Traversability-based RRT∗ for planetary rover path planning in rough terrain with LIDAR point cloud data

Reiya Takemura, Genya Ishigami

研究成果: Article査読

12 被引用数 (Scopus)

抄録

Sampling-based search algorithms such as Rapidly-Exploring Random Trees (RRT) have been utilized for mobile robot path planning and motion planning in high dimensional continuous spaces. This paper presents a path planning method for a planetary exploration rover in rough terrain. The proposed method exploits the framework of a sampling-based search, the optimal RRT (RRT∗) algorithm. The terrain geometry used for planning is composed of point cloud data close to continuous space captured by a light detection and ranging (LIDAR) sensor. During the path planning phase, the proposed RRT∗ algorithm directly samples a point (node) from the LI-DAR point cloud data. The path planner then considers the rough terrain traversability of the rover during the tree expansion process of RRT∗. This process improves conventional RRT∗ in that the generated path is safe and feasible for the rover in rough terrain. In this paper, simulation study on the proposed path planning algorithm in various real terrain data confirms its usefulness.

本文言語English
ページ(範囲)838-846
ページ数9
ジャーナルJournal of Robotics and Mechatronics
29
5
DOI
出版ステータスPublished - 2017 10月

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 電子工学および電気工学

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