E-Skin Using Fringing Field Electrical Impedance Tomography with an Ionic Liquid Domain

Abstract

Electrical impedance tomography (EIT) is a promising technique for large area tactile sensing for robotic skin. This study presents a novel EIT-based force and touch sensor that features a latex membrane acting as soft skin and an ionic liquid domain. The sensor works based on fringing field EIT where the touch or force leads to a deformation in the latex membrane causing detectable changes in EIT data. This article analyses the performance of this electronic skin in terms of its dynamical behaviour, position accuracy and quantitative force sensing. Investigation into the sensor’s performance showed it to be hypersensitive, in that it can reliably detect forces as small as 64 mN. Furthermore, multi-touch discrimination and annular force sensing is displayed. The hysteresis in force sensing is investigated showing a very negligible hysteresis. This is a direct result of the latex membrane and the ionic liquid-based domain design compared to more traditional fabric-based touch sensors due to the reduction in electromechanical coupling. A novel test is devised that displayed the dynamic performance of the sensor by showing its ability to record a 1 Hz frequency, which was applied to the membrane in a tapping fashion. Overall, the results show a considerable progress in ionic liquid EIT-based sensors. These findings place the EIT-based sensors that comprise a liquid domain, at the forefront of research into tactile robotic skin.

Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation

“…Electrical impedance tomography (EIT) is an imaging technique based on the variation in the electrical impedance distribution on the surface of a deformable object when subjected to an external load [54,55]. Such sensors have two sets of electrodes arranged around a conductive sensing area of the sensor.…”

Multi-touch Recognition of Hydrogel-based E-skins using Real-world EIT Datasets

“…Though it is straightforward to automate a pressing mechanism which collects and validates single-point presses for training [11], [20], there are few examples of multi-touch skins being trained in this way. Many works validate analytic or pretrained EIT models by manually placing multiple objects on the skins' surfaces, though this approach cannot be used for large-scale data collection [21], [22], [23]. Others -including the authors' previous work -employ multiple probes with a fixed separation, limiting the variability of data which can be collected [19], [24], [25].…”

Enhancing electrical resistance tomography reconstruction from conductive fabric: a comparative study of CycleGAN and pix2pixGAN

“…The stitch dimension of the yarn in knitted fabrics determines the change in resistance, while the different directions of the fabric elongation affect the strain in distinct ways. Soleimani and Friedrich [18] integrated electrical impedance tomography (EIT) with conductive fabrics to create wearable monitoring devices. This configuration enables the simultaneous monitoring of both the electrical conductivity and mechanical characteristics of fabrics, offering a more comprehensive assessment of the health status of the monitored area and the potential detection of anomalies.…”