Layered e-skin for Shear Sensing

2026-09-18

AI Summary

本文通过设计一种双层力敏电阻阵列结合深度学习模型，解决了机器人指尖的高分辨率压力映射和剪切力估计问题。

Abstract

This paper presents a stacked two-layer force-sensing resistor (FSR) array designed for robotic fingertips that combines high-resolution pressure mapping with shear-force estimation. A compliant lattice elastomer spacer converts shear loading into a measurable inter-layer displacement, producing relative center-of-pressure (CoP) shifts between layers. A physics-based moment balance links inter-layer CoP displacement to shear force, while an end-to-end CNN--GRU model captures nonlinear effects from load-dependent compression and contact redistribution. This model, with both layers as input, achieves coefficients of determination $R^2 = 0.914$ for $F_x$ and $R^2 = 0.944$ for $F_y$, consistently outperforming single-layer baselines for shear-force estimation. Robotic manipulation experiments show that, for contact-motion tracking, the deep layer tracks the translation and rotation imposed by the robot arm, whereas the superficial layer tracks the slip at the contact surface. Transient changes in the difference between the total pressure responses of the two layers provide the best slip-event detection performance among the tested cues. These results demonstrate that two-layer FSR arrays can provide three-axis force estimation, contact-motion tracking, and slip-event detection beyond conventional normal-force sensing.

Problem

• shear force
• pressure mapping
• force-sensing resistor (FSR)
• robotic fingertips
• slip-event detection

Innovation

• two-layer FSR array
• shear sensing
• end-to-end CNN-GRU model
• contact-motion tracking
• slip-event detection