Soft Magnetic Tactile Skin for Continuous Force and Location Estimation Using Neural Networks

Abstract

Soft tactile skins can provide an in-depth understanding of contact location and force through a soft and deformable interface. However, widespread implementation of soft robotic sensing skins remains limited due to non-scalable fabrication techniques, lack of customization, and complex integration requirements. In this work, we demonstrate magnetic composites fabricated with two different matrix materials, a silicone elastomer and urethane foam, that can be used as continuous tactile surfaces for single-point contact localization. Building upon previous work, we increased the sensing area from a 15 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> grid to a 40 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> continuous surface. Additionally, new preprocessing methods for the raw magnetic field data, in conjunction with the use of a neural network, enables rapid location and force estimation in free space. We report an average localization of 1 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> for the silicone surface and 2 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> for the urethane foam. Our approach to soft sensing skins addresses the need for tactile soft surfaces that are simple to fabricate and integrate, customizable in shape and material, and usable in both soft and hybrid robotic systems.