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

Reiya Takemura, Genya Ishigami

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)838-846
Number of pages9
JournalJournal of Robotics and Mechatronics
Volume29
Issue number5
DOIs
Publication statusPublished - 2017 Oct 1

Fingerprint

Motion planning
Sampling
Mobile robots
Planning
Geometry
Sensors

Keywords

  • Field robotics
  • Path planning
  • Planetary rover
  • RRT

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

Cite this

Traversability-based RRT∗ for planetary rover path planning in rough terrain with LIDAR point cloud data. / Takemura, Reiya; Ishigami, Genya.

In: Journal of Robotics and Mechatronics, Vol. 29, No. 5, 01.10.2017, p. 838-846.

Research output: Contribution to journalArticle

@article{4e393f9cc5204f26841210c3abdaf1f9,
title = "Traversability-based RRT∗ for planetary rover path planning in rough terrain with LIDAR point cloud data",
abstract = "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.",
keywords = "Field robotics, Path planning, Planetary rover, RRT",
author = "Reiya Takemura and Genya Ishigami",
year = "2017",
month = "10",
day = "1",
doi = "10.20965/jrm.2017.p0838",
language = "English",
volume = "29",
pages = "838--846",
journal = "Journal of Robotics and Mechatronics",
issn = "0915-3942",
publisher = "Fuji Technology Press",
number = "5",

}

TY - JOUR

T1 - Traversability-based RRT∗ for planetary rover path planning in rough terrain with LIDAR point cloud data

AU - Takemura, Reiya

AU - Ishigami, Genya

PY - 2017/10/1

Y1 - 2017/10/1

N2 - 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.

AB - 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.

KW - Field robotics

KW - Path planning

KW - Planetary rover

KW - RRT

UR - http://www.scopus.com/inward/record.url?scp=85031903255&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85031903255&partnerID=8YFLogxK

U2 - 10.20965/jrm.2017.p0838

DO - 10.20965/jrm.2017.p0838

M3 - Article

AN - SCOPUS:85031903255

VL - 29

SP - 838

EP - 846

JO - Journal of Robotics and Mechatronics

JF - Journal of Robotics and Mechatronics

SN - 0915-3942

IS - 5

ER -