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Machine Vision and Applications

, Volume 24, Issue 2, pp 351–370 | Cite as

Direction Kernels: using a simplified 3D model representation for grasping

  • Antonio AdánEmail author
  • Andrés S. Vázquez
  • Pilar Merchán
  • Ruben Heradio
Original Paper
  • 243 Downloads

Abstract

Humans decide how to carry out a spontaneous interaction with an object by using the whole geometric information obtained from their eyes. The aim of this paper is to present how our object representation model MWS (Adán in Comput Vis Image Underst 79:281–307, 2000) can help a robot manipulator to make a single and reliable interaction. The contribution of this paper is particularly focused on the grasp synthesis stage. The main idea is that the grasping system, through MWS, can use non-strict-local features of the contact points to find a consistent grasping configuration. The Direction Kernels (DK) concept, which is integrated into the MWS model, is used to define a set of candidate contact-points and interaction regions. The set of DK is a global feature which represents the principal normal vectors of the object and their relative weight in a three-connectivity mesh model. Our method calculates the optimal grasp points (which are ordered according to the quality function) for two-finger grippers, whilst maintaining the requirements of force closure and safety of the grasp. Our strategy has been extensively tested on real free-shape objects using a 6 DOF industrial robot.

Keywords

3D model representation Object recognition Grasping 

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References

  1. 1.
    Cutkosky M.R.: On grasp choice, grasp models and the design of hands for manufacturing tasks. IEEE Trans. Robot. Autom. 5(3), 269–279 (1989)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Nguyen, V.D.: Constructing stable grasps in 3D. In: IEEE International Conference on Robotics and Automation, vol. 4 (1987)Google Scholar
  3. 3.
    Ferrari, C., Canny, J.: Planning optimal grasps. IEEE ICRA, Nice, pp. 2290–2295 (1992)Google Scholar
  4. 4.
    Borst, C., Fisher, M., Hirzinger, G.: A fast and robust grasps planner for arbitrary 3D objects. IEEE ICRA 1999, Detroit, pp. 1890–1896Google Scholar
  5. 5.
    Pollard, N.: Synthesizing grasps from generalized prototypes. IEEE ICRA, Minneapolis, pp. 2124–2130 (1996)Google Scholar
  6. 6.
    Miller, A.T., Allen, P.K.: Examples of 3D Grasp Quality computations. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1240–1246 (1999)Google Scholar
  7. 7.
    Ponce J. Faverjon B.: On computing three finger force-closure grasps of polyhedral objects. IEEE Trans. Robot. Autom. 11(6), 868–881 (1995)CrossRefGoogle Scholar
  8. 8.
    Ding, D. Liu Y., Wang S.: Computing 3D optimal form-closure grasps. IEEE ICRA, San Francisco, pp. 3573–3578 (2000)Google Scholar
  9. 9.
    Chinellato, E., Fisher, R.B., Morales, A., Pobil, A.P.: Ranking planar grasp configurations for a three-finger hand. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1133–1139 (2003)Google Scholar
  10. 10.
    Prado-Gardini, R., Suárez, R.: Heuristic approach to construct 3-finger force-closure grasp for polyhedral objects. In: 7th IFAC Symposium on Robot Control (SYROCO’2003), pp. 387–392 (2003)Google Scholar
  11. 11.
    Sanz, P.J., Marín, R., Dabic, S.: Improving 2D visually-guided grasping performance by jeans of new geometric computation techniques. In: Intelligent Manipulation and Grasping, Genova, Italy, pp. 559–654 (2004)Google Scholar
  12. 12.
    Kawarazaki, N., Hesegawa, T., Nishihara, Z.: Grasp planning algorithm for a multifingered hand-arm robot. In: IEEE ICRA, Leuven, pp. 933–939 (1998)Google Scholar
  13. 13.
    Fernandez, C., Vicente, A., Reinoso, O., Aracil, R.: A decision tree based approach to grasp synthesis. In: Intelligent Manipulation and Grasping, Genova, Italy, pp. 486–491 (2004)Google Scholar
  14. 14.
    Morales, A., Chinellato, E., Sanz, P.J., Fagg, A.H., del Pobil, A.P.: Vision based planar grasp synthesis and reliability assessment for a multifinger robot hand: a learning approach. In: Intelligent Manipulation and Grasping, pp. 566–570 (2004)Google Scholar
  15. 15.
    Pelossof, R., Miller, A., Allen, P., Jebara, T.: An SVM learning approach to robotic grasping. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3215–3218 (2004)Google Scholar
  16. 16.
    Michel, C., Remond, C., Perdereau, V., Drouin, M.: A robotic planner based on the natural grasping axis. In: Intelligent Manipulation and Grasping, Genova, Italy, pp. 492–497 (2004)Google Scholar
  17. 17.
    Miller, A., Knoop, S., Christensen, H.I., Allen, P.K.: Automatic grasp planning using primitives. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1824–1829 (2003)Google Scholar
  18. 18.
    Adán A., Cerrada C., Feliu V.: Modeling wave set: definition and application of a new topological organization for 3D object modeling. Comput. Vis. Image Underst. 79, 281–307 (2000)CrossRefGoogle Scholar
  19. 19.
    Adán, A., Adán, M.: A flexible similarity measure for 3D shapes recognition. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (Nov. 2004)Google Scholar
  20. 20.
    Merchán P., Adán A.: Exploration Trees on Highly Complex Scenes: A New Approach for 3D Segmentation. Pattern Recognit. 40(7), 1879–1898 (2007)zbMATHCrossRefGoogle Scholar
  21. 21.
    Merchan, P., Adán, A., Salamanca, S.: Recognition of free-form objects in complex scenes using DGI-BS models. In: Third International Symposium on 3D Data Processing, Visualization and Transmission, 3DPVT, Chapel Hill, USA (2006)Google Scholar
  22. 22.
    Vázquez, A.S., Torres, R., Adán, A., Cerrada, C.: Path planning for manipulation environments through interpolated walks. In: International Conference on Intelligent Robots and Systems (IROS’06), Beijing, ChinaGoogle Scholar
  23. 23.
    Vazquez, A.: Robot interaction in 3D environment: new approaches on integration architectures, path planning and grasping. PhD thesis, Castilla La Mancha University (2008)Google Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Antonio Adán
    • 1
    Email author
  • Andrés S. Vázquez
    • 1
  • Pilar Merchán
    • 2
  • Ruben Heradio
    • 3
  1. 1.Dpto Ingeniería E.E.A.CUniversidad de Castilla La ManchaCiudad RealSpain
  2. 2.Escuela de ingenierías IndustrialesUniversidad de ExtremaduraBadajozSpain
  3. 3.Dpto de Ingeniería de Software y Sistemas Informáticos, UNEDMadridSpain

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