Soft Computing

, Volume 22, Issue 3, pp 759–772 | Cite as

Optimizing force closure grasps on 3D objects using a modified genetic algorithm

  • V. Rakesh
  • Utkarsh Sharma
  • S. Murugan
  • S. Venugopal
  • T. Asokan
Methodologies and Application
  • 114 Downloads

Abstract

The problem of automated grasp generation is exacerbated by the infinite types of objects to be handled by robots. In this work, the issue is cast as an optimization problem and a modified genetic algorithm-based approach has been formulated for the synthesis of high-quality grasps. The convex hull of the grasp contact wrenches is built, and the largest ball is inscribed within it. The radius of this resulting ball, centered at the origin, is used to represent the grasp quality. An initial feasible grasp is increased in quality by generating wrench population considering the complete body for an exhaustive search. Tessellated objects are utilized for the planner to ensure the applicability of the approach on complex shapes. The performance efficacy of the proposed method is numerically showcased through various frictional and non-frictional prehensile contact examples and is featured along with the results of an existing heuristic method on similar models with moderate and dense tessellation.

Keywords

Robot grasp synthesis Tessellated object Convex hull Grasp quality Modified genetic algorithm (GA) 

Notes

Acknowledgments

The authors gratefully acknowledge the colleagues at IGCAR for their constant encouragement during this study. The authors also thank the editor and anonymous reviewers for their insightful and constructive suggestions and careful review of the paper.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Bicchi A (1995) On the closure properties of robotic grasping. Int J Robot Res 14(4):319–334CrossRefGoogle Scholar
  2. Borst Ch, Fischer M, Hirzinger G (2003) Grasping the dice by dicing the grasp. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systemsGoogle Scholar
  3. Chella A, Dindo H, Matraxia F, Pirrone R (2007) Real-time visual grasp synthesis using genetic algorithms and neural networks. In: Proceedings of the 10th congress of the Italian association for artificial intelligence on AI*IA 2007: artificial intelligence and human-oriented computing (AI*IA ’07). Springer, Berlin, pp 567–578Google Scholar
  4. Chen I, Burdick J (1993) Finding antipodal point grasps on irregularly shaped objects. IEEE Trans Rob Autom 9(4):507–512CrossRefGoogle Scholar
  5. Daoud N, Gazeau J, Zeghloul S, Arsicault M (2011) A fast grasp synthesis method for online manipulation. Rob Auton Syst 59(6):421–427CrossRefGoogle Scholar
  6. Deb K (2009) Optimization for engineering design: algorithms and examples. PHI Learning Pvt. Ltd, New DelhiGoogle Scholar
  7. Ding D, Liu Y, Shen YT, Xiang GL (2000) An efficient algorithm for computing a 3D form-closure grasp. In: Proceedings of IEEE international conference on robotics and automationGoogle Scholar
  8. Ding D, Liu YH, Wang MY (2001) On computing inmobilizing grasps of 3-D curved objects. In: Proceedings of the IEEE international symposium on computational intelligence in robotics and automation, pp 11–16Google Scholar
  9. El-Khoury S, Sahbani A (2010) A new strategy combining empirical and analytical approaches for grasping unknown 3D objects. Rob Auton Syst 58(5):497–507CrossRefGoogle Scholar
  10. Fernandez J, Walker I (1998) Biologically inspired robot grasping using genetic programming. In: Proceedings of IEEE international conference on robotics and automation, pp. 3032–3039Google Scholar
  11. Ferrari C, Canny J (1992) Planning optimal grasps. In: Proceedings of the IEEE international conference on robotics and automation, pp 2290–2295Google Scholar
  12. Fischer M, Hirzinger G (1997) Fast planning of precision grasps for 3D objects. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systemsGoogle Scholar
