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JRM Vol.29 No.5 pp. 847-855
doi: 10.20965/jrm.2017.p0847
(2017)

Paper:

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Vision-Based Behavior Planning for Lunar or Planetary Exploration Rover on Flat Surface

Riho Ejiri*, Takashi Kubota**, and Ichiro Nakatani**

*The University of Tokyo
3-1-1 Yoshinodai, Chuo-ku, Sagamihara 252-5210, Japan

**Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA)
3-1-1 Yoshinodai, Chuo-ku, Sagamihara 252-5210, Japan

Received:
April 7, 2017
Accepted:
August 23, 2017
Published:
October 20, 2017
Creative Commons License
Keywords:
behavior planning, route planning, environment recognition, sensing planning
Abstract

Lunar or planetary exploration rovers are expected to have the ability to move across an area as wide as possible in an unknown environment during a limited mission period. Hence, they need an efficient navigation method. Most of the surface of the moon or planets consists of flat ground, sand, and scattered rocks. In a simple flat sandy terrain with some rocks, rough route planning is sufficient for a lunar or planetary rover to avoid obstacles and reach an assigned point. This paper proposes an efficient vision-based planning scheme for exploration rovers on a flat surface with scattered obstacles. In the proposed scheme, dangerous areas are robustly extracted by processing image data, and the degree of danger is defined. A rough routing plan and sensing plan are simultaneously constructed based on the dangerous-area extraction results. The effectiveness of the proposed scheme is discussed based on the results of some simulations and simple experiments.

This paper proposes an efficient vision based planning scheme for exploration rovers on flat surface with scattered obstacles

This paper proposes an efficient vision based planning scheme for exploration rovers on flat surface with scattered obstacles

Cite this article as:
R. Ejiri, T. Kubota, and I. Nakatani, “Vision-Based Behavior Planning for Lunar or Planetary Exploration Rover on Flat Surface,” J. Robot. Mechatron., Vol.29 No.5, pp. 847-855, 2017.
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Last updated on Apr. 22, 2024