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Deep learning-assisted object recognition with hybrid triboelectric-capacitive tactile sensor

Engineering and Technology

Deep learning-assisted object recognition with hybrid triboelectric-capacitive tactile sensor

Y. Xie, H. Cheng, et al.

Explore the groundbreaking development of a hybrid tactile sensor that fuses triboelectric and capacitive sensing technologies, offering unparalleled object recognition capabilities. Achieving a remarkable 98.46% accuracy through deep learning, this innovation promises to enhance robotic perception and tactile intelligence. This research was conducted by Yating Xie, Hongyu Cheng, Chaocheng Yuan, Limin Zheng, Zhengchun Peng, and Bo Meng.

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Playback language: English
Abstract
This paper introduces a hybrid tactile sensor integrating triboelectric and capacitive sensing units based on porous PDMS. The triboelectric unit is sensitive to surface material and texture, while the capacitive unit responds to object hardness. Combining signals from both units enables object recognition, even differentiating between the same object in different states. Deep learning enhances the sensor's accuracy, achieving 98.46% recognition accuracy for 12 samples. This sensor shows promise for robotic perception and tactile intelligence.
Publisher
Microsystems & Nanoengineering
Published On
Jan 01, 2024
Authors
Yating Xie, Hongyu Cheng, Chaocheng Yuan, Limin Zheng, Zhengchun Peng, Bo Meng
Tags
tactile sensor
triboelectric sensing
capacitive sensing
object recognition
deep learning
robotic perception

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