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dc.contributor.authorInziarte Hidalgo, Ibai
dc.contributor.authorUriarte Gallastegui, Irantzu ORCID
dc.contributor.authorFernández Gámiz, Unai
dc.contributor.authorSorrosal Yarritu, Gorka
dc.contributor.authorZulueta Guerrero, Ekaitz
dc.date.accessioned2023-02-24T15:52:51Z
dc.date.available2023-02-24T15:52:51Z
dc.date.issued2023-02-06
dc.identifier.citationMathematics 11(4) : (2023) // Article ID 828es_ES
dc.identifier.issn2227-7390
dc.identifier.urihttp://hdl.handle.net/10810/60073
dc.description.abstractThis research proposes an optimal robotic arm speed shape in neurological surgery to minimise a cost functional that uses an adaptive scheme to determine the brain tissue force. Until now, there have been no studies or theories on the shape of the robotic arm speed in such a context. The authors have applied a robotic arm with optimal speed control in neurological surgery. The results of this research are as follows: In this article, the authors propose a control scheme that minimises a cost functional which depends on the position error, trajectory speed and brain tissue force. This work allowed us to achieve an optimal speed shape or trajectory to reduce brain retraction damage during surgery. The authors have reached two main conclusions. The first is that optimal control techniques are very well suited for robotic control of neurological surgery. The second conclusion is that several studies on functional cost parameters are needed to achieve the best trajectory speed of the robotic arm. These studies could attempt to optimise the functional cost parameters and provide a mechanical characterisation of brain tissue based on real data.es_ES
dc.description.sponsorshipThe authors were supported by the government of the Basque Country through the research grant ELKARTEK KK-2021/00014 BASQNET (Estudio de nuevas técnicas de inteligencia artificial basadas en Deep Learning dirigidas a la optimización de procesos industriales). This study has also been conducted partially under the framework of the project MODELO (Grants for R&D projects—2021 and supported by the European Regional Development Funds), ADA project (Grants for R&D projects—2022 and supported by the European Regional Development Funds).es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectneurosurgical roboticses_ES
dc.subjectoptimal controles_ES
dc.subjectoptimal speed shapees_ES
dc.titleRobotic-Arm-Based Force Control in Neurosurgical Practicees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.date.updated2023-02-24T14:08:52Z
dc.rights.holder© 2023 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/2227-7390/11/4/828es_ES
dc.identifier.doi10.3390/math11040828
dc.departamentoesIngeniería de sistemas y automática
dc.departamentoesIngeniería Energética
dc.departamentoesIngeniería mecánica
dc.departamentoeuIngeniaritza mekanikoa
dc.departamentoeuEnergia Ingenieritza
dc.departamentoeuSistemen ingeniaritza eta automatika


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© 2023 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 © 2023 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/).