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dc.contributor.authorBasurto Hornillos, Nuño 
dc.contributor.authorCambra Baseca, Carlos 
dc.contributor.authorHerrero Cosío, Álvaro 
dc.date.accessioned2022-09-14T08:23:19Z
dc.date.available2022-09-14T08:23:19Z
dc.date.issued2022-05
dc.identifier.issn1433-7541
dc.identifier.urihttp://hdl.handle.net/10259/6847
dc.description.abstractIn robotic systems, both software and hardware components are equally important. However, scant attention has been devoted until now in order to detect anomalies/failures affecting the software component of robots while many proposals exist aimed at detecting physical anomalies. To bridge this gap, the present paper focuses on the study of anomalies affecting the software performance of a robot by using a novel visualization tool. Unsupervised visualization methods from the machine learning field are applied in order to upgrade the recently proposed Hybrid Unsupervised Exploratory Plots (HUEPs). Furthermore, Curvilinear Component Analysis and t-distributed stochastic neighbor embedding are added to the original HUEPs formulation and comprehensively compared. Furthermore, all the different combinations of HUEPs are validated in a real-life scenario. Thanks to this intelligent visualization of robot status, interesting conclusions can be obtained to improve anomaly detection in robot performance.en
dc.description.sponsorshipOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherSpringeres
dc.relation.ispartofPattern Analysis and Applications. 2022, V. 25, n. 2, p. 271-283en
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectSmart roboticsen
dc.subjectComponent-based robot softwareen
dc.subjectPerformance monitoringen
dc.subjectAnomaly detectionen
dc.subjectMachine learningen
dc.subjectUnsupervised visualizationen
dc.subjectClusteringen
dc.subjectExploratory projection pursuiten
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleA visual tool for monitoring and detecting anomalies in robot performanceen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1007/s10044-021-01053-0es
dc.identifier.doi10.1007/s10044-021-01053-0
dc.identifier.essn1433-755X
dc.journal.titlePattern Analysis and Applicationsen
dc.volume.number25es
dc.issue.number2es
dc.page.initial271es
dc.page.final283es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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