Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications

By Tao Jin, Zhongda Sun, Long Li, Quan Zhang, Minglu Zhu, Zixuan Zhang, Guangjie Yuan, Tao Chen, Yingzhong Tian, X. Hou and 1 more. Nature Communications, 2020

575 citations recorded by the source corpus.

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. Designing efficient sensors for human machine interaction remains a challenge. Here, the authors present a soft robotic fingers system based on a triboelectric nanogenerator (L-TENG) sensor to capture the continuous motion of soft gripper and a soft tactile (T-TENG) sensor for tactile sensing, that can achieve an object recognition accuracy of 98.1%.

Subfields: Biomedical Engineering, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/2ded44b2a8691ad09d60e086ebfa79cbaab4980a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Chengkuo Lee
2. Feng Wen
3. Tianyiyi He
4. Zixuan Zhang
5. Qijun Sun
6. Xiaole Cao
7. Christian Brecher
8. Yao Xiong

In Cognitive Neuroscience

1. Chengkuo Lee
2. Feng Wen
3. Tianyiyi He
4. Zixuan Zhang
5. Xinge Guo
6. Long Liu

Citations in the corpus

• A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics (2021, methodology): “Clustering algorithms, such as principle component analysis (PCA) [260,274] and k-means methods [205] and generative models using generative adversarial network (GAN) [137,150] and variational autoencoders (VAE) [150] all use unlabeled data at the training stage, thus falling into the category of un”
• AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove (2021, methodology): “In addition to the general HMIs using resistive and capacitive techniques 32 – 37 , self-powered triboelectric HMIs are also extensively investigated with different prototypes, such as touchpad, wrist band, sock, and glove 38 – 46 .”
• Multidiscipline Applications of Triboelectric Nanogenerators for the Intelligent Era of Internet of Things (2022, background): “These subtle features hidden behind the real-time signal spectrum could be automatically extracted for AI applications, e.g., gesture recognition [28], texture recognition [29], object classification [30, 31], VR [32], and digital twins [33].”
• Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses (2023, methodology): “…falling based on acceleration Copyright (2020) American Chemical Society. c Digital twin unmanned warehouse system with machine learning algorithms [171] Copyright (2020) The Authors. d AI-enabled sign language recognition and communication system [172] Copyright (2021) The Authors and angular…”