Development of a Wireless Embedded Sensing System With Physics-Based Neural Networks for Simultaneous Displacement and Force Measurements of a Magnetic Leadscrew

Lightweight impedance-controllable end-effectors are increasingly important in emerging applications involving physical human-robot interaction. Motivated by this need, this paper presents a method to design a magnetic lead screw (Mag-LS) with a built-in wireless sensing system that utilizes its inherent magnetic field for simultaneous displacement and force measurements. Based on the field/force models derived in closed form, this paper analyzes the uniqueness of the inverse solutions for designing the magnetic sensing system and optimizing its parameters to minimize computational cost for implementation on a standalone microcontroller where sensor noises are filtered. Unmodeled geometrical factors are accounted for using physics-based artificial neural networks (ANNs). A prototype Mag-LS with embedded sensors and testbed have been developed, on which noise and parametric effects on sensing accuracy are experimentally investigated. The results demonstrate the effectiveness of a multi-single-output ANN for simultaneously measuring the displacements/force of a Mag-LS, offering an alternative collocated force and displacement sensing solution for impedance/force control.