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Conference Paper: Efficient global penetration depth computation for articulated models

TitleEfficient global penetration depth computation for articulated models
Authors
KeywordsConfiguration space
Articulated models
Penetration depth
Support vector machine
Issue Date2016
PublisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/cad
Citation
The 2015 SIAM Conference on Geometric and Physical Modeling (GD/SPM15), Salt Lake City, UT., 12-14 October 2015. In Computer-Aided Design, 2015, v. 70, p. 116-125 How to Cite?
AbstractWe present an algorithm for computing the global penetration depth between an articulated model and an obstacle or between the distinctive links of an articulated model. In so doing, we use a formulation of penetration depth derived in configuration space. We first compute an approximation of the boundary of the obstacle regions using a support vector machine in a learning stage. Then, we employ a nearest neighbor search to perform a runtime query for penetration depth. The computational complexity of the runtime query depends on the number of support vectors, and its computational time varies from 0.03 to 3 milliseconds in our benchmarks. We can guarantee that the configuration realizing the penetration depth is penetration free, and the algorithm can handle general articulated models. We tested our algorithm in robot motion planning and grasping simulations using many high degree of freedom (DOF) articulated models. Our algorithm is the first to efficiently compute global penetration depth for high-DOF articulated models.
Persistent Identifierhttp://hdl.handle.net/10722/212283
ISSN
2017 Impact Factor: 2.947
2015 SCImago Journal Rankings: 1.078
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorTian, H-
dc.contributor.authorZhang, X-
dc.contributor.authorWang, C-
dc.contributor.authorPan, J-
dc.contributor.authorManocha, D-
dc.date.accessioned2015-07-21T02:30:45Z-
dc.date.available2015-07-21T02:30:45Z-
dc.date.issued2016-
dc.identifier.citationThe 2015 SIAM Conference on Geometric and Physical Modeling (GD/SPM15), Salt Lake City, UT., 12-14 October 2015. In Computer-Aided Design, 2015, v. 70, p. 116-125-
dc.identifier.issn0010-4485-
dc.identifier.urihttp://hdl.handle.net/10722/212283-
dc.description.abstractWe present an algorithm for computing the global penetration depth between an articulated model and an obstacle or between the distinctive links of an articulated model. In so doing, we use a formulation of penetration depth derived in configuration space. We first compute an approximation of the boundary of the obstacle regions using a support vector machine in a learning stage. Then, we employ a nearest neighbor search to perform a runtime query for penetration depth. The computational complexity of the runtime query depends on the number of support vectors, and its computational time varies from 0.03 to 3 milliseconds in our benchmarks. We can guarantee that the configuration realizing the penetration depth is penetration free, and the algorithm can handle general articulated models. We tested our algorithm in robot motion planning and grasping simulations using many high degree of freedom (DOF) articulated models. Our algorithm is the first to efficiently compute global penetration depth for high-DOF articulated models.-
dc.languageeng-
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/cad-
dc.relation.ispartofComputer-Aided Design-
dc.subjectConfiguration space-
dc.subjectArticulated models-
dc.subjectPenetration depth-
dc.subjectSupport vector machine-
dc.titleEfficient global penetration depth computation for articulated models-
dc.typeConference_Paper-
dc.identifier.emailPan, J: jpan@cs.hku.hk-
dc.identifier.authorityPan, J=rp01984-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.cad.2015.07.007-
dc.identifier.scopuseid_2-s2.0-84942501209-
dc.identifier.hkuros244995-
dc.identifier.hkuros244994-
dc.identifier.volume70-
dc.identifier.spage116-
dc.identifier.epage125-
dc.identifier.isiWOS:000362925100012-
dc.publisher.placeUnited Kingdom-

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