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Applied Intelligence

, Volume 10, Issue 1, pp 25–36 | Cite as

Planar Grasping Characterization Based on Curvature-Symmetry Fusion

  • P.J. Sanz
  • J.M. Iñesta
  • A.P. Del Pobil
Article

Abstract

A new strategy is presented to simplify the real-time determination of grasping points in unknown objects from 2D images. We work with a parallel-jaw gripper and assume point contact with friction, taking into account stability conditions. This strategy is supported by a new tool that permits to establish a supervisor mechanism with the aim to seek grasping points from geometric reasoning on the contours extracted from 2D images captured by the system in execution time. This approach is named “curvature-symmetry fusion” (CSF) and its objective is to integrate curvature and symmetry knowledge in a single data structure to provide the necessary information to predict the more suitable directions used by a supervisor mechanism described below. These algorithms have been implemented on a SCARA manipulator with one end point mounted camera. Visual feedback was used in the control system and the total time for the execution is about 2 or 3 seconds in our inexpensive prototype, making real applications feasible.

robotic manipulators grasp determination robot vision 

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References

  1. 1.
    S.J. Joshua, Symmetry Principles and Magnetic Symmetry in Solid State Physics, Adam Hilger: Bristol, 1991.Google Scholar
  2. 2.
    M. Brady and H. Asada, “Smoothed local symmetries and their implementation,” The International Journal of Robotics Research, vol. 3,no. 3, pp. 36–61, 1984.Google Scholar
  3. 3.
    L. van Gool, T. Moons, D. Ungureanu, and E. Pauwels, “Symmetry from shape and shape from symmetry,” The International Journal of Robotics Research, vol. 14,no. 5, pp. 407–424, October 1995.Google Scholar
  4. 4.
    J.M. I~nesta, M. Buendía, and M.A. Sarti, “Local symmetries of digital contours from their chain codes,” Pattern Recognition, vol. 29,no. 10, pp. 1737–1749, 1996.Google Scholar
  5. 5.
    W. Ledermann, Handbook of Applicable Mathematics: Combinatorics and Geometry, vol. 5, Part B, chapt. 11: “Symmetry”, John Wiley, 1985.Google Scholar
  6. 6.
    X. Markenscoff, L. Ni, and C.H. Papadimitriou, “The Geometry of Grasping,” The International Journal of Robotics Research, vol. 9,no. 1, pp. 61–74, February 1990.Google Scholar
  7. 7.
    V.-D. Nguyen, “Constructing force-closure grasps,” The International Journal of Robotics Research, vol. 7,no. 3, June 1988.Google Scholar
  8. 8.
    A. Blake, “A symmetry theory of planar grasp,” The International Journal of Robotics Research, vol. 14,no. 5, pp. 425–444, October 1995.Google Scholar
  9. 9.
    A. Rosenfeld and E. Johnston, “Angle detection on digital curves,” IEEE Transactions on Computers, vol. C-22, pp. 875–878, September 1973.Google Scholar
  10. 10.
    Y.F. Li and M.H. Lee, “Applying vision guidance in robotic food handling,” IEEE Robotics and Automation Magazine, vol. 3,no. 1, pp. 4–12, March 1996.Google Scholar
  11. 11.
    I. Kamon, T. Flash, and S. Edelman, “Learning to grasp using visual information,” in Proc. of the IEEE Int. Conf. on Robotics and Automation, Minneapolis, Minesota, April 1996, pp. 2470–2476.Google Scholar
  12. 12.
    H. Zabrodsky, S. Peleg, and D. Avnir, “Symmetry as a continuous feature,” IEEE Trans. Patt. Anal. Mach. Intell., vol. 7,no. 12, pp. 1154–1166, 1995.Google Scholar
  13. 13.
    M. Leyton, “Symmetry-curvature duality,” Comput. Vision Graphics Image Process, vol. 38, pp. 327–341, 1987.Google Scholar
  14. 14.
    I. Kamon, T. Flash, and S. Edelman, “Learning to grasp using visual information,” in Proc. of the IEEE Int.Conf. on Robotics and Automation, Minneapolis, Minesota, April 1996, pp. 2470–2476.Google Scholar
  15. 15.
    P.J. Sanz, J. Domingo, A.P. del Pobil, and J. Pelechano, “An integrated approach to position a robot arm in a system for planar part grasping,” Advanced Manufacturing Forum, special issue on Applications of Artificial Intelligence, vol. 1, pp. 137–148, 1996.Google Scholar
  16. 16.
    P.J. Sanz, “Razonamiento geométrico basado en visión para la determinación y ejecución del agarre en robots manipuladores,” Ph.D. Thesis, Jaume I Univ., Spain, 1996 (in spanish).Google Scholar
  17. 17.
    P.J. Sanz, J.M. I~nesta, and A.P. del Pobil, “Towards an automatic determination of grasping points through a machine vision approach,” in Proc. of the Ninth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE), Fukuoka, Japan, 1996, pp. 767–772.Google Scholar
  18. 18.
    E. Davis, Representations of Commonsense Knowledge, Morgan Kaufmann Publishers: CA, 1990.Google Scholar
  19. 19.
    D. Opitz, H.H. Bulthoff, and A. Blake, “Optimal grasp points: Computational theory and human psychophysics,” Perception, pp. 22:123, 1993.Google Scholar
  20. 20.
    W.K. Pratt, Digital Image Processing, J. Wiley and Sons: New York, 1991.Google Scholar
  21. 21.
    J. Ponce, D. Stam, and B. Faverjon, “On computing force-closure grasps of curved two dimensional objects,” Int. J. Robotics Res., vol. 12,no. 3, pp. 263–273, June 1993.Google Scholar

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • P.J. Sanz
    • 1
  • J.M. Iñesta
    • 1
  • A.P. Del Pobil
    • 1
  1. 1.Departamento de InformáticaUniversitat Jaume ICastellónSpain

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