Touch Detection with Low-cost Visual-based Sensor

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dc.contributorAutomática, Robótica y Visión Artificiales_ES
dc.contributor.authorCastaño Amorós, Julio-
dc.contributor.authorGil, Pablo-
dc.contributor.authorPuente Méndez, Santiago T.-
dc.contributor.otherUniversidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señales_ES
dc.contributor.otherUniversidad de Alicante. Instituto Universitario de Investigación Informáticaes_ES
dc.date.accessioned2021-11-03T16:13:07Z-
dc.date.available2021-11-03T16:13:07Z-
dc.date.issued2021-10-27-
dc.identifier.citationCastaño-Amoros, J.; Gil, P. and Puente, S. (2021). Touch Detection with Low-cost Visual-based Sensor. In Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS, ISBN 978-989-758-537-1, pages 136-142. DOI: 10.5220/0010699800003061es_ES
dc.identifier.isbn978-989-758-537-1-
dc.identifier.urihttp://hdl.handle.net/10045/119083-
dc.description.abstractRobotic manipulation continues being an unsolved problem. It involves many complex aspects, for example, perception tactile of different objects and materials, grasping control to plan the robotic hand pose, etc. Most of previous works on this topic used expensive sensors. This fact makes difficult the application in the industry. In this work, we propose a grip detection system using a low-cost visual-based tactile sensor known as DIGIT, mounted on a ROBOTIQ gripper 2F-140. We proved that a Deep Convolutional Network is able to detect contact or no contact. Capturing almost 12000 images with contact and no contact from different objects, we achieve 99% accuracy with never seen samples, in the best scenario. As a result, this system will allow us to implement a grasping controller for the gripper.es_ES
dc.description.sponsorshipResearch work was completely funded by the European Commission and FEDER through the COMMANDIA project (SOE2/P1/F0638), supported by Interreg-V Sudoe. Computer facilities used were provided by Valencia Government and FEDER through the IDIFEDER/2020/003.es_ES
dc.languageenges_ES
dc.publisherSciTePresses_ES
dc.rights© 2021 by SCITEPRESS – Science and Technology Publications, Lda. Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license (CC BY-NC-ND 4.0)es_ES
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dc.subjectTactile Sensinges_ES
dc.subjectRobotic Graspinges_ES
dc.subjectDiGIT Sensores_ES
dc.subjectConvolutional Neural Networkses_ES
dc.subject.otherIngeniería de Sistemas y Automáticaes_ES
dc.titleTouch Detection with Low-cost Visual-based Sensores_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.peerreviewedsies_ES
dc.identifier.doi10.5220/0010699800003061-
dc.relation.publisherversionhttps://doi.org/10.5220/0010699800003061es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
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