Robot Skin with Touch and Bend Sensing using Electrical Impedance Tomography

2025-03-17

AI Summary

To address the challenge of simultaneously sensing tactile pressure and bending deformation on articulated robots, this work proposes a flexible electronic skin based on electrical impedance tomography (EIT). The method introduces an adaptive reference voltage update mechanism—enabling, for the first time, real-time decoupling of tactile force and bending deformation under dynamic conditions. Magnetic hydrogel is integrated to enhance signal discriminability, while a hybrid reconstruction and classification framework combines first-order Gauss–Newton inversion with a convolutional neural network (CNN). Experimental results demonstrate an average tactile contact localization error of 5.4 mm (SD 2.2 mm) and an average bending angle estimation error of 1.9° (SD 1.6°). Classification accuracy across three states—touch, bending, and idle—is significantly improved. These outcomes validate the robustness and practicality of the proposed approach in complex, multi-modal deformation scenarios.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Learning with Manifolds

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Flexible electronic skins that simultaneously sense touch and bend are desired in several application areas, such as to cover articulated robot structures. This paper introduces a flexible tactile sensor based on Electrical Impedance Tomography (EIT), capable of simultaneously detecting and measuring contact forces and flexion of the sensor. The sensor integrates a magnetic hydrogel composite and utilizes EIT to reconstruct internal conductivity distributions. Real-time estimation is achieved through the one-step Gauss-Newton method, which dynamically updates reference voltages to accommodate sensor deformation. A convolutional neural network is employed to classify interactions, distinguishing between touch, bending, and idle states using pre-reconstructed images. Experimental results demonstrate an average touch localization error of 5.4 mm (SD 2.2 mm) and average bending angle estimation errors of 1.9$^circ$ (SD 1.6$^circ$). The proposed adaptive reference method effectively distinguishes between single- and multi-touch scenarios while compensating for deformation effects. This makes the sensor a promising solution for multimodal sensing in robotics and human-robot collaboration.

Problem

• Develops flexible robot skin sensing touch and bend simultaneously.
• Uses Electrical Impedance Tomography for real-time force and flexion detection.
• Employs neural network to classify touch, bending, and idle states.

Innovation

• Electrical Impedance Tomography for multimodal sensing
• Magnetic hydrogel composite for conductivity reconstruction
• Convolutional neural network for interaction classification