A Method for Achieving Nanoscale Visual Positioning Measurement Based on Ultra-Precision Machining Microstructures

Abstract

Microscopic visual measurement is one of the main methods used for precision measurements. The observation morphology and image registration algorithm used in the measurement directly affect the accuracy and speed of the measurement. This paper analyzes the influence of morphology on different image registration algorithms through the imaging process of surface morphology and finds that complex morphology has more features, which can improve the accuracy of image registration. Therefore, the surface microstructure of ultra-precision machining is an ideal observation object. In addition, by comparing and analyzing the measurement results of commonly used image registration algorithms, we adopt a method of using the high-speed SURF algorithm for rough measurement and then combining the robust template-matching algorithm with image interpolation for precise measurements. Finally, this method has a repeatability of approximately 54 nm when measuring a planar displacement of 25 μm.

Closed-Loop Optical Tracking of a Micro-Conveyor over a Smart Surface

“…In [16], for a 5 mm square working space, the positioning error is about 400 µm for open loop control and 200 µm for closed loop control. For micro-scale vision positioning systems, a better resolution can be obtained but with a limited range [17,18]. In [19], for a range of 221 µm, the target position is obtained with a resolution below 0.5 nm.…”