a small number of key points. Finally, A* is used to search between each segment to obtain a new path. The ERRRT-A* algorithm is compared with other commonly used algorithms on maps of different size in terms of path length and planning time. Simulation results indicate that compared with other algorithms, this algorithm achieves fast planning in large-scale scenes while obtaining short path length, which can effectively balance the path length and planning time.
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