- Volume 16 Issue 12
DOI QR Code
Fast and Fine Control of a Visual Alignment Systems Based on the Misalignment Estimation Filter
정렬오차 추정 필터에 기반한 비전 정렬 시스템의 고속 정밀제어
- Jeong, Hae-Min (STX Offshore & Shipbuilding Co.) ;
- Hwang, Jae-Woong (Korea Aerospace University) ;
- Kwon, Sang-Joo (Korea Aerospace University)
- Received : 2010.03.22
- Accepted : 2010.08.07
- Published : 2010.12.01
In the flat panel display and semiconductor industries, the visual alignment system is considered as a core technology which determines the productivity of a manufacturing line. It consists of the vision system to extract the centroids of alignment marks and the stage control system to compensate the alignment error. In this paper, we develop a Kalman filter algorithm to estimate the alignment mark postures and propose a coarse-fine alignment control method which utilizes both original fine images and reduced coarse ones in the visual feedback. The error compensation trajectory for the distributed joint servos of the alignment stage is generated in terms of the inverse kinematic solution for the misalignment in task space. In constructing the estimation algorithm, the equation of motion for the alignment marks is given by using the forward kinematics of alignment stage. Secondly, the measurements for the alignment mark centroids are obtained from the reduced images by applying the geometric template matching. As a result, the proposed Kalman filter based coarse-fine alignment control method enables a considerable reduction of alignment time.
Supported by : 한국연구재단
- S. J. Kwon, C. Park, and S. M. Lee, “Kinematics and control of a visual alignment system for flat panel displays,” Journal of Institute of Control, Robotics and Systems, vol. 14, no. 4, Apr., pp. 369-375, 2008. https://doi.org/10.5302/J.ICROS.2008.14.4.369
- H. Jeong, S. J. Kwon, and S. M. Lee, “Mask-panel alignment robot system using a parallel mechanism with actuation redundancy,” Journal of Institute of Control, Robotics and Systems, vol. 15, no. 9, pp. 887-893, 2009. https://doi.org/10.5302/J.ICROS.2009.15.9.887
- R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice-Hall, 2002.
- E. Gose, R. Johnsonbaugh, and S. Jost, Pattern Recognition and Image Analysis, Prentice-Hall, 1996.
- C. S. Park and S. J. Kwon, “An efficient vision algorithm for fast and fine mask-panel alignment,” Proc. of 2006 SICE-ICASE International Joint Conference (SICE-ICCAS 2006), pp. 1461-1465, Oct. 2006.
- M. S. Grewal and A. P. Andrews, Kalman Filtering: Theory and Practice Using Matlab, Wiley Inter-science, 2001.
- R. Qian, M. Sezan, and K. Matthews, “A robust real-time face tracking algorithm,” Proc. IEEE ICIP, vol. 1, pp. 131-135, Oct. 1998. https://doi.org/10.1109/ICIP.1998.723443
- D. S. Jang, S. W. Jang, and H. I. Choi, “2-D human body tracking with structural kalman filter,” Pattern Recognition, vol. 35, pp. 2041-2049, 2002. https://doi.org/10.1016/S0031-3203(01)00201-1
- J. Wang, H. Zha, and R. Cipolla, “Coarse-to-fine vision-based localization by indexing scale-Invariant features,” IEEE Trans. on Systems, Man, and Cybermetics-Part B: Cybernetics, vol. 36, no. 2, pp. 413-422, Apr. 2006. https://doi.org/10.1109/TSMCB.2005.859085
- L. Ren, L. Wang, J. K. Mills, and D. Sun, “Vision-based 2-D automatic micrograsping using coarse-to-fine grasping strategy,” IEEE Trans. on Industrial Electronics, vol. 55, no. 9, pp. 3324-3331, Sep. 2008. https://doi.org/10.1109/TIE.2008.927976
- S. J. Ralis, B. Vikramaditya, and B. J. Nelson, “Micropositioning of a weakly calibrated microassembly system using coarse-tofine visual servoing strategies,” IEEE Trans. on Electronics Packaging and Manufacturing, vol. 23, no. 2, pp. 123-131, Apr. 2000. https://doi.org/10.1109/6104.846935