Characterization and Compensation of XY Micropositioning Robots using Vision and Pseudo-Periodic Encoded Patterns

Accuracy is an important issue for microrobotic applications. High accuracy is usually a necessary condition for reliable system performance. However there are many sources of inaccuracy acting on the microrobotic systems. Characterization and compensation enable reduction of the systematic errors of the micropositioning stages and improve the positioning accuracy. In this paper, we propose a novel method based on vision and pseudo-periodic encoded patterns to characterize the position-dependent errors along XY stages. This method is particularly suitable for microscale motion characterization thanks to its high range-to-resolution ratio and avoidance of camera calibration. Based on look-up tables and interpolation techniques, we perform compensation and get improved accuracy. The experimental results show an accuracy improved by 84% for square tracking and by 68% for random points reaching (respectively from 22 mu m to 3.5 mu m and from 22 mu m to 7 mu m).