Singh Avinash, Pinto Massimilano, Kaltsas Petros, Pirozzi Salvatore, Sulaiman Shifa, Ficuciello Fanny
Department of Information Technology and Electrical Engineering, Università degli Studi di Napoli Federico II, Napoli, Italy.
Department of Engineering, Università degli Studi della Campania "Luigi Vanvitelli", Caserta, Italy.
Front Robot AI. 2024 Sep 26;11:1460589. doi: 10.3389/frobt.2024.1460589. eCollection 2024.
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.
普里兹马二代手部是那不勒斯费德里科二世大学研发的一款欠驱动假手，用于研究抓握活动中的手部内操作。该机器人手配备的3个电机通过弹性肌腱驱动19个关节。手部的操作通过将触觉手部传感与欠驱动能力相结合来实现。由于其灵活的运动能力，该假手有潜力应用于工业和假肢领域。然而，目前没有尺寸合适且与之兼容的商用触觉传感器可用于该假手。因此，在这项工作中，我们为普里兹马二代手部开发了一种基于光电技术设计的新型触觉传感器。所提出传感器的优化尺寸使其能够与假手的指尖集成。从新型触觉传感器获得的输出电压用于通过神经网络（NNs）确定手部内操作任务期间的最佳抓握力和扭矩。基于均方误差（MSE）值比较使用卷积神经网络（CNN）和人工神经网络（ANN）获得的抓握力值，以找出更适合这些任务的训练网络。在使用各种手部操作任务的模拟研究和实验验证中展示并比较了所提出的新型传感方法的触觉传感能力。结果发现，与该假手之前使用的传感器版本相比，所开发的触觉传感器表现出更好的性能。
