Intelligent soft robotic fingers with multi-modality perception ability

Tongjing Wu; Haitao Deng; Zhongda Sun; Xinran Zhang; Chengkuo Lee; Xiaosheng Zhang

doi:10.1016/j.isci.2023.107249

Intelligent soft robotic fingers with multi-modality perception ability

iScience. 2023 Jun 27;26(8):107249. doi: 10.1016/j.isci.2023.107249. eCollection 2023 Aug 18.

Authors

Tongjing Wu^{1

2}, Haitao Deng¹, Zhongda Sun², Xinran Zhang¹, Chengkuo Lee², Xiaosheng Zhang¹

Affiliations

¹ School of Integrated Circuit Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
² Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore.

Abstract

In the context of industry 4.0, automatic sorting is becoming prevalent in production lines. Herein, we developed a bionic sensing system to achieve real-time object recognition. The system consists of 9 single-layer triboelectric nanogenerators (SL-TENGs) as touch sensors and 3 comb-shaped TENGs (CS-TENGs) as bending sensors, with a sensitivity of 110 V/kPa and stable output after 20,000 press cycles. These sensors were attached to a manipulator composed of three soft actuators, serving as soft robotic fingers. An enhanced electrical output of these sensors was achieved successfully, demonstrating their feasibility in detecting grasping location, contact pressure, and bending curvature. A one-dimensional convolutional neural network (1D-CNN) with 98.96% accuracy extracted information from the sensors, enabling the manipulator to serve as an intelligent sensing system with multi-modality perception ability. This robotic manipulator successfully integrated TENG-based self-powered sensors, soft actuators, and artificial intelligence, demonstrating the potential for future digital twin applications, particularly in automatic component sorting.

Keywords: Bionics; Control engineering; Robotics.