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dc.contributor.authorKewlani, Gaurav
dc.contributor.authorIagnemma, Karl
dc.date.accessioned2010-10-21T15:31:41Z
dc.date.available2010-10-21T15:31:41Z
dc.date.issued2009-12
dc.date.submitted2009-10
dc.identifier.isbn978-1-4244-3803-7
dc.identifier.otherINSPEC Accession Number: 11010168
dc.identifier.urihttp://hdl.handle.net/1721.1/59443
dc.description.abstractThe ability of mobile robots to quickly and accurately analyze their dynamics is critical to their safety and efficient operation. In field conditions, significant uncertainty is associated with terrain and/or vehicle parameter estimates, and this must be considered in an analysis of robot motion. Here a Multi-Element generalized Polynomial Chaos (MEgPC) approach is presented that explicitly considers vehicle parameter uncertainty for long term estimation of robot dynamics. It is shown to be an improvement over the generalized Askey polynomial chaos framework as well as the standard Monte Carlo scheme, and can be used for efficient, accurate prediction of robot dynamics.en_US
dc.description.sponsorshipUnited States. Army Research Office (contract number W912HZ-08-C-0062)en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/IROS.2009.5354420en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceIEEEen_US
dc.titleA multi-element generalized polynomial chaos approach to analysis of mobile robot dynamics under uncertaintyen_US
dc.typeArticleen_US
dc.identifier.citationKewlani, G., and K. Iagnemma. “A Multi-Element generalized Polynomial Chaos approach to analysis of mobile robot dynamics under uncertainty.” Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on. 2009. 1177-1182. ©2009 Institute of Electrical and Electronics Engineers.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.departmentMassachusetts Institute of Technology. Laboratory for Manufacturing and Productivityen_US
dc.contributor.approverIagnemma, Karl
dc.contributor.mitauthorKewlani, Gaurav
dc.contributor.mitauthorIagnemma, Karl
dc.relation.journalIEEE/RSJ International Conference on Intelligent Robots and Systems, 2009. IROS 2009en_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsKewlani, Gaurav; Iagnemma, Karlen
dc.identifier.orcidhttps://orcid.org/0000-0001-6244-0069
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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