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Randomized physics-based motion planning for grasping in cluttered and uncertain environments
dc.contributor.author | Muhayy, Uddin |
dc.contributor.author | Moll, Mark |
dc.contributor.author | Kavraki, Lydia |
dc.contributor.author | Rosell Gratacòs, Jan |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.date.accessioned | 2018-01-24T06:31:35Z |
dc.date.available | 2018-01-24T06:31:35Z |
dc.date.issued | 2018-04-01 |
dc.identifier.citation | Muhayyuddin, Moll, M., Kavraki, L., Rosell, J. Randomized physics-based motion planning for grasping in cluttered and uncertain environments. "IEEE robotics and automation letters", 1 Abril 2018, vol. 3, núm. 2, p. 712-719. |
dc.identifier.issn | 2377-3766 |
dc.identifier.uri | http://hdl.handle.net/2117/113122 |
dc.description | © 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works |
dc.description.abstract | Planning motions to grasp an object in cluttered and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exist and objects obstructing the way are required to be carefully grasped and moved out. This letter takes a different approach and proposes to address this problem by using a randomized physics-based motion planner that permits robot–object and object–object interactions. The main idea is to avoid an explicit high-level reasoning of the task by providing the motion planner with a physics engine to evaluate possible complex multibody dynamical interactions. The approach is able to solve the problem in complex scenarios, also considering uncertainty in the objects’ pose and in the contact dynamics. The work enhances the state validity checker, the control sampler, and the tree exploration strategy of a kinodynamic motion planner called KPIECE. The enhanced algorithm, called p-KPIECE, has been validated in simulation and with real experiments. The results have been compared with an ontological physics-based motion planner and with task and motion planning approaches, resulting in a significant improvement in terms of planning time, success rate, and quality of the solution path. |
dc.format.extent | 8 p. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Robòtica |
dc.subject.lcsh | Robots--Control systems |
dc.subject.other | Motion and path planning |
dc.subject.other | manipulation planning |
dc.subject.other | physics-based planning |
dc.subject.other | planning under uncertainties. |
dc.title | Randomized physics-based motion planning for grasping in cluttered and uncertain environments |
dc.type | Article |
dc.subject.lemac | Robots -- Sistemes de control |
dc.contributor.group | Universitat Politècnica de Catalunya. SIR - Service and Industrial Robotics |
dc.identifier.doi | 10.1109/LRA.2017.2783445 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://ieeexplore.ieee.org/document/8207585/ |
dc.rights.access | Open Access |
local.identifier.drac | 21860219 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/1PE/DPI2016-80077-R |
local.citation.author | Muhayyuddin; Moll, M.; Kavraki, L.; Rosell, J. |
local.citation.publicationName | IEEE robotics and automation letters |
local.citation.volume | 3 |
local.citation.number | 2 |
local.citation.startingPage | 712 |
local.citation.endingPage | 719 |
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