Detección de agarre de objetos desconocidos con sensor visual-táctil

  1. Julio Castaño-Amorós 1
  2. Pablo Gil 1
  3. Ines Fernández 1
  4. Santiago Puente 1
  1. 1 Universitat d'Alacant
    info

    Universitat d'Alacant

    Alicante, España

    ROR https://ror.org/05t8bcz72

Book:
XLII Jornadas de Automática: libro de actas, Castellón, 1 a 3 de septiembre de 2021

Publisher: Universitat Jaume I ; Servizo de Publicacións ; Universidade da Coruña ; Comité Español de Automática

ISBN: 978-84-9749-804-3

Year of publication: 2021

Pages: 535-541

Congress: Jornadas de Automática (42. 2021. Castellón)

Type: Conference paper

DIALNET GOOGLE SCHOLAR lock_openRUC editor

Abstract

Robotic manipulation is still a challenge. It involves many complex aspects such as tactile perception of a wide variety of objects and materials, grip control to plan robotic hand posture, etc. Most of the previous work used expensive sensors for tactile perception tasks. This fact implies difficulty in transferring application results to industry. In this work, a grip detection system is proposed. It uses DIGIT sensors based on low-cost image technology. The method developed, which is based on deep Convolutional Neural Networks (CNN), is capable of detecting contact or non-contact, with success rates greater than 95 %. The system has been trained and tested on our own dataset, composed of more than 16,000 images from different object grasping, also using several DIGIT units. The detection method is part of a grip controller used with a ROBOTIQ 2F-140 gripper.

Data source: Dialnet