Advances in robotics research

  • 제1권1호
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  • Pages.81-99
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  • 2014
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  • 2287-4976(pISSN)
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  • 2287-4984(eISSN)
테크노프레스 (Techno-Press)
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On low cost model-based monitoring of industrial robotic arms using standard machine vision

  • Karagiannidisa, Aris (School of Mechanical Engineering, National Technical University of Athens) ;
  • Vosniakos, George C. (School of Mechanical Engineering, National Technical University of Athens)
  • 투고 : 2013.09.03
  • 심사 : 2013.11.09
  • 발행 : 2014.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

This paper contributes towards the development of a computer vision system for telemonitoring of industrial articulated robotic arms. The system aims to provide precision real time measurements of the joint angles by employing low cost cameras and visual markers on the body of the robot. To achieve this, a mathematical model that connects image features and joint angles was developed covering rotation of a single joint whose axis is parallel to the visual projection plane. The feature that is examined during image processing is the varying area of given circular target placed on the body of the robot, as registered by the camera during rotation of the arm. In order to distinguish between rotation directions four targets were used placed every $90^{\circ}$ and observed by two cameras at suitable angular distances. The results were deemed acceptable considering camera cost and lighting conditions of the workspace. A computational error analysis explored how deviations from the ideal camera positions affect the measurements and led to appropriate correction. The method is deemed to be extensible to multiple joint motion of a known kinematic chain.

키워드

참고문헌

  1. Ballard, D.H. (1981), "Generalizing the Hough transform to detect arbitrary shapes", Pattern Recogn., 13, 111-122. https://doi.org/10.1016/0031-3203(81)90009-1
  2. Bradski, G. (2000), "The OpenCV Library", Dr. Dobb's Journal of Software Tools.
  3. Camarillo, D.B., Loewke, K.E., Carlson, C.R. and Salisbury, J.K. (2008), "Vision based 3-D shape sensing of flexible manipulators", Proceedings IEEE International Conference on Robotics and Automation, Pasadena, California, May, 2940-2947.
  4. Chen, S., Li, Y. and Kwok, N.M. (2011), "Active vision in robotic systems: a survey of recent developments", Int. J. Robot. Res., 30, 1343-1377. https://doi.org/10.1177/0278364911410755
  5. Di Leo, G. and Paolillo, A. (2011), "Uncertainty evaluation of camera model parameters", Proceedings 28th IEEE Instrumentation and Measurement Technology Conference, Hangzhou, P. R. China May, 6.
  6. Fung, J. and Mann, S. (2004), "Using multiple graphics cards as a general purpose parallel computer: applications to computer vision", Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK, August,Vol.1, 805-808.
  7. Fung, J., Tang, F. and Mann, S. (2002), "Mediated reality using computer graphics hardware for computer vision", Proceedings 6th International Symposium on Wearable Computers, Washington, USA, October, 83-89.
  8. Golnabi, H. and Asadpour, A. (2007), "Design and application of industrial machine vision systems", Robot. Comput. Integr. Manuf., 23, 630-637. https://doi.org/10.1016/j.rcim.2007.02.005
  9. Heisele, B. and Ritter, W (1995), "Obstacle detection based on color blob flow", Proceedings of the Intelligent Vehicles '95 Symposium, Detroit, USA, September, 282-286.
  10. Jungel, M., Mellmann, H. and Spranger, M. (2008), "Improving vision-based distance measurements using reference objects", in: Visser, U., Ribeiro, F., Ohashi, T., Dellaert, F. (Eds.), RoboCup 2007: Robot Soccer World Cup XI, Lecture Notes in Computer Science, Springer, Berlin Heidelberg, 89-100.
  11. Li, W., Jin, J., Li, X. and Li, B. (2010), "Method of rotation angle measurement in machine vision based on calibration pattern with spot array", Appl. Optics, 49, 1001-1006. https://doi.org/10.1364/AO.49.001001
  12. Ming, A. and Ma, H. (2007), "A blob detector in color images", Proceedings of the 6th ACM International Conference on Image and Video Retrieval, Amsterdam, Netherlands, July, 364-370.
  13. Mirghasemi, S. and Banihashem, E. (2009), "Sea target detection based on SVM method using HSV color space", Proceedings of 2009 IEEE Student Conference on Research and Development, Serdang, Malaysia, November, 555-558.
  14. Russ, J.C. (2011), The image processing handbook, Taylor & Francis, 6th edition, Boca Raton.
  15. Su, C.T., Chang, C.A. and Tien, F.C. (1995), "Neural networks for precise measurement in computer vision systems", Comput. Ind., 27, 225-236. https://doi.org/10.1016/0166-3615(95)00024-8
  16. Tyrrell, J.A., Lapre, J.M., Carothers, C.D., Roysam, B. and Stewart, C.V. (2004), "Efficient migration of complex off-line computer vision software to real-time system implementation on generic computer hardware", IEEE T. Inf. Technol. B., 8, 142-153. https://doi.org/10.1109/TITB.2004.828883
  17. Wang, W.G., Hu, Z.Y. and Shun, Z.Y. (2002), "3D shape measurement based on computer vision", Proceedings 2002 International Conference on Machine Learning and Cybernetics, Beijing, P.R. China, November, Vol. 2, 910-914.
  18. Wu, J. and Xiao, Z. (2010), "Video surveillance object recognition based on shape and color features", Proceedings 3rd International Congress on Image and Signal Processing, Yantai, P.R. China, October, 451-454.