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Learning-enhanced Electronic Skin for Tactile Sensing on Deformable Surface Based on Electrical Impedance Tomography by Dong, Huazhi; Wu, Xiaopeng; Hu, Delin; Liu, Zhe; Giorgio-Serchi, Francesco; Yang, Yunjie is a scholarly article available to read on EtoBox.

What is Learning-enhanced Electronic Skin for Tactile Sensing on Deformable Surface Based on Electrical Impedance Tomography about?

Electrical Impedance Tomography (EIT)-based tactile sensors offer cost-effective and scalable solutions for robotic sensing, especially promising for soft robots. However a major issue of EIT-based tactile sensors when applied in highly deformable objects is their performance degradation due to surface deformations. This limitation stems from their inherent sensitivity to strain, which is particularly exacerbated in soft bodies, thus requiring dedicated data interpretation to disentangle the parameter being measured and the signal deriving from shape changes. This has largely limited their practical implementations. This paper presents a machine learning-assisted tactile sensing approach to address this challenge by tracking surface deformations and segregating this contribution in the signal readout during tactile sensing. We first capture the deformations of the target object, followed by tactile reconstruction using a deep learning model specifically designed to process and fuse EIT data and deformation information. Validations using numerical simulations achieved high correlation coefficients (0.9660 - 0.9999), peak signal-to-noise ratios (28.7221 - 55.5264 dB) and low relative

Author

Dong, Huazhi; Wu, Xiaopeng; Hu, Delin; Liu, Zhe; Giorgio-Serchi, Francesco; Yang, Yunjie

Published

2025

Language

EN