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Intelligent Design and Algorithms to Control a Stereoscopic Camera on a Robotic Workspace

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
M. Papoutsidakis, K. Kalovrektis, C. Drosos, G. Stamoulis
10.5120/ijca2017914495

M Papoutsidakis, K Kalovrektis, C Drosos and G Stamoulis. Intelligent Design and Algorithms to Control a Stereoscopic Camera on a Robotic Workspace. International Journal of Computer Applications 167(12):32-35, June 2017. BibTeX

@article{10.5120/ijca2017914495,
	author = {M. Papoutsidakis and K. Kalovrektis and C. Drosos and G. Stamoulis},
	title = {Intelligent Design and Algorithms to Control a Stereoscopic Camera on a Robotic Workspace},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {167},
	number = {12},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {32-35},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume167/number12/27824-2017914495},
	doi = {10.5120/ijca2017914495},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

As a result of the increasingly demands in modern industry, the development of robotic systems with greater flexibility between processes and lower human factor modification and intervention requirements, were necessary. The visual control technology simplifies the process of calibration of a robot with the assistance of visual feedback. The visual inspection of a robot includes the use of industrial cameras and a computer vision system to control its position relative to the work piece. In this paper a system will be designed and constructed, which will be using computer vision to monitor a production line and remove the defective products. For this purpose a stereoscopic camera will be built that will perform and calculate the object’s coordinates in its environment. The system controller will make the trajectory planning into the three-dimensional environment, and the control of the robotic arm.

References

  1. S. Hutchinson, G. Hager, and P. I Corke. A tutorial on visual servo control. IEEE Trans. on Robotics and Automation, 12(5):651-670, October 1996.
  2. W. C. Chang. Precise positioning of binocular eye-to-hand robotic manipulators. Journal of Intelligent Robot System, 49(1):219-236, 2007.
  3. E. Malis, F. Chaumette, and S. Boudet. 2-21/2-D Visual servoing. IEEE Trans. on Robotics and Automation, 15(2): 238-250, 1999.
  4. P. I. Corke. Visual control of robot manipulators. World Scientific Series on Robotics and Automated System, 2(1): 8-12, 1993.
  5. D. I. Kosmopoulos. Robust Jacobian matrix estimation for image-based visual servoing. Robotics and Computer-Integrated Manufacturing, 27(1): 82-87, 2011.
  6. J. Feddema, C. Lee, and O. Mitchel. Automatic selection of image features for visual servoing of a robot manipulator. In Proc. Of IEEE International Conference on Robotics and Automation, vol. 2, pp. 832-837, Scottsdale, AZ, May 1989.
  7. P. I. Corke and S. Hutchinson. A new partitioned approach to image-based visual servo control. IEEE Trans. on Robotics and Automation, 17(1): 507-515, August 2001.
  8. W. C. Chang. Precise positioning of binocular eye-to-hand robotic manipulators. Journal of Intelligent Robot System, 49(1): 219–236, 2007.
  9. R. C. Harrell, D. C. Slaugher, and P. D. Adsit. A fruit-tracking system for robotic harvesting system. Machine Vision and Applications, 2(5): 69-80, 1989.
  10. R. Nevtia. Depth measurement by motion stereo. Computer graphics and Image Processing, 5(1): 203-214, 1976.
  11. J. Stavnitzky, D. Capson. Multiple camera model-based 3-D visual servoing. IEEE Trans. on Robotics and Automation, 16(6): 732-739, 2000.
  12. Richard Hartley and Andrew Zisserman. Multiple View Geometry in computer vision. Cambridge University Press, 2nd edition, 2003. ISBN = 0-521054051-8.
  13. D. G. Lowe. Robust model-based motion tracking through the integration of search and estimation. In International Journal of Computer Vision, pages 113–122, 1992.
  14. Gary Bradski & Adrian Kaebler. Computer Vision with the OpenCV Library. O’REILLY Media,2008. ISBN: 978-0-596-51613-0

Keywords

Robotic arm, visual recognition, object identification, quality control, movement algorithm, robotic working space