Large-area Tomographic Tactile Skin with Air Pressure Sensing for Improved Force Estimation

This paper presents a dual-channel tactile skin that integrates Electrical Impedance Tomography (EIT) with air pressure sensing to achieve accurate multi-contact force detection. The EIT layer provides spatial contact information, while the air pressure sensor delivers precise total force measurement. Our framework combines these complementary modalities through: deep learning-based EIT image reconstruction, contact area segmentation, and force allocation based on relative conductivity intensities from EIT. The experiments demonstrated 15.1% average force estimation error in single-contact scenarios and 20.1% in multi-contact scenarios without extensive calibration data requirements. This approach effectively addresses the challenge of simultaneously localizing and quantifying multiple contact forces without requiring complex external calibration setups, paving the way for practical and scalable soft robotic skin applications.

Robot Skin with Touch and Bend Sensing using Electrical Impedance Tomography

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.