Fingertip-Inspired Spatially Anisotropic Inductive Liquid Metal Sensors with Ultra-Wide Range, High Linearity and Exceptional Stability

Abstract

The advancement of robotic behavior and intelligence has led to an urgent demand for improving their sensitivity and interactive capabilities, which presents challenges in achieving multidimensional, wide-ranging, and reliable tactile sensing. Here an anisotropic inductive liquid metal sensor (AI-LMS) is introduced inspired by the human fingertip, which inherently possesses the capability to detect spatially multi-axis pressure with a wide sensing range, exceptional linearity, and signal stability. Additionally, it can detect very small pressures and responds swiftly to prescribed forces. Compared to resistive signals, inductive signals offer significant advantages. Further, integrated with a deep neural network model, the AI-LMS can decouple multi-axis pressures acting simultaneously upon it. Notably, the sensing range of Ecoflex and PDMS-based AI-LMS can be expanded by a factor of 4 and 9.5, respectively. For practical illustrations, a high-precision surface scanning reconstruction system is developed capable of capturing intricate details of 3D surface profiles. The utilization of biomimetic AI-LMS as robotic fingertips enables real-time discrimination of diverse delicate grasping behaviors across different fingers. The innovations and unique features in sensing mechanisms and structural design are expected to bring transformative changes and find extensive applications in the field of soft robotics.