Learning-enhanced electronic skin for tactile sensing on deformable surface based on electrical impedance tomography

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Electrical Impedance Tomography (EIT)-based tac-tile sensors offer cost-effective and scalable solutions for robotic sensing, especially promising for soft robots. However a ma-jor 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 disen-tangle 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 defor-mations 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 image errors (0.0107 - 0.0805). Experimental validations, using a hydrogel-based EIT e-skin under various deformation scenarios, further demonstrated the effectiveness of the proposed approach in real-world settings. The findings could underpin enhanced tactile interaction in soft and highly deformable robotic applications.
Original languageEnglish
Number of pages10
JournalIEEE Transactions on Instrumentation and Measurement
Publication statusAccepted/In press - 12 Dec 2024

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