分层电子皮肤用于剪切力感知
Layered e-skin for Shear Sensing
- Queen Mary University of London（伦敦玛丽女王大学）
- The Imperial College of Science, Technology and Medicine（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出双层FSR阵列电子皮肤，通过层间位移与CNN-GRU模型实现剪切力估计，在机器人指尖上达到高精度，并支持接触运动跟踪与滑移检测。
中文摘要 AI 辅助
本文提出了一种堆叠式双层力敏电阻（FSR）阵列，专为机器人指尖设计，结合了高分辨率压力映射与剪切力估计。一个柔顺的晶格弹性体间隔层将剪切载荷转换为可测量的层间位移，从而在两层之间产生相对压力中心（CoP）位移。基于物理的力矩平衡将层间CoP位移与剪切力联系起来，而端到端的CNN--GRU模型则捕捉了由载荷相关压缩和接触重新分布引起的非线性效应。该模型以两层作为输入，在$F_x$上实现了决定系数$R^2 = 0.914$，在$F_y$上实现了$R^2 = 0.944$，在剪切力估计方面始终优于单层基线。机器人操作实验表明，在接触运动跟踪方面，深层跟踪机器人手臂施加的平移和旋转，而浅层则跟踪接触表面的滑动。两层总压力响应之差的瞬态变化在测试的线索中提供了最佳的滑移事件检测性能。这些结果表明，双层FSR阵列可以提供三轴力估计、接触运动跟踪和滑移事件检测，超越了传统的法向力感知。
英文摘要
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.
