Terrain-Dependent Slip Risk Prediction for Planetary Exploration Rovers

Masafumi Endo, Shogo Endo, Kenji Nagaoka, Kazuya Yoshida

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1883-1896
Number of pages14
JournalRobotica
Volume39
Issue number10
DOIs
Publication statusPublished - 2021 Oct 23

Keywords

  • Exteroceptive sensing
  • Machine learning
  • Planetary exploration rovers
  • Slope estimation
  • Wheel slip prediction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mathematics(all)
  • Computer Science Applications

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