  13. Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, ReadingMATHGoogle Scholar
  14. Huebner K, Ruthotto S, Kragic D (2008) Minimum volume bounding box decomposition for shape approximation in robot grasping. In: Proceedings of the IEEE international conference on robotics and automation, pp 1628–1633Google Scholar
  15. Lakshminarayana K (1978) Mechanics of form closure. ASME Technical Report 78-DET-32Google Scholar
  16. Li JW, Liu H, Cai HG (2003) On computing three-finger force-closure grasps of 2D and 3D objects. IEEE Trans Rob Autom 19(1):155–161CrossRefGoogle Scholar
  17. Lippiello V, Siciliano B, Villani L (2010) Fast multi-fingered grasp synthesis based on object dynamic properties. In: Proceedings of the IEEE/ASME international conference on advanced intelligent mechatronics (AIM), pp 1134–1139Google Scholar
  18. Lippiello V, Siciliano B, Villani L (2013) Multi-fingered grasp synthesis based on the object dynamic properties. Rob Auton Syst 61(6):626–636CrossRefGoogle Scholar
  19. Liu YH, Lam ML, Ding D (2004) A complete and efficient algorithm for searching 3-D form closure grasps in the discrete domain. IEEE Trans Rob Autom 20(5):805–816CrossRefGoogle Scholar
  20. Mannepalli S, Dutta A, Saxena A (2010) A multi-objective GA based algorithm for 2D form and force closure grasp of prismatic objects. Int J Robot Autom 25(2)Google Scholar
  21. Markenscoff X, Ni L, Papadimitriou CH (1990) The geometry of grasping. Int J Rob Res 9(1):61–74CrossRefGoogle Scholar
  22. Mason MT (2001) Mechanics of robotic manipulation. MIT Press, CambridgeGoogle Scholar
  23. Miller AT, Knoop S, Allen PK, Christensen HI (2003) Automatic grasp planning using shape primitives. In: Proceedings of IEEE international conference on robotics and automationGoogle Scholar
  24. Mirtich B, Canny J (1994) Easily computable optimum grasps in 2D and 3D. In: Proceedings of IEEE international conference on robotics and automation, pp 739–747Google Scholar
  25. Ngo CY, Li VOK (1998) Fixed channel assignment in cellular radio networks using a modified genetic algorithm. IEEE Trans Veh Technol 47(1):163–172CrossRefGoogle Scholar
  26. Nguyen V (1986) The synthesis of stable force-closure grasps. Technical Report 905, MIT Artificial Intelligence LaboratoryGoogle Scholar
  27. Nguyen V (1988) Constructing force-closure grasps. Int J Rob Res 7(3):3–16CrossRefGoogle Scholar
  28. Niparnan N, Sudsang A (2004) Fast computation of 4-fingered force-closure grasps from surface points. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 3692–3697Google Scholar
  29. Reulaux F (1963) The kinematics of machinery. Dover, New YorkGoogle Scholar
  30. Roa MA, Suarez R (2007) Geometrical approach for grasp synthesis on discretized 3D objects. In: Proceedings of the 2007 IEEE/RSJ international conference on intelligent robots and systemsGoogle Scholar
  31. Roa MA, Suarez R (2009) Finding locally optimum force-closure grasps. Robot Comput Integr Manuf 25:536–544CrossRefGoogle Scholar
  32. Roa MA, Suárez R (2015) Grasp quality measures: review and performance. Auton Robots 38(1):65–88. doi: 10.1007/s10514-014-9402-3
  33. Suárez R, Roa M, Cornella J (2006) Grasp quality measures. Technical Report IOC-DT-P 2006-10, Universitat Politècnica de Catalunya, Institut d’Organització i Control de Sistemes IndustrialsGoogle Scholar
  34. Zhu X, Wang J (2003) Synthesis of force-closure grasps on 3D objects based on the Q distance. IEEE Trans Rob Autom 19(3):669–679Google Scholar
  35. Zhu X, Ding H (2004) Planning force-closure grasps on 3-D objects. In: Proceedings of IEEE international conference on robotics and automationGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Robotics and Remote Handling SectionIndira Gandhi Centre for Atomic Research (IGCAR)KalpakkamIndia
  2. 2.Department of Engineering DesignIndian Institute of Technology MadrasChennaiIndia

Personalised recommendations