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ORIGINAL RESEARCH article

Front. Robot. AI
Sec. Biomedical Robotics
Volume 11 - 2024 | doi: 10.3389/frobt.2024.1460589
This article is part of the Research Topic Latest Trends in Bio-Inspired Medical Robotics: Structural Design, Manufacturing, Sensing, Actuation and Control View all 5 articles

Validations of Various In-Hand Object Manipulation Strategies Employing a Novel Tactile Sensor Developed for an Under-Actuated Robot Hand

Provisionally accepted
Avinash Singh Avinash Singh 1Massimilano Pinto Massimilano Pinto 1Petros Kaltsas Petros Kaltsas 1Salvatore Pirozzi Salvatore Pirozzi 2Shifa Sulaiman Shifa Sulaiman 1*Fanny Ficuciello Fanny Ficuciello 1*
  • 1 University of Naples Federico II, Naples, Italy
  • 2 University of Campania Luigi Vanvitelli, Caserta, Campania, Italy

The final, formatted version of the article will be published soon.

    Prisma Hand II is an under-actuated prosthetic hand developed at the University of Naples, Federico II to study in-hand manipulations during grasping activities. 3 motors equipped on the robotic hand drive 19 joints using elastic tendons. The operations of the hand are achieved by combining tactile hand sensing with under-actuation capabilities. The hand has the potential to be employed in both industrial and prosthetic applications due to its dexterous motion capabilities.However,currently there are no commercially available tactile sensors with compatible dimensions suitable for the prosthetic hand. Hence, in this work, we develop a novel tactile sensor designed based on an opto-electronic technology for the Prisma Hand II. The optimised dimensions of the proposed sensor made it possible to be integrated with the fingertips of the prosthetic hand.The output voltage obtained from the novel tactile sensor is used to determine optimum grasping forces and torques during in-hand manipulation tasks employing Neural Networks (NNs). The grasping force values obtained using a Convolutional Neural Network (CNN) and an Artificial Neural Network (ANN) are compared based on Mean Square Error (MSE) values to find out a better training network for the tasks. The tactile sensing capabilities of the proposed novel sensing method are presented and compared in simulation studies and experimental validations using various hand manipulation tasks. The developed tactile sensor is found to be showcasing a better performance compared to previous version of the sensor used in the hand.

    Keywords: Under actuation, In-hand manipulation, tactile sensing, Neural Networks Architectures, Control

    Received: 06 Jul 2024; Accepted: 02 Sep 2024.

    Copyright: © 2024 Singh, Pinto, Kaltsas, Pirozzi, Sulaiman and Ficuciello. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Shifa Sulaiman, University of Naples Federico II, Naples, Italy
    Fanny Ficuciello, University of Naples Federico II, Naples, Italy

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.