Difference between revisions of "Publications:Comparison of Machine Learning Approaches for Soil Embedding Detection of Planetary Exploration Rovers"
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|PID=971911 | |PID=971911 | ||
| − | |Name=Gonzalez, Ramon (Massachusetts Institute of Technology, Cambridge, MA, USA) (Robotic Mobility Group);Byttner, Stefan | + | |Name=Gonzalez, Ramon (Massachusetts Institute of Technology, Cambridge, MA, USA) (Robotic Mobility Group);Byttner, Stefan (stefan) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650));Iagnemma, Karl (kariag) (Massachusetts Institute of Technology, Cambridge, MA, USA) (Robotic Mobility Group) |
|Title=Comparison of Machine Learning Approaches for Soil Embedding Detection of Planetary Exploration Rovers | |Title=Comparison of Machine Learning Approaches for Soil Embedding Detection of Planetary Exploration Rovers | ||
|PublicationType=Conference Paper | |PublicationType=Conference Paper | ||
Latest revision as of 22:42, 30 September 2016
Property "Author" has a restricted application area and cannot be used as annotation property by a user. Property "Author" has a restricted application area and cannot be used as annotation property by a user. Property "Author" has a restricted application area and cannot be used as annotation property by a user.
| Title | Comparison of Machine Learning Approaches for Soil Embedding Detection of Planetary Exploration Rovers |
|---|---|
| Author | |
| Year | 2016 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | Proceedings of the 8th ISTVS Americas Conference, Detroit, September 12-14, 2016. |
| Conference | International Conference of the ISTVS (International Society for Terrain-Vehicle Systems), Detroit, Michigan, USA, 12-14 September, 2016 |
| DOI | |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:971911 |
| Abstract | 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 %). |
