Grasping force based manipulation for multifingered hand-arm Robot using neural networks

Chun-Hsu Ko; Jing-Kun Chen

doi:10.3934/naco.2014.4.59

Article Contents

2014, Volume 4, Issue 1: 59-74. Doi: 10.3934/naco.2014.4.59

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Grasping force based manipulation for multifingered hand-arm Robot using neural networks

Chun-Hsu Ko^1, and
Jing-Kun Chen^1,

1.
Department of Electrical Engineering, I-Shou University, Kaohsiung City 84001

Received: June 2013

Revised: November 2013

Published: December 2013

Abstract / Introduction Related Papers Cited by

Abstract

Multifingered hand-arm robots play an important role in dynamic manipulation tasks. They can grasp and move various shaped objects. It is important to plan the motion of the arm and appropriately control the grasping forces for the multifingered hand-arm robots. In this paper, we perform the grasping force based manipulation of the multifingered hand-arm robot by using neural networks. The motion parameters are analyzed and planned with the constraint of the multi-arms kinematics. The optimal grasping force problem is recast