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dc.contributor.authorOutón Méndez, José Luis
dc.contributor.authorMerino Bermejo, Ibon
dc.contributor.authorVillaverde, Iván
dc.contributor.authorIbarguren Soldevilla, Aitor
dc.contributor.authorHerrero Cueva, Héctor
dc.contributor.authorDaelman, Paul
dc.contributor.authorSierra Araujo, Basilio ORCID
dc.date.accessioned2021-06-14T12:31:18Z
dc.date.available2021-06-14T12:31:18Z
dc.date.issued2021-05-27
dc.identifier.citationElectronics 10(11) : (2021) // Article ID 1276es_ES
dc.identifier.issn2079-9292
dc.identifier.urihttp://hdl.handle.net/10810/51859
dc.description.abstractIn modern industry there are still a large number of low added-value processes that can be automated or semi-automated with safe cooperation between robot and human operators. The European SHERLOCK project aims to integrate an autonomous industrial mobile manipulator (AIMM) to perform cooperative tasks between a robot and a human. To be able to do this, AIMMs need to have a variety of advanced cognitive skills like autonomous navigation, smart perception and task management. In this paper, we report the project’s tackle in a paradigmatic industrial application combining accurate autonomous navigation with deep learning-based 3D perception for pose estimation to locate and manipulate different industrial objects in an unstructured environment. The proposed method presents a combination of different technologies fused in an AIMM that achieve the proposed objective with a success rate of 83.33% in tests carried out in a real environment.es_ES
dc.description.sponsorshipThis research was funded by EC research project “SHERLOCK—Seamless and safe human-centered robotic applications for novel collaborative workplace”. Grant number: 820683 (https://www.sherlock-project.eu accessed on 12 March 2021).es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/820683es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectautonomous industrial mobile manipulatores_ES
dc.subjectdeep learninges_ES
dc.subjectroboticses_ES
dc.subjectperceptiones_ES
dc.subjectsensor fusiones_ES
dc.subjectautonomous navigationes_ES
dc.subjectcomputer visiones_ES
dc.subjectskillses_ES
dc.subjectstate machinees_ES
dc.titleA Real Application of an Autonomous Industrial Mobile Manipulator within Industrial Contextes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.date.updated2021-06-10T13:46:33Z
dc.rights.holder2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).es_ES
dc.relation.publisherversionhttps://www.mdpi.com/2079-9292/10/11/1276/htmes_ES
dc.identifier.doi10.3390/electronics10111276
dc.contributor.funderEuropean Commission
dc.departamentoesCiencia de la computación e inteligencia artificial
dc.departamentoeuKonputazio zientziak eta adimen artifiziala


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2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).