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dc.contributor.authorJaillet, Leonard Georges
dc.contributor.authorHoffman, Judy
dc.contributor.authorVan den Berg, Jur
dc.contributor.authorAbbeel, Pieter
dc.contributor.authorPorta Pleite, Josep Maria
dc.contributor.authorGoldberg, Ken
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.date.accessioned2012-02-17T19:00:01Z
dc.date.available2012-02-17T19:00:01Z
dc.date.created2011
dc.date.issued2011
dc.identifier.citationJaillet, L. [et al.]. EG-RRT: Environment-guided random trees for kinodynamic motion planning with uncertainty and obstacles. A: IEEE/RSJ International Conference on Intelligent Robots and Systems. "Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems". San Francisco: 2011, p. 2646-2652.
dc.identifier.urihttp://hdl.handle.net/2117/15234
dc.description.abstractExisting sampling-based robot motion planning methods are often inefficient at finding trajectories for kinodynamic systems, especially in the presence of narrow passages between obstacles and uncertainty in control and sensing. To address this, we propose EG-RRT, an Environment-Guided variant of RRT designed for kinodynamic robot systems that combines elements from several prior approaches and may incorporate a cost model based on the LQG-MP framework to estimate the probability of collision under uncertainty in control and sensing. We compare the performance of EG-RRT with several prior approaches on challenging sample problems. Results suggest that EG-RRT offers significant improvements in performance.
dc.format.extent7 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial artificial
dc.subject.lcshPredictive control
dc.subject.otherrobots
dc.titleEG-RRT: Environment-guided random trees for kinodynamic motion planning with uncertainty and obstacles
dc.typeConference report
dc.subject.lemacControl predictiu
dc.contributor.groupUniversitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
dc.identifier.doi10.1109/IROS.2011.6048409
dc.subject.inspecClassificació INSPEC::Cybernetics::Artificial intelligence::Planning (artificial intelligence)::Path planning
dc.relation.publisherversionhttp://dx.doi.org/10.1109/IROS.2011.6048409
dc.rights.accessOpen Access
local.identifier.drac8950730
dc.description.versionPostprint (author’s final draft)
local.citation.authorJaillet, L.; Hoffman, J.; van den Berg, J.; Abbeel, P.; Porta, J.M.; Goldberg, K.
local.citation.contributorIEEE/RSJ International Conference on Intelligent Robots and Systems
local.citation.pubplaceSan Francisco
local.citation.publicationNameProceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
local.citation.startingPage2646
local.citation.endingPage2652


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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain