Learning for autonomous navigation

Angelova, Anelia; Howard, Andrew; Matthies, Larry; Tang, Benyang; Turmon, Michael; Mjolsness, Eric

dc.contributor.author	Angelova, Anelia
dc.contributor.author	Howard, Andrew
dc.contributor.author	Matthies, Larry
dc.contributor.author	Tang, Benyang
dc.contributor.author	Turmon, Michael
dc.contributor.author	Mjolsness, Eric
dc.date.accessioned	2006-02-13T21:43:36Z
dc.date.available	2006-02-13T21:43:36Z
dc.date.issued	2005-12
dc.identifier.citation	Neural Information Processing Systems, Vancouver, Canada, December 8-12, 2005	en
dc.identifier.clearanceno	05-3730
dc.identifier.uri	http://hdl.handle.net/2014/38551
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.	en
dc.description.sponsorship	NASA	en
dc.format.extent	961255 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	robotics	en
dc.subject	navigation	en
dc.subject	learning	en
dc.title	Learning for autonomous navigation	en
dc.type	Preprint	en