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Learning to Predict Slip for Ground Robots

Angelova, Anelia and Matthies, Larry and Helmick, Daniel and Sibley, Gabe and Perona, Pietro (2006) Learning to Predict Slip for Ground Robots. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation. IEEE , Piscataway, NJ, pp. 3324-3331. ISBN 0-7803-9505-0. https://resolver.caltech.edu/CaltechAUTHORS:20110120-094149191

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Abstract

In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do that, the information of terrain appearance and geometry regarding some location is correlated to the slip measured by the rover while this location is being traversed. This relationship is learned from previous experience, so slip can be predicted later at a distance from visual information only. The advantages of the approach are: 1) learning from examples allows the system to adapt to unknown terrains rather than using fixed heuristics or predefined rules; 2) the feedback about the observed slip is received from the vehicle's own sensors which can fully automate the process; 3) learning slip from previous experience can replace complex mechanical modeling of vehicle or terrain, which is time consuming and not necessarily feasible. Predicting slip is motivated by the need to assess the risk of getting trapped before entering a particular terrain. For example, a planning algorithm can utilize slip information by taking into consideration that a slippery terrain is costly or hazardous to traverse. A generic nonlinear regression framework is proposed in which the terrain type is determined from appearance and then a nonlinear model of slip is learned for a particular terrain type. In this paper we focus only on the latter problem and provide slip learning and prediction results for terrain types, such as soil, sand, gravel, and asphalt. The slip prediction error achieved is about 15% which is comparable to the measurement errors for slip itself.


Item Type:Book Section
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URLURL TypeDescription
http://dx.doi.org/10.1109/ROBOT.2006.1642209DOIUNSPECIFIED
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1642209&tag=1PublisherUNSPECIFIED
ORCID:
AuthorORCID
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2006 IEEE. Issue Date: 15-19 May 2006; Date of Current Version: 26 June 2006. This research is funded by the NASA Mars Technology Program. Thanks also to the JPL LAGR team for giving us access to the LAGR vehicle, to Andrew Howard, Steve Goldberg, Lee Magnone, and Nathan Koenig for helping us with the data collection, to Max Bajracharya for reviewing the paper and providing helpful comments and to Randy Lindemann and Garett Sohl for discussions.
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NASA Mars Technology ProgramUNSPECIFIED
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INSPEC Accession Number9109599
Record Number:CaltechAUTHORS:20110120-094149191
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20110120-094149191
Official Citation:Angelova, A.; Matthies, L.; Helmick, D.; Sibley, G.; Perona, P.; , "Learning to predict slip for ground robots," Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on , vol., no., pp.3324-3331, 15-19 May 2006 doi: 10.1109/ROBOT.2006.1642209 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1642209&isnumber=34383
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:21824
Collection:CaltechAUTHORS
Deposited By: Jason Perez
Deposited On:20 Jan 2011 21:12
Last Modified:03 Oct 2019 02:30

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