Touch Detection with Low-cost Visual-based Sensor

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Títol: Touch Detection with Low-cost Visual-based Sensor
Autors: Castaño Amorós, Julio | Gil, Pablo | Puente Méndez, Santiago T.
Grups d'investigació o GITE: Automática, Robótica y Visión Artificial
Centre, Departament o Servei: Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Investigación Informática
Paraules clau: Tactile Sensing | Robotic Grasping | DiGIT Sensor | Convolutional Neural Networks
Àrees de coneixement: Ingeniería de Sistemas y Automática
Data de publicació: 27-d’octubre-2021
Editor: SciTePress
Citació bibliogràfica: Castaño-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: 10.5220/0010699800003061
Resum: Robotic 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.
Patrocinadors: Research 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.
ISBN: 978-989-758-537-1
DOI: 10.5220/0010699800003061
Idioma: eng
Tipus: info:eu-repo/semantics/conferenceObject
Drets: © 2021 by SCITEPRESS – Science and Technology Publications, Lda. Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license (CC BY-NC-ND 4.0)
Revisió científica: si
Versió de l'editor:
Apareix a la col·lecció: INV - AUROVA - Comunicaciones a Congresos Internacionales

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