Compressive Sensing-Based Metrology for Micropositioning Stages Characterization

High accuracy is a necessary condition for reliable performance of micropositioning stages (MPSs). However, there are various sources of errors that affect their precision. Characterization is a prior step to calibration for compensating systematic errors so as to improve the positioning accuracy. In this letter, the compressive sensing (CS) theory is applied to characterize system errors of MPSs. This method could be flexibly collaborated with any sensors and applicable to widespread microsystems where the motions and errors are required to be measured. CS 1) improves the data acquisition and processing in terms of time and 2) could be employed as an interpolating strategy to efficiently replace the lookup tables. As a case study, the CS-based method is applied to characterize the position-dependent errors of an XY serial MPS. Experimental results show that the method is able to retrieve the microscale positions with largely shortened time and high precision. The spent time for data acquisition and processing is shortened by more than 84% for X stage and 82% for Y stage. These results are especially promising for microscale purposes where the system behavior is varying and difficult to characterize.

[1]  P. Vandergheynst,et al.  Compressed sensing imaging techniques for radio interferometry , 2008, 0812.4933.

[2]  Zeng-Guang Hou,et al.  Compressive sensing approach based mapping and localization for mobile robot in an indoor wireless sensor network , 2010, 2010 International Conference on Networking, Sensing and Control (ICNSC).

[3]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[4]  Massimo Fornasier,et al.  Compressive Sensing , 2015, Handbook of Mathematical Methods in Imaging.

[5]  Michael Elad,et al.  Dictionaries for Sparse Representation Modeling , 2010, Proceedings of the IEEE.

[6]  G. Sukhatme,et al.  Compressive Sensing based Lightweight Sampling for Large Robot Groups , 2009 .

[7]  Ning Tan,et al.  Calibration of Nanopositioning Stages , 2015, Micromachines.

[8]  Makoto Iwasaki,et al.  Mode Switching Feedback Compensation Considering Rolling Friction Characteristics for Fast and Precise Positioning , 2014, IEEE Transactions on Industrial Electronics.

[9]  Bo Song,et al.  Compressive Feedback-Based Motion Control for Nanomanipulation—Theory and Applications , 2014, IEEE Transactions on Robotics.

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

[11]  Jianwei Ma,et al.  Compressed Sensing for Surface Characterization and Metrology , 2010, IEEE Transactions on Instrumentation and Measurement.

[12]  Jianwei Ma,et al.  Single-Pixel Remote Sensing , 2009, IEEE Geoscience and Remote Sensing Letters.

[13]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[14]  Stéphane Chrétien,et al.  An Alternating $l_1$ Approach to the Compressed Sensing Problem , 2008, IEEE Signal Processing Letters.

[15]  Stéphane Chrétien.  An alternating l1 approach to the compressed sensing problem , 2007 .

[16]  Jindong Tan,et al.  Adaptive sampling using mobile robotic sensors , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Tatsuo Arai,et al.  High-Speed Automated Manipulation of Microobjects Using a Two-Fingered Microhand , 2015, IEEE Transactions on Industrial Electronics.

[18]  Johann W. Kolar,et al.  A Novel Motor Topology for High-Speed Micro-Machining Applications , 2014, IEEE Transactions on Industrial Electronics.

[19]  Ning Tan,et al.  Accuracy Quantification and Improvement of Serial Micropositioning Robots for In-Plane Motions , 2015, IEEE Transactions on Robotics.

[20]  Shuang Wang,et al.  RFID and vision based person tracking of a mobile robot using improved compressive tracking , 2013, 2013 IEEE International Conference on Information and Automation (ICIA).

[21]  Yehuda Pfeffer.  A Micro-Mirror Array based System for Compressive Sensing of Hyperspectral Data , 2010 .

[22]  Fumiyuki Adachi,et al.  High-resolution compressive channel estimation for broadband wireless communication systems , 2014, Int. J. Commun. Syst..

[23]  Ning Tan,et al.  Characterization and compensation of XY micropositioning robots using vision and pseudo-periodic encoded patterns , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[24]  Patrick Sandoz,et al.  Subpixelic Measurement of Large 1D Displacements: Principle, Processing Algorithms, Performances and Software , 2014, Sensors.

[25]  Zhang Yi,et al.  Vision-Servo System for Automated Cell Injection , 2009, IEEE Transactions on Industrial Electronics.

[26]  Jindong Tan,et al.  Compressive mobile sensing in robotic mapping , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[27]  Mojdeh Mohtashemi,et al.  Sparse sensing DNA microarray-based biosensor: Is it feasible? , 2010, 2010 IEEE Sensors Applications Symposium (SAS).

[28]  Farinaz Koushanfar,et al.  Post-silicon timing characterization by compressed sensing , 2008, 2008 IEEE/ACM International Conference on Computer-Aided Design.

[29]  Urbashi Mitra,et al.  Mission design for compressive sensing with mobile robots , 2011, 2011 IEEE International Conference on Robotics and Automation.

[30]  Justin P. Haldar,et al.  Compressed-Sensing MRI With Random Encoding , 2011, IEEE Transactions on Medical Imaging.

[31]  Kurt Bryan,et al.  Making Do with Less: An Introduction to Compressed Sensing , 2013, SIAM Rev..