Position Determination of a Robot End-Effector Using a 6D-Measurement System Based on the Two-View Vision


A mechatronic system based on the micro-macro-kinematic consists of an industrial robot and a piezoelectric stage mounted on the robot’s end-effector and has to carry out operations like micro-assembly or micro-milling. The piezoelectric stage has to compensate the positioning error of the robot. Therefore, the position of the robot’s end-effector has to be measured with high accuracy. This paper presents a high accuracy 6D-measurement system, which is used to determine the position and orientation of the robot’s end-effector. We start with the description of the operational concept and components of the measurement system. Then we look at image processing methods, camera calibration and reconstruction methods and choose the most accurate ones. We apply the well-known pin-hole camera model to calibrate single cameras. Then we apply the epipolar geometry to describe the relationship between two cameras and calibrate them as a stereo vision system. A distortion model is also applied to enhance the accuracy of the system. The measurement results are presented in the end of the paper.

Share and Cite:

A. Janz, C. Pape and E. Reithmeier, "Position Determination of a Robot End-Effector Using a 6D-Measurement System Based on the Two-View Vision," Open Journal of Applied Sciences, Vol. 3 No. 7, 2013, pp. 393-403. doi: 10.4236/ojapps.2013.37049.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] R. C. Gonzalez and R. E. Woods, “Digital Image Processing,” 2nd Edition, Prentice Hall, Upper Saddle River, 2002.
[2] J. Heikkilä, “Geometric Camera Calibration Using Circular Control Points,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 22, No. 10, 2000, pp. 1066-1077. http://dx.doi.org/10.1109/34.879788
[3] Z. Zhang, “Flexible Camera Calibration by Viewing a Plane from Unknown Orientations,” Proceedings of the International Conference on Computer Vision ICCV, Kerkyra, 20-27 September 1999, pp. 666-673. http://dx.doi.org/10.1109/ICCV.1999.791289
[4] R. Y. Tsai, “A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses,” IEEE Journal of Robotics and Automation, Vol. 3, No. 4, 1987, pp. 323- 344. http://dx.doi.org/10.1109/JRA.1987.1087109
[5] C. Ricolfe-Viala and A.J. Sanches-Salmeron, “Lens Distortion Models Evaluation,” Applied Optics, Vol. 49, No. 30, 2012, pp. 5914-5928. http://dx.doi.org/10.1364/AO.49.005914
[6] Z. Zhang, “A Flexible New Technique for Camera Calibration,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 22, No. 11, 2000, pp. 1330-1334. http://dx.doi.org/0.1109/34.888718
[7] R. Hartley and S. B. Kang, “Parameter-free Radial Distortion Correction with Centre of Distortion Estimation,” Proceeding of the 10th IEEE International Conference on Computer Vision ICCV’05, Beijing, 17-21 October 2005, pp. 1-8.
[8] R. Hartley and A. Zisserman, “Multiple View Geometry in Computer Vision,” 2nd Edition, Cambridge University Press, Cambridge, 2003.
[9] R. Hartley and C. SilpaAnan, “Reconstruction from Two Views Using Approximate Calibration,” Proceedings of the 5th Asian Conference on Computer Vision ACCV’02, Melbourne, January 2004, pp. 1-6.
[10] B. Albouy, S. Treuillet, Y. Lucas and D. Birov, “Fundamental Matrix Estimation Revisited Through a Global 3D Reconstruction Framework,” Proceedings of the Advanced Concepts for Intelligent Vision Systems ACIVS 2004, Brussels, 31 August-3 September 2004, pp. 185-192.
[11] K. Kanatani and Y. Sugaya, “Compact Fundamental Matrix Computation,” Proceedings of the 3rd Pacific Rim Symposium of Image and Video Technology, Tokyo, 13-16 January 2009, pp. 179-190.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.