Publication:
Gaussian process regression for forward and inverse kinematics of a soft robotic arm

Simple item page
dc.affiliation.dptoUC3M. Departamento de Ingeniería de Sistemas y Automáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Robótica (Robotics Lab)es
dc.contributor.authorRelaño Gibert, Carlos
dc.contributor.authorMuñoz Mendi, Javier
dc.contributor.authorMonje Micharet, Concepción Alicia
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2023-12-19T18:02:44Z
dc.date.available2023-12-19T18:02:44Z
dc.date.issued2023-11-01
dc.description.abstractThe use of soft robotics to perform tasks and interact with the environment requires good system identification. Data-driven methods offer a promising alternative where traditional analytical model-based techniques have proven insufficient. However, their use has been limited and under-explored in soft robotics. The novelty of this research lies in the application of Gaussian processes to soft robotics and the exploration of approximate Gaussian processes (AGP) and deep Gaussian processes (DGP) methods. It highlights the advantages of Gaussian processes in modeling uncertainty, incorporating prior knowledge, and handling complex systems. This is achieved through the identification of the forward and inverse kinematics of a two-degree-of-freedom soft robotic arm actuated by three tendons. A comparison is made between different configurations using Gaussian processes and the results are also compared with those obtained from the analytical model of the kinematics and an artificial neural network (ANN). The research contributes to the development of more efficient and accurate techniques for system identification, kinematics modeling, and control in soft robotics.en
dc.description.sponsorshipAcknowledgments. This research has been supported by the project SOFIA, with reference PID2020-113194GB-I00, funded by the Spanish Ministry of Economics, Industry and Competitiveness (MCIN/ AEI /10.13039/501100011033), and the project Desarrollo de articulaciones blandas para aplicaciones robóticas, with reference IND2020/IND-1739, funded by the Community of Madrid (CAM) (Department of Education and Research). All authors approved the version of the manuscript to be published.en
dc.description.statusPublicadoes
dc.format.extent15
dc.identifier.bibliographicCitationRelaño Carlos; Muñoz, Javier; Monje Concepción A. "Gaussian process regression for forward and inverse kinematics of a soft robotic arm". Engineering Applications of Artificial Intelligence 126 (2023) 107174 (15p.) https://doi.org/10.1016/j.engappai.2023.107174en
dc.identifier.doihttps://doi.org/10.1016/j.engappai.2023.107174
dc.identifier.issn0952-1976
dc.identifier.publicationfirstpage107174-1
dc.identifier.publicationissueNovember, 107174en
dc.identifier.publicationlastpage107174-15
dc.identifier.publicationtitleEngineering Applications of Artificial Intelligenceen
dc.identifier.publicationvolume126. Part Den
dc.identifier.urihttps://hdl.handle.net/10016/39121
dc.identifier.uxxiAR/0000033651
dc.language.isoengen
dc.publisherElsevier Ltd.en
dc.relation.datasethttps://doi.org/10.21950/Y4AN3E
dc.relation.projectIDComunidad de Madrid. IND2020/IND-17396es
dc.relation.projectIDGobierno de España. PID2020-113194GB-I00es
dc.rights© 2023 The Authors. Published by Elsevier Ltd.en
dc.rightsThis is an open access article under the CC BY-NC-ND licenseen
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaIngeniería Mecánicaes
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.otherSoft roboticsen
dc.subject.otherGaussian processesen
dc.subject.otherMachine learningen
dc.subject.otherIdentification of soft robotsen
dc.titleGaussian process regression for forward and inverse kinematics of a soft robotic armen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
engineering_EAAI_2023.pdf
Size:
5.65 MB
Format:
Adobe Portable Document Format
Download
Collections
DISA - LR - Artículos de Revistas