AI training for application to industrial robotics: trajectory generation for neural network tuning
| dc.contributor.author | Merino Olagüe, Mikel | |
| dc.contributor.author | Ibarrola Chamizo, Javier | |
| dc.contributor.author | Aginaga García, Jokin | |
| dc.contributor.author | Hualde Otamendi, Mikel | |
| dc.contributor.department | Ingeniería | es_ES |
| dc.contributor.department | Ingeniaritza | eu |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.date.accessioned | 2023-11-28T15:59:43Z | |
| dc.date.available | 2023-11-28T15:59:43Z | |
| dc.date.issued | 2023 | |
| dc.date.updated | 2023-11-28T15:31:20Z | |
| dc.description.abstract | In the present work robot trajectories are generated and kinematically simulated. Different data (joint coordinates, end effector position and orientation, images, etc.) are obtained in order to train a neural network suited for applications in robotics. The neural network has the goal of automatically generating trajectories based on a set of images and coordinates. For this purpose, trajectories are designed in two separate sections which are conveniently connected using Bezier curves, ensuring continuity up to accelerations. In addition, among the possible trajectories that can be carried out due to the different configurations of the robot, the most suitable ones have been selected avoiding collisions and singularities. The designed algorithm can be used in multiple applications by adapting its different parameters. | en |
| dc.description.sponsorship | This work was funded by the “Convocatoria de ayudas a proyectos de I+D del Gobierno de Navarra” under the projects with Ref. 0011–1365-2021–000080 and Ref. 0011–1411-2021–000023. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Merino, M., Ibarrola, J., Aginaga, J., Hualde, M. (2023) AI training for application to industrial robotics: trajectory generation for neural network tuning. En Vizán Idoipe, A., García Prada J. C. (Eds.), Proceedings of the XV Ibero-American Congress of Mechanical Engineering (pp. 405-411). Springer. https://doi.org/10.1007/978-3-031-38563-6_59. | en |
| dc.identifier.doi | 10.1007/978-3-031-38563-6_59 | |
| dc.identifier.isbn | 978-3-031-38562-9 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/46830 | |
| dc.language.iso | eng | en |
| dc.publisher | Springer | en |
| dc.relation.ispartof | Vizán Idoipe, A., García Prada, J.C. (eds). Proceedings of the XV Ibero-American Congress of Mechanical Engineering. IACME 2022. Springer, 2023 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/Gobierno de Navarra//0011–1365-2021–000080/ | |
| dc.relation.projectID | info:eu-repo/grantAgreement/Gobierno de Navarra//0011–1411-2021–000023/ | |
| dc.relation.publisherversion | https://doi.org/10.1007/978-3-031-38563-6_59 | |
| dc.rights | © The Author(s) 2023. This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Industrial robotics | en |
| dc.subject | Trajectory planning | en |
| dc.subject | Artificial intelligence | en |
| dc.title | AI training for application to industrial robotics: trajectory generation for neural network tuning | en |
| dc.type | info:eu-repo/semantics/conferenceObject | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
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