Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications

Tao Jin, Zhongda Sun, Long Li, Quan Zhang, Minglu Zhu, Zixuan Zhang, Guangjie Yuan, Tao Chen, Yingzhong Tian (), Xuyan Hou () and Chengkuo Lee ()
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Tao Jin: Shanghai University
Zhongda Sun: National University of Singapore
Long Li: Shanghai University
Quan Zhang: Shanghai University
Minglu Zhu: National University of Singapore
Zixuan Zhang: National University of Singapore
Guangjie Yuan: Shanghai University
Tao Chen: Soochow University
Yingzhong Tian: Shanghai University
Xuyan Hou: Harbin Institute of Technology
Chengkuo Lee: National University of Singapore

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract Designing efficient sensors for soft robotics aiming at human machine interaction remains a challenge. Here, we report a smart soft-robotic gripper system based on triboelectric nanogenerator sensors to capture the continuous motion and tactile information for soft gripper. With the special distributed electrodes, the tactile sensor can perceive the contact position and area of external stimuli. The gear-based length sensor with a stretchable strip allows the continuous detection of elongation via the sequential contact of each tooth. The triboelectric sensory information collected during the operation of soft gripper is further trained by support vector machine algorithm to identify diverse objects with an accuracy of 98.1%. Demonstration of digital twin applications, which show the object identification and duplicate robotic manipulation in virtual environment according to the real-time operation of the soft-robotic gripper system, is successfully created for virtual assembly lines and unmanned warehouse applications.

Date: 2020
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DOI: 10.1038/s41467-020-19059-3

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