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 μm to 3.5 μm and from 22 μm to 7 μm).

[1]  Ning Tan,et al.  Calibration and validation of XYΘ micropositioners with vision , 2012, 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).

[2]  Stéphane Régnier,et al.  Microrobotics for Micromanipulation. , 2010 .

[3]  Philippe Lutz,et al.  Active force control for robotic micro-assembly: Application to guiding tasks , 2010, 2010 IEEE International Conference on Robotics and Automation.

[4]  Qingsong Xu,et al.  Design, Fabrication, and Visual Servo Control of an XY Parallel Micromanipulator With Piezo-Actuation , 2009, IEEE Transactions on Automation Science and Engineering.

[5]  P Sandoz,et al.  Position referencing in optical microscopy thanks to sample holders with out‐of‐focus encoded patterns , 2007, Journal of microscopy.

[6]  Ning Tan,et al.  Calibration of single-axis nanopositioning cell subjected to thermal disturbance , 2013, 2013 IEEE International Conference on Robotics and Automation.

[7]  Patrick Sandoz,et al.  Pseudo-Periodic Encryption of Extended 2-D Surfaces for High Accurate Recovery of any Random Zone by Vision , 2010 .

[8]  Éric Marchand,et al.  CAD Model-based Tracking and 3D Visual-based Control for MEMS Microassembly , 2010, Int. J. Robotics Res..

[9]  Micky Rakotondrabe,et al.  Signal Measurement and Estimation Techniques for Micro and Nanotechnology. , 2011 .

[10]  Darwin G. Caldwell,et al.  A fast and precise micropipette positioning system based on continuous camera-robot recalibration and visual servoing , 2009, 2009 IEEE International Conference on Automation Science and Engineering.

[11]  Philippe Lutz,et al.  Study of Forces During Microassembly Tasks Using Two-Sensing-Fingers Grippers , 2012, IEEE/ASME Transactions on Mechatronics.

[12]  Rudy Moddemeijer.  On the determination of the position of extrema of sampled correlators , 1991, IEEE Trans. Signal Process..

[13]  Dan O. Popa,et al.  ${\rm M}^{3}$-Deterministic, Multiscale, Multirobot Platform for Microsystems Packaging: Design and Quasi-Static Precision Evaluation , 2009, IEEE Transactions on Automation Science and Engineering.

[14]  Srinivas Akella,et al.  Coordinating Multiple Droplets in Planar Array Digital Microfluidic Systems , 2005, Int. J. Robotics Res..

[15]  Gregory S. Chirikjian,et al.  A stochastic kinematic model of class averaging in single-particle electron microscopy , 2011, Int. J. Robotics Res..

[16]  Yuen Kuan Yong,et al.  Design, Identification, and Control of a Flexure-Based XY Stage for Fast Nanoscale Positioning , 2009, IEEE Transactions on Nanotechnology.

[17]  Laurent Robert,et al.  Position-referenced microscopy for live cell culture monitoring , 2011, Biomedical optics express.

[18]  Harry E. Stephanou,et al.  A Multiscale Assembly and Packaging System for Manufacturing of Complex Micro-Nano Devices , 2012, IEEE Transactions on Automation Science and Engineering.