Deep Learning Based Large‐Area Contact Sensing for Safe Human–Robot Interaction Using Conformal Kirigami Structure‐Enabled Robotic E‐Skin

Abstract

Collaborative robots need to work with people in shared spaces interactively, so a robotic e‐skin with large‐area contact sensing capability is a crucial technology to ensure human safety. However, realizing real‐time contact localization and intensity estimation on a robot body with a large area of continuous and complex surfaces is challenging. Herein, a novel large‐area conformal Kirigami structure that can be customized for complex geometries and transform small‐area planar sensor arrays into large‐area curved conformal e‐skin is proposed. This sensor network can effectively detect Lamb/guided wave responses generated by transient hard contact. Additionally, a convolutional neural network‐based deep learning algorithm is implemented to decode the features of guided wave signals and predict the contact location and energy intensity on the robot surface. With the deep learning‐based method, the accuracy of collision localization can reach 2.85 ± 1.90 mm and the prediction error of collision energy can reach 9.8 × 10 −4 ± 8.9 × 10 −4 J. Demonstrations show that the proposed method can provide real‐time on‐site contact sensing, providing a promising solution for future intelligent human–robot interaction.