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dc.contributor.authorUd Din, Muhayy
dc.contributor.authorAkbari, Aliakbar
dc.contributor.authorRosell Gratacòs, Jan
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2018-01-24T13:25:49Z
dc.date.issued2017
dc.identifier.citationUd Din, M., Akbari, A., Rosell, J. Knowledge-Oriented Physics-Based Motion Planning for Grasping Under Uncertainty. A: Iberian Robotics Conference. "ROBOT 2017 : Third Iberian Robotics Conference, vol. 1". Sevilla: Springer, 2017, p. 502-515.
dc.identifier.isbn978-3-319-70832-4
dc.identifier.urihttp://hdl.handle.net/2117/113151
dc.description.abstractGrasping an object in unstructured and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exits. High-level knowledge and reasoning processes, as well as the allowing of interaction between objects, can enhance the planning efficiency in such environments. In this direction, this study proposes a knowledge-oriented physics-based motion planning approach for a hand-arm system that uses a high-level knowledge-based reasoning to partition the workspace into regions to both guide the planner and reason about the result of the dynamical interactions between rigid bodies. The proposed planner is a kinodynamic RRT that uses a region-biased state sampling strategy and a smart validity checker that takes into account uncertainty in the pose of the objects. Complex dynamical interactions along with possible physics-based constraints such as friction and gravity are handled by a physics engine that is used as the RRT state propagator. The proposal is validated for different scenarios in simulation and in a real environment using a 7-degree-of-freedom KUKA Lightweight robot equipped with a two-finger gripper. The results show a significant improvement in the success rate of the execution of the computed plan in the presence of object pose uncertainty.
dc.format.extent14 p.
dc.language.isoeng
dc.publisherSpringer
dc.subjectÀrees temàtiques de la UPC::Informàtica::Robòtica
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshRobots--Dynamics
dc.titleKnowledge-Oriented Physics-Based Motion Planning for Grasping Under Uncertainty
dc.typeConference report
dc.subject.lemacRobots--Dinàmica
dc.contributor.groupUniversitat Politècnica de Catalunya. SIR - Service and Industrial Robotics
dc.identifier.doi10.1007/978-3-319-70833-1_41
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-319-70833-1_41
dc.rights.accessRestricted access - publisher's policy
drac.iddocument21624718
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/DPI2013-40882-P
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/2PE/DPI2016-80077-R
dc.date.lift10000-01-01
upcommons.citation.authorUd Din, M.; Akbari, A.; Rosell, J.
upcommons.citation.contributorIberian Robotics Conference
upcommons.citation.pubplaceSevilla
upcommons.citation.publishedtrue
upcommons.citation.publicationNameROBOT 2017 : Third Iberian Robotics Conference, vol. 1
upcommons.citation.startingPage502
upcommons.citation.endingPage515


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