A Microvision-Based Motion Measurement System for Nanopositioners Using the Feature-to-Phase Method
Sheng Yao, Xianmin Zhang, Benliang Zhu, Hai Li, Longhuan Yu, Sergej Fatikow
Abstract
The development of automation at the nanoscale has been calling for precision motion sensing for robotic nanopositioners. This study presents a microvision-based measurement system for the accurate and efficient motion sensing of three-degree-of-freedom (DOF) nanopositioners. In this measurement system, an optimized target searching (OTS) scheme is proposed for automatic tracking target selection. A strategy is designed to combine feature matching with phase correlation to balance the measurement accuracy and efficiency. By proposing a multiple target tracking scheme, high-precision angular measurement is achieved, and velocity estimation is also provided in this microvision-based measurement system. Subsequently, offline simulations and online experiments are performed to comprehensively evaluate the performances of the microvision-based system with comparisons to traditional instrumentation. The simulation and experimental results demonstrate that the proposed system and marker-free method can realize excellent extensibility, strong robustness, and high precision of motion tracking for the nanopositioners, with measurement accuracy higher than 93 nm and 96 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> rad.