Sandwich Miura-Ori Enabled Large Area, Super Resolution Tactile Skin for Human–Machine Interactions

Abstract

With substantial advances in materials science and electronics, flexible tactile sensors have emerged as a promising sector with extensive applications, notably in human-machine interactions. However, achieving large-area sensing with few sensing units at a low cost remains a challenge; the use of sensor arrays will complicate wiring and increase costs. To solve these issues, a sandwich Miura-ori (SMo)-enabled super-resolution tactile skin capable of resolving normal and shear forces is proposed, and a theoretical model that incorporates the impact of actual manufacturing process is also developed, enabling the model to be employed for different tactile skins following calibration. Using machine learning techniques, the proposed tactile skin can accurately localize touch inputs (average localization error of 1.89 mm) and estimate the external force (average estimation error of 8%). Furthermore, a curved SMo skin is designed and fabricated using the tessellation algorithm, then installed on a robotic arm to control the motion, demonstrating its potential in human-machine interactions. This research introduces a straightforward and cost-effective approach to the design and manufacturing of super-resolution tactile skins, and it also offers a valuable solution for future large-area tactile sensor technologies.