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  • 1.
    Bouguelia, Mohamed-Rafik
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Gonzalez, Ramon
    Robotic Mobility Group, Massachusetts Institute of Technology, Cambridge, USA.
    Iagnemma, Karl
    Robotic Mobility Group, Massachusetts Institute of Technology, Cambridge, USA.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Unsupervised classification of slip events for planetary exploration rovers2017In: Journal of terramechanics, ISSN 0022-4898, E-ISSN 1879-1204, Vol. 73, p. 95-106Article in journal (Refereed)
    Abstract [en]

    This paper introduces an unsupervised method for the classification of discrete rovers' slip events based on proprioceptive signals. In particular, the method is able to automatically discover and track various degrees of slip (i.e. low slip, moderate slip, high slip). The proposed method is based on aggregating the data over time, since high level concepts, such as high and low slip, are concepts that are dependent on longer time perspectives. Different features and subsets of the data have been identified leading to a proper clustering, interpreting those clusters as initial models of the prospective concepts. Bayesian tracking has been used in order to continuously improve the parameters of these models, based on the new data. Two real datasets are used to validate the proposed approach in comparison to other known unsupervised and supervised machine learning methods. The first dataset is collected by a single-wheel testbed available at MIT. The second dataset was collected by means of a planetary exploration rover in real off-road conditions. Experiments prove that the proposed method is more accurate (up to 86% of accuracy vs. 80% for K-means) in discovering various levels of slip while being fully unsupervised (no need for hand-labeled data for training). © 2017 ISTVS

  • 2.
    Shibly, H.
    et al.
    Mechanical Engineering Department, Birzeit University, Palestinian Territories, via Israel.
    Iagnemma, Karl
    Mechanical Engineering Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
    Dubowsky, S.
    Mechanical Engineering Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
    An equivalent soil mechanics formulation for rigid wheels in deformable terrain, with application to planetary exploration rovers2005In: Journal of terramechanics, ISSN 0022-4898, E-ISSN 1879-1204, Vol. 42, no 1, p. 1-13Article in journal (Refereed)
    Abstract [en]

    A simplified, closed-form version of the basic mechanics of a driven rigid wheel on low-cohesion deformable terrain is presented. This approach allows the formulation of an on-line terrain parameter estimation algorithm, which has important applications for planetary exploration rovers. Analytical comparisons of the original and simplified equations are presented, and are shown to closely agree. Experimental results from a single-wheel testbed operating in dry sand shows that the simplified equations can be used for mobility prediction with good accuracy. Methods for incorporating the simplified equations into an on-line terrain parameter algorithm are discussed.

  • 3.
    Shoop, Sally
    et al.
    US Army Engineering Research and Development Center, Cold Region Research and Engineering Laboratory, Hanover, NH, USA.
    Iagnemma, Karl
    Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, MA, USA.
    Richter, Lutz
    German Aerospace Center (DLR), Institute of Space Systems, Bremen, Germany.
    Special issue on terrain interaction for small robotic vehicles2009In: Journal of terramechanics, ISSN 0022-4898, E-ISSN 1879-1204, Vol. 46, no 3, p. 65-66Article in journal (Refereed)
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