Skip to main content
Log in

Planar Grasping Characterization Based on Curvature-Symmetry Fusion

  • Published:
Applied Intelligence Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. S.J. Joshua, Symmetry Principles and Magnetic Symmetry in Solid State Physics, Adam Hilger: Bristol, 1991.

    Google Scholar 

  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. 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. 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. W. Ledermann, Handbook of Applicable Mathematics: Combinatorics and Geometry, vol. 5, Part B, chapt. 11: “Symmetry”, John Wiley, 1985.

  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. V.-D. Nguyen, “Constructing force-closure grasps,” The International Journal of Robotics Research, vol. 7,no. 3, June 1988.

  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. A. Rosenfeld and E. Johnston, “Angle detection on digital curves,” IEEE Transactions on Computers, vol. C-22, pp. 875–878, September 1973.

  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. 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.

  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. M. Leyton, “Symmetry-curvature duality,” Comput. Vision Graphics Image Process, vol. 38, pp. 327–341, 1987.

    Google Scholar 

  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.

  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. 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. 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.

  18. E. Davis, Representations of Commonsense Knowledge, Morgan Kaufmann Publishers: CA, 1990.

    Google Scholar 

  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. W.K. Pratt, Digital Image Processing, J. Wiley and Sons: New York, 1991.

    Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sanz, P., Iñesta, J. & Del Pobil, A. Planar Grasping Characterization Based on Curvature-Symmetry Fusion. Applied Intelligence 10, 25–36 (1999). https://doi.org/10.1023/A:1008381314159

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008381314159

Navigation