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Learning and prediction of slip from visual information

Angelova, Anelia and Matthies, Larry and Helmick, Daniel and Perona, Pietro (2007) Learning and prediction of slip from visual information. Journal of Field Robotics, 24 (3). pp. 205-231. ISSN 1556-4959. https://resolver.caltech.edu/CaltechAUTHORS:20140730-101717617

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Abstract

This paper presents an approach for slip prediction from a distance for wheeled ground robots using visual information as input. Large amounts of slippage which can occur on certain surfaces, such as sandy slopes, will negatively affect rover mobility. Therefore, obtaining information about slip before entering such terrain can be very useful for better planning and avoiding these areas. To address this problem, terrain appearance and geometry information about map cells are correlated to the slip measured by the rover while traversing each cell. This relationship is learned from previous experience, so slip can be predicted remotely from visual information only. The proposed method consists of terrain type recognition and nonlinear regression modeling. The method has been implemented and tested offline on several off-road terrains including: soil, sand, gravel, and woodchips. The final slip prediction error is about 20%. The system is intended for improved navigation on steep slopes and rough terrain for Mars rovers. (c) 2007 Wiley Periodicals, Inc.


Item Type:Article
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http://onlinelibrary.wiley.com/doi/10.1002/rob.20179/abstractPublisherArticle
ORCID:
AuthorORCID
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2007 Wiley Periodicals, Inc. Received 2 June 2006; accepted 7 December 2006. The research described here was carried out by the Jet Propulsion Laboratory, California Institute of Technology, with funding from the NASA’s Mars Technology Program. The authors thank the JPL LAGR team for giving us access to the LAGR vehicle, Andrew Howard, Steve Goldberg, Gabe Sibley, Nathan Koenig, and Lee Magnone for helping them with the data collection, and Daniel Clouse and four anonymous reviewers for providing very useful comments on the paper.
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Issue or Number:3
Record Number:CaltechAUTHORS:20140730-101717617
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20140730-101717617
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:47601
Collection:CaltechAUTHORS
Deposited By: Caroline Murphy
Deposited On:21 Aug 2014 23:56
Last Modified:27 Aug 2020 22:45

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