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dc.contributor.authorUd Din, Muhayy
dc.contributor.authorRosell Gratacòs, Jan
dc.contributor.authorBukhari, Sohail
dc.contributor.authorAhmad, Mansoor
dc.contributor.authorQazi, Wajahat Mahmood
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2021-02-09T11:26:36Z
dc.date.available2021-02-09T11:26:36Z
dc.date.issued2020
dc.identifier.citationUd Din, M. [et al.]. A lightweight perception module for planning purposes. A: IEEE International Conference on Emerging Technologies and Factory Automation. "2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): Proceedings: Vienna, Austria - Hybrid: 08-11 September, 2020". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 1277-1280. ISBN 978-1-7281-8957-4. DOI 10.1109/ETFA46521.2020.9212008.
dc.identifier.isbn978-1-7281-8957-4
dc.identifier.urihttp://hdl.handle.net/2117/337586
dc.description© 2020 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.abstractSensing is an essential component for robots to perform the manipulation tasks in real environments. This study proposes a lightweight deep-learning-based sensing modules which allows the robots to automatically model the workspace for manipulation planning. This sensing module is developed as a part of our ongoing manipulation planning framework. It will be used to enhance the sensing accuracy and make it capable of planning the manipulation tasks in real environments. The retrained model is further trained over commonly used objects to enhance the prediction accuracy.
dc.format.extent4 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
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::Robòtica
dc.subject.lcshMachine learning
dc.titleA lightweight perception module for planning purposes
dc.typeConference lecture
dc.subject.lemacRobots -- Sistemes de control
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. SIR - Service and Industrial Robotics
dc.identifier.doi10.1109/ETFA46521.2020.9212008
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9212008
dc.rights.accessOpen Access
local.identifier.drac29706834
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/ DPI2016-80077-R
local.citation.authorUd Din, M.; Rosell, J.; Bukhari, S.; Ahmad, M.; Qazi, W.
local.citation.contributorIEEE International Conference on Emerging Technologies and Factory Automation
local.citation.publicationName2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): Proceedings: Vienna, Austria - Hybrid: 08-11 September, 2020
local.citation.startingPage1277
local.citation.endingPage1280


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Attribution-NonCommercial-NoDerivs 3.0 Spain
Salvo que se indique lo contrario, los contenidos de esta obra estan sujetos a la licencia de Creative Commons : Reconocimiento-NoComercial-SinObraDerivada 3.0 España