Measuring a Soft Resistive Strain Sensor Array by Solving the Resistor Network Inverse Problem

TL;DR

This paper introduces a non-invasive method to measure deformation in soft resistive sensor networks by solving the inverse resistor network problem, enabling more efficient soft robotic sensing without complex wiring.

Contribution

The paper presents a novel algorithm for reconstructing resistor values in soft sensor networks from boundary measurements, reducing wiring complexity in soft robotics.

Findings

Algorithm accurately reconstructs resistor networks from boundary data.

The method effectively measures deformation modes in soft silicone sensors.

Reconstruction error depends on network size and measurement noise.

Abstract

Soft robotics is applicable to a variety of domains due to the adaptability offered by the soft and compliant materials. To develop future intelligent soft robots, soft sensors that can capture deformation with nearly infinite degree-of-freedom are necessary. Soft sensor networks can address this problem, however, measuring all sensor values throughout the body requires excessive wiring and complex fabrication that may hinder robot performance. We circumvent these challenges by developing a non-invasive measurement technique, which is based on an algorithm that solves the inverse problem of resistor network, and implement this algorithm on a soft resistive, strain sensor network. Our algorithm works by iteratively computing the resistor values based on the applied boundary voltage and current responses, and we analyze the reconstruction error of the algorithm as a function of network size and measurement error. We further develop electronics setup to implement our algorithm on a stretchable resistive strain sensor network made of soft conductive silicone, and show the response of the measured network to different deformation modes. Our work opens a new path to address the challenge of measuring many sensor values in soft sensors, and could be applied to soft robotic sensor systems.