Optimizing force closure grasps on 3D objects using a modified genetic algorithm
- 154 Downloads
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.
KeywordsRobot grasp synthesis Tessellated object Convex hull Grasp quality Modified genetic algorithm (GA)
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.
This article does not contain any studies with human participants or animals performed by any of the authors.
- 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
- 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
- Deb K (2009) Optimization for engineering design: algorithms and examples. PHI Learning Pvt. Ltd, New DelhiGoogle 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 automationGoogle Scholar
- 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
- 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
- Ferrari C, Canny J (1992) Planning optimal grasps. In: Proceedings of the IEEE international conference on robotics and automation, pp 2290–2295Google Scholar
- 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
- 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
- Lakshminarayana K (1978) Mechanics of form closure. ASME Technical Report 78-DET-32Google 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 1134–1139Google 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)Google Scholar
- Mason MT (2001) Mechanics of robotic manipulation. MIT Press, CambridgeGoogle 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 automationGoogle Scholar
- 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
- Nguyen V (1986) The synthesis of stable force-closure grasps. Technical Report 905, MIT Artificial Intelligence LaboratoryGoogle 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 3692–3697Google Scholar
- Reulaux F (1963) The kinematics of machinery. Dover, New YorkGoogle 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 systemsGoogle 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 IndustrialsGoogle Scholar
- 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
- Zhu X, Ding H (2004) Planning force-closure grasps on 3-D objects. In: Proceedings of IEEE international conference on robotics and automationGoogle Scholar