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Part grasping for automated disassembly

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Abstract

A robot grasp synthesis algorithm for automated disassembly is presented. The goal is to select grasping points in each part to be disassembled so that a previously planned disassembly sequence can be performed holding the parts firmly and avoiding collisions. The algorithm is structured in five steps in order to make it general enough to cope with different robot grippers and different geometrical data (2D or 3D). The system is learning based, and behaviour rules are automatically extracted from grasping examples given by the user, using mainly decision trees and nearest neighbour techniques. Some simulation experiments have been carried out and results with a two fingered robot gripper are presented.

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References

  1. Aksoy HK, Gupta SM (2002) Capacity and buffer trade-offs in a remanufacturing system. Environ Conscious Manuf II, SPIE 4569:167–174

    Google Scholar 

  2. Gungor A, Gupta SM (2001) Disassembly sequence plan generation using a branch-and-bound algorithm. Int J Prod Res 39(3):481–509

    Article  Google Scholar 

  3. Zeid I, Gupta SM, Bardasz T (1997) A case-based reasoning approach to planning for disassembly. J Intell Manuf 8(2):97–106

    Article  Google Scholar 

  4. Zussman E, Zhou MC (2000) Design and implementation of an adaptive process planner for disassembly processes. IEEE Trans Robot Automat 16(2):171–179

    Article  Google Scholar 

  5. Torres F, Puente ST, Aracil R (2003) Disassembly planning based on precedence relations among assemblies. Int J Adv Manuf Technol 21(5):317–327

    Article  Google Scholar 

  6. Shyamsundar N, Gadh R (1999) Geometric abstractions to support disassembly analysis. IIE Trans 31:935–946

    Article  Google Scholar 

  7. Srinivasan H, Gadh R (1998) A geometric algorithm for single selective disassembly using the wave propagation abstraction. Comput Aided Des 30(8):603–613

    Article  MATH  Google Scholar 

  8. Torres F, Gil P, Puente ST, Pomares J, Aracil R (2004) Automatic PC disassembly for component recovery. Int J Adv Manuf Technol 23(1–2):39–46

    Article  Google Scholar 

  9. Bicchi A (2000) Hands for dexterous manipulation and robust grasping: a difficult road towards simplicity. IEEE Trans Robot Automat 16(6):652–662

    Article  Google Scholar 

  10. Van Holland W (1997) Assembly features in modelling and planning. Dissertation, Delft University of Technology

  11. Puente ST (2002) Desensamblado automático no destructivo para la reutilización de componentes. Aplicación al desessamblado de PC’s. Dissertation, University of Alicante

  12. Wai Sung RC (2001) Automatic assembly feature recognition and disassembly sequence generation. Dissertation, Heriot-Watt University

  13. Bard C, Trocca J, Vercelli G (1991) Shape analysis and hand preshaping for grasping. Proc Intelligent Robots and Systems, Intelligence for Mechanical Systems 1:64–69

    Google Scholar 

  14. Nguyen VD (1986) The synthesis of stable force-closure grasps. Technical report AI-TR-905, MIT Artificial Intelligence Laboratory

  15. Toth E (1999) Stable object grasping with dextrous hand in three-dimension. Periodica Polytechnica Ser El Eng 43(3):207–214

    Google Scholar 

  16. Ferrari C, Canny J (1992) Planning optimal grasps. Proc. IEEE Conf. on Robotics and Automation, Nice, pp 2290–2295

  17. Cornellá J, Suárez R (2003) On 2D 4-finger frictionless optimal grasps. 16th IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, pp 156–162

  18. Pollard NS (1996) Synthesizing grasps from generalized prototypes. Proc IEEE International Conference on Robotics and Automation, Minneapolis, pp 901–911

  19. Fernandez C, Vicente MA, Reinoso O, Aracil R (2004) A decision tree based approach to grasp synthesis. Proc International Conference on Intelligent Manipulation and Grasping, Genoa, pp 486–491

  20. Hwang CS, Takano M, Sasaki K (1999) Kinematics of grasping and manipulation of a B-spline surface object by a multifingered robot hand. J Robot Syst 16(8):445–460

    Article  MATH  Google Scholar 

  21. Hunt KH, Samuel AE, McAree PR (1991) Special configurations of multi-finger multi-freedom grippers: a kinematic study. Int J Robot Res 10(2):123–134

    Article  Google Scholar 

  22. Jimenez P, Thomas F, Torras C (2001) 3D collision detection: a survey. Comput Graph 25(2):269–285

    Article  Google Scholar 

  23. Lin M (1993). Efficient collision detection for animation and robotics. Dissertation, University of California

  24. Gottschalk S, Lin M , Manocha D (1996) A hierarchical structure for rapid interference detection. Proc of ACM Siggraph’96, pp 171–180

  25. Hudson T, Lin M, Cohen J, Gottschalk S, Manocha D (1997) V-collide: accelerated collision detection for VRML. Proc. of VRML Conference, pp 119–125

  26. Klosowski J (1998) Efficient collision detection forinteractive 3D graphics and virtual environments. Dissertation, University of New York

  27. Nguyen VD (1986) The synthesis of stable force-closure grasps. Technical report AI-TR-905, MIT Artificial Intelligence Laboratory

  28. Ponce J, Faverjon B (1995) On computing three finger force-closure grasp of polygonal objects. IEEE Trans Robot Automat 11(6):868–881

    Article  Google Scholar 

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Correspondence to Cesar Fernandez.

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Fernandez, C., Reinoso, O., Vicente, M.A. et al. Part grasping for automated disassembly. Int J Adv Manuf Technol 30, 540–553 (2006). https://doi.org/10.1007/s00170-005-0054-5

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  • DOI: https://doi.org/10.1007/s00170-005-0054-5

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