Tactile and textile skin technologies have become increasingly important for enhancing human-robot interaction and allowing robots to adapt to different environments. Despite notable advancements, there are ongoing challenges in skin signal processing, particularly in achieving both accuracy and speed in dynamic touch sensing.
This paper introduces a new framework that poses the touch sensing problem as an estimation problem of resistive sensory arrays. Utilizing a Regularized Least Squares objective function—which estimates the resistance distribution of the skin—we enhance the touch sensing accuracy and mitigate the ghosting effects, where false or misleading touches may be registered. Furthermore, our study presents a streamlined skin design that simplifies manufacturing processes without sacrificing performance. Experimental outcomes substantiate the effectiveness of our method, showing 26.9% improvement in multi-touch force-sensing accuracy for the tactile skin.
An overview of our proposed tactile skin sensing method. (a) Two textile pieces with conductive stripes are separately knitted. (b) The two textile pieces are sewn together orthogonally to create a grid of sensing cells. (c) The skin is modeled as a resistive sensory array. Our approach predicts force applied on the skin by estimating cell resistances using an Arduino board.
(a) Ghosting effects occur when an alternate path of current (shown in green) is formed for the sensing cell at the upper left corner. (b) Ohmmeter Configurations used in our approach.
Our approach to multi-touch force sensing using textile-based tactile skin comprises two stages: calibration and estimation. Given the readout data acquired from the Ohmmeter circuit, we estimate the cell resistances of the skin using our proposed resistance estimation algorithm and fit them to simple linear regression models for each cell.
Simulation results for varying wire resistances in tactile skin. Our method reduces ghosting effects compared to naive solutions.
Simulation results for tactile skin with variable cell resistances. Our optimization approach achieves lower error rates.
The Force Estimation experiment setup. (a) flat surface experiment setup, (b) curved surface experiment, and (c) two-layer design of the skin allows detection of forces as low as 1N.
Flat Surface Experiment. Our method achieved 27.3% lower multi-touch error compared to the naive approach.
Curved Surface Experiment. Our method achieved 26.4% lower multi-touch error compared to the naive approach.
Time series data showing our algorithm effectively mitigating ghosting effects caused by force applied on the same row or column.
@article{su2023optimizing,
author = {Su, Bo Ying and Wu, Yuchen and Wen, Chengtao and Liu, Changliu},
title = {Optimizing Multi-Touch Textile and Tactile Skin Sensing Through Circuit Parameter Estimation},
journal = {IEEE},
year = {2023},
}