Neural-Gas Network-Based Optimal Design Method for ERT-Based Whole-Body Robotic Skin

Abstract

Electrical resistance tomography (ERT) is an inferential imaging technique that has been utilized to develop large-scale robotic skin due to its scalable and practical properties. The performance of ERT-based sensors has been improved by optimizing the electrode arrangement, but it has relied on a heuristic design due to an absence of quantitative studies on the effects of the electrode arrangement. This article introduces a novel design method to optimize an electrode arrangement for ERT-based robotic skins. The method is based on a neural-gas network, and it finds an optimal design that maximizes the minimum electric current density. The optimal design was comprehensively evaluated using the ERT forward model, and the result revealed that the optimal design achieved the best intrinsic properties in terms of ill-posedness, sensitivity, and spatial discriminability. For validation, we conducted an indentation experiment on ERT-based robotic skin with the optimal design. Although only 30 electrodes were used to cover the 700 cm(2) sensing area, physical contacts could be localized with an error of 6.6 +/- 3.5 mm, and the two-point resolution was adequate for daily tasks. Finally, the developed robotic skin was integrated with a commercial robot arm, and its use in physical human-robot interaction was demonstrated.