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Comparison of Machine Learning Approaches for Soil Embedding Detection of Planetary Exploration Rovers
Massachusetts Institute of Technology, Cambridge, MA, USA. (Robotic Mobility Group)
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Massachusetts Institute of Technology, Cambridge, MA, USA. (Robotic Mobility Group)
2016 (English)In: Proceedings of the 8th ISTVS Americas Conference, Detroit, September 12-14, 2016., 2016Conference paper (Refereed)
Abstract [en]

This paper analyzes the advantages and limitations of known machine learning approaches to cope with the problem of incipient rover embedding detection based on propioceptive signals. In particular, two supervised learning approaches (Support Vector Machines and Feed-forward Neural Networks) are compared to two unsupervised learning approaches (K-means and Self-Organizing Maps) in order to identify various degrees of slip (e.g. low slip, moderate slip, high slip). A real dataset collected by a single-wheel testbed available at MIT has been used to validate each strategy. The SVM algorithm achieves the best performance (accuracy >95 %). However, the SOM algorithm represents a better solution in terms of accuracy and the need of hand-labeled data for training the classifier (accuracy >84 %).

Place, publisher, year, edition, pages
2016.
Keyword [en]
Support Vector Machine (SVM), Feed-forward Neural Network (FF-NN), K-means, Self-Organizing Map (SOM), Mars Science Laboratory (MSL) rover
National Category
Signal Processing Robotics
Identifiers
URN: urn:nbn:se:hh:diva-32049OAI: oai:DiVA.org:hh-32049DiVA: diva2:971911
Conference
International Conference of the ISTVS (International Society for Terrain-Vehicle Systems), Detroit, Michigan, USA, 12-14 September, 2016
Note

Funding: NASA

Available from: 2016-09-19 Created: 2016-09-19 Last updated: 2016-12-01Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • 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
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  • asciidoc
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