Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/114665
DC FieldValueLanguage
dc.contributor.authorBabcinschi, Mihail-
dc.contributor.authorCruz, Francisco-
dc.contributor.authorDuarte, Nicole-
dc.contributor.authorSantos, Silvia-
dc.contributor.authorAlves, Samuel-
dc.contributor.authorNeto, Pedro-
dc.date.accessioned2024-04-04T10:24:22Z-
dc.date.available2024-04-04T10:24:22Z-
dc.date.issued2023-
dc.identifier.issn2195-4356pt
dc.identifier.issn2195-4364pt
dc.identifier.urihttps://hdl.handle.net/10316/114665-
dc.description.abstractThere is a great demand for the robotization of manufacturing pro-cesses featuring monotonous labor. Some manufacturing tasks requiring specific skills (welding, painting, etc.) suffer from a lack of workers. Robots have been used in these tasks, but their flexibility is limited since they are still difficult to program/re-program by non-experts, making them inaccessible to most compa-nies. Robot offline programming (OLP) is reliable. However, generated paths directly from CAD/CAM do not include relevant parameters representing human skills such as robot end-effector orientations and velocities. This paper presents an intuitive robot programming system to capture human manufacturing skills and transform them into robot programs. Demonstrations from human skilled workers are recorded using a magnetic tracking system attached to the worker tools. Collected data include the orientations and velocity of the working paths. Positional data are extracted from CAD/CAM since its error when captured by the magnetic tracker, is significant. Paths poses are transformed in Cartesian space and validated in a simulation environment. Robot programs are generated and transferred to the real robot. Experiments on the process of glass adhesive application demonstrated the intuitiveness to use and effectiveness of the pro-posed framework in capturing human skills and transferring them to the robot.pt
dc.language.isoengpt
dc.publisherSpringer Naturept
dc.relationEuropean Community’s HORIZON 2020 programme under grant agreement No. 958303 (PENELOPE)pt
dc.relationUIDB/00285/2020pt
dc.rightsopenAccesspt
dc.subjectRoboticspt
dc.subjectManufacturing Skillspt
dc.subjectHuman-Robot Interfacespt
dc.titleIntuitive Robot Programming by Capturing Human Manufacturing Skills: A Framework for the Process of Glass Adhesive Applicationpt
dc.typearticle-
degois.publication.firstPage677pt
degois.publication.lastPage684pt
degois.publication.titleLecture Notes in Mechanical Engineeringpt
dc.peerreviewedyespt
dc.identifier.doi10.1007/978-3-031-17629-6_71pt
dc.date.embargo2023-01-01*
uc.date.periodoEmbargo0pt
item.fulltextCom Texto completo-
item.grantfulltextopen-
item.languageiso639-1en-
item.openairetypearticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.researchunitCEMMPRE - Centre for Mechanical Engineering, Materials and Processes-
crisitem.author.researchunitCEMMPRE - Centre for Mechanical Engineering, Materials and Processes-
crisitem.author.researchunitISR - Institute of Systems and Robotics-
crisitem.author.researchunitCEMMPRE - Centre for Mechanical Engineering, Materials and Processes-
crisitem.author.researchunitCEMMPRE - Centre for Mechanical Engineering, Materials and Processes-
crisitem.author.parentresearchunitUniversity of Coimbra-
crisitem.author.orcid0000-0002-5392-099X-
crisitem.author.orcid0000-0003-2177-5078-
Appears in Collections:FCTUC Eng.Mecânica - Artigos em Revistas Internacionais
I&D CEMMPRE - Artigos em Revistas Internacionais
Show simple item record

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.