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

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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.

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References

  • Bicchi A (1995) On the closure properties of robotic grasping. Int J Robot Res 14(4):319–334

    Article  Google Scholar 

  • 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 systems

  • 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 567–578

  • Chen I, Burdick J (1993) Finding antipodal point grasps on irregularly shaped objects. IEEE Trans Rob Autom 9(4):507–512

    Article  Google Scholar 

  • Daoud N, Gazeau J, Zeghloul S, Arsicault M (2011) A fast grasp synthesis method for online manipulation. Rob Auton Syst 59(6):421–427

    Article  Google Scholar 

  • Deb K (2009) Optimization for engineering design: algorithms and examples. PHI Learning Pvt. Ltd, New Delhi

    Google Scholar 

  • 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 automation

  • 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 11–16

  • El-Khoury S, Sahbani A (2010) A new strategy combining empirical and analytical approaches for grasping unknown 3D objects. Rob Auton Syst 58(5):497–507

    Article  Google Scholar 

  • Fernandez J, Walker I (1998) Biologically inspired robot grasping using genetic programming. In: Proceedings of IEEE international conference on robotics and automation, pp. 3032–3039

  • Ferrari C, Canny J (1992) Planning optimal grasps. In: Proceedings of the IEEE international conference on robotics and automation, pp 2290–2295

  • Fischer M, Hirzinger G (1997) Fast planning of precision grasps for 3D objects. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems

  • Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  • 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 1628–1633

  • Lakshminarayana K (1978) Mechanics of form closure. ASME Technical Report 78-DET-32

  • 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):155–161

    Article  Google Scholar 

  • 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 1134–1139

  • Lippiello V, Siciliano B, Villani L (2013) Multi-fingered grasp synthesis based on the object dynamic properties. Rob Auton Syst 61(6):626–636

    Article  Google Scholar 

  • 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):805–816

    Article  Google Scholar 

  • 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)

  • Markenscoff X, Ni L, Papadimitriou CH (1990) The geometry of grasping. Int J Rob Res 9(1):61–74

    Article  Google Scholar 

  • Mason MT (2001) Mechanics of robotic manipulation. MIT Press, Cambridge

    Google Scholar 

  • Miller AT, Knoop S, Allen PK, Christensen HI (2003) Automatic grasp planning using shape primitives. In: Proceedings of IEEE international conference on robotics and automation

  • Mirtich B, Canny J (1994) Easily computable optimum grasps in 2D and 3D. In: Proceedings of IEEE international conference on robotics and automation, pp 739–747

  • Ngo CY, Li VOK (1998) Fixed channel assignment in cellular radio networks using a modified genetic algorithm. IEEE Trans Veh Technol 47(1):163–172

    Article  Google Scholar 

  • Nguyen V (1986) The synthesis of stable force-closure grasps. Technical Report 905, MIT Artificial Intelligence Laboratory

  • Nguyen V (1988) Constructing force-closure grasps. Int J Rob Res 7(3):3–16

    Article  Google Scholar 

  • 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 3692–3697

  • Reulaux F (1963) The kinematics of machinery. Dover, New York

    Google Scholar 

  • 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 systems

  • Roa MA, Suarez R (2009) Finding locally optimum force-closure grasps. Robot Comput Integr Manuf 25:536–544

    Article  Google Scholar 

  • Roa MA, Suárez R (2015) Grasp quality measures: review and performance. Auton Robots 38(1):65–88. doi:10.1007/s10514-014-9402-3

  • 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 Industrials

  • Zhu X, Wang J (2003) Synthesis of force-closure grasps on 3D objects based on the Q distance. IEEE Trans Rob Autom 19(3):669–679

    Google Scholar 

  • Zhu X, Ding H (2004) Planning force-closure grasps on 3-D objects. In: Proceedings of IEEE international conference on robotics and automation

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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.

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Authors and Affiliations

  1. Robotics and Remote Handling Section, Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam, Tamil Nadu, 603 102, India

    V. Rakesh, S. Murugan & S. Venugopal

  2. Department of Engineering Design, Indian Institute of Technology Madras, Chennai, 600 036, India

    Utkarsh Sharma & T. Asokan

Authors
  1. V. Rakesh
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  2. Utkarsh Sharma
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  3. S. Murugan
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  4. S. Venugopal
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  5. T. Asokan
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Correspondence to V. Rakesh.

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The authors declare that they have no conflict of interest.

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

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Communicated by V. Loia.

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Rakesh, V., Sharma, U., Murugan, S. et al. Optimizing force closure grasps on 3D objects using a modified genetic algorithm. Soft Comput 22, 759–772 (2018). https://doi.org/10.1007/s00500-016-2377-6

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  • DOI: https://doi.org/10.1007/s00500-016-2377-6

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