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Learning for autonomous navigation : extrapolating from underfoot to the far field

Show simple item record Matthies, Larry Turmon, Michael Howard, Andrew Angelova, Anelia Tang, Benyang Mjolsness, Eric 2006-03-10T21:24:03Z 2006-03-10T21:24:03Z 2005
dc.identifier.citation Journal of Machine Learning Research, Vol.1, pp.1-48 (2005) en
dc.identifier.clearanceno 05-3174
dc.description.abstract Autonomous off-road navigation of robotic ground vehicles has important applications on Earth and in space exploration. Progress in this domain has been retarded by the limited lookahead range of 3-D sensors and by the difficulty of preprogramming systems to understand the traversability of the wide variety of terrain they can encounter. Enabling robots to learn from experience may alleviate both of these problems. We define two paradigms for this, learning from 3-D geometry and learning from proprioception, and describe initial instantiations of them we have developed under DARPA and NASA programs. Field test results show promise for learning traversability of vegetated terrain, learning to extend the lookahead range of the vision system, and learning how slip varies with slope. en
dc.description.sponsorship NASA/JPL en
dc.format.extent 965302 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2005 en
dc.subject autonomous robots en
dc.subject vision systems en
dc.title Learning for autonomous navigation : extrapolating from underfoot to the far field en
dc.type Preprint en

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