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Self-supervised terrain classification for planetary surface exploration rovers
MIT.
MIT.
2012 (English)In: Journal of Field Robotics, ISSN 1556-4959, Vol. 29, no 3, p. 445-468Article in journal (Refereed) Published
Abstract [en]

In future planetary exploration missions, improvements in autonomous rover mobility have the potential to increase scientific data return by providing safe access to geologically interesting sites that lie in rugged terrain, far from landing areas. To improve rover-based terrain sensing, this paper proposes a self-supervised learning framework that will enable a robotic system to learn to predict mechanical properties of distant terrain, based on measurements of mechanical properties of similar terrain that has been traversed previously. In this framework, a proprioceptive terrain classifier is used to distinguish terrain classes based on features derived from rover-terrain interaction, and labels from this classifier are used to train an exteroceptive (i.e., vision-based) terrain classifier. Once trained, the vision-based classifier is able to recognize similar terrain classes in stereo imagery. This paper presents two distinct proprioceptive classifiers-a novel approach based on optimization of a traction force model and a previously described approach based on wheel vibration-as well as a vision-based terrain classification approach suitable for environments with unexpected appearances. The high accuracy of the self-supervised learning framework and its supporting algorithms is demonstrated using experimental data from a four-wheeled robot in an outdoor Mars-analogue environment. © 2012 Wiley Periodicals, Inc.

Place, publisher, year, edition, pages
Hoboken, NJ: John Wiley & Sons, 2012. Vol. 29, no 3, p. 445-468
Keywords [en]
Autonomous rovers, Experimental data, Landing area, Learning frameworks, Planetary surface exploration, Planetary-exploration missions, Robotic systems, Rover-terrain interaction, Rugged terrain, Scientific data, Stereo imagery, Terrain classification, Terrain classifiers, Terrain sensing, Traction forces, Vision based
National Category
Robotics
Identifiers
URN: urn:nbn:se:hh:diva-20724DOI: 10.1002/rob.21408ISI: 000302469800005Scopus ID: 2-s2.0-84859711759OAI: oai:DiVA.org:hh-20724DiVA, id: diva2:586857
Available from: 2013-01-13 Created: 2013-01-12 Last updated: 2018-03-22Bibliographically approved

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Iagnemma, Karl

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf