Ground vehicle path planning on Uneven terrain Using UAV Measurement point clouds

Kei Otomo, Kiichiro Ishikawa
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Abstract

Abstract. The objective of this study is to develop a system to support rapid ground vehicle activities by planning safe travel routes for ground vehicles from point clouds of wide-area uneven terrain environments measured using UAVs. However, fast path planning is difficult in complex environments such as large, uneven terrain environments. Therefore, this paper proposes a new RRT method based on the RRT algorithm that can perform fast path planning, even in complex environments. In the proposed method, narrow areas that are difficult to be explored by ordinary RRTs are first identified in advance, and nodes are placed in these areas to guide the search. When searching with RRTs, the tree is extended via these guide nodes to efficiently traverse the narrow area. In the validation of the proposed method, a comparison was made with RRT and RRT-Connect in two environments, including narrow areas. The results show that the proposed method has a higher route discovery capability, at least two times fewer search nodes and five times faster path planning capability than other RRTs.
利用无人机测量点云进行不平整地形上的地面车辆路径规划
摘要本研究的目的是开发一种系统,通过使用无人机测量大面积不平整地形环境的点云,为地面车辆规划安全的行驶路线,从而支持地面车辆的快速活动。然而,在复杂的环境(如大面积不平坦地形环境)中,快速路径规划十分困难。因此,本文提出了一种基于 RRT 算法的新 RRT 方法,即使在复杂环境中也能执行快速路径规划。在所提出的方法中,首先要提前确定普通 RRT 难以探索的狭窄区域,并在这些区域中放置节点来引导搜索。在使用 RRT 进行搜索时,树会通过这些引导节点进行扩展,从而有效地穿越狭窄区域。为了验证所提出的方法,我们在包括狭窄区域在内的两种环境中将其与 RRT 和 RRT-Connect 进行了比较。结果表明,与其他 RRT 相比,建议的方法具有更高的路径发现能力,搜索节点数量至少减少两倍,路径规划能力快五倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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