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Terrain-Dependent Slip Risk Prediction for Planetary Exploration Rovers

Published online by Cambridge University Press:  23 February 2021

Masafumi EndoOpen the ORCID record for Masafumi Endo[Opens in a new window],
Shogo Endo,
Kenji Nagaoka and
Kazuya Yoshida
Masafumi Endo*
Affiliation:
Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan E-mails: syogo.endo.r7@dc.tohoku.ac.jp, yoshida@astro.mech.tohoku.ac.jp
Shogo Endo
Affiliation:
Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan E-mails: syogo.endo.r7@dc.tohoku.ac.jp, yoshida@astro.mech.tohoku.ac.jp
Kenji Nagaoka
Affiliation:
Department of Mechanical and Control Engineering, Graduate School of Engineering, Kyushu Institute of Technology, Kitakyushu, Japan E-mail: nagaoka.kenji572@mail.kyutech.jp
Kazuya Yoshida
Affiliation:
Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan E-mails: syogo.endo.r7@dc.tohoku.ac.jp, yoshida@astro.mech.tohoku.ac.jp
*
*Corresponding author. E-mail: masafumi.endo@ieee.org
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Summary

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Wheel slip prediction on rough terrain is crucial for secure, long-term operations of planetary exploration rovers. Although rough, unstructured terrain hampers mobility, prediction by modeling wheel–terrain interactions remains difficult owing to unclear terrain conditions and complexities of terramechanics models. This study proposes a vision-based approach with machine learning for predicting wheel slip risk by estimating the slope from 3D information and classifying terrain types from image information. It considers the slope estimation accuracy for risk prediction under sharp increases in wheel slip due to inclined ground. Experimental results obtained with a rover testbed on several terrain types validate this method.

Keywords

Planetary exploration roversMachine learningWheel slip predictionExteroceptive sensingSlope estimation

Information

Type
Article
Information
Robotica , Volume 39 , Issue 10 , October 2021, pp. 1883 - 1896
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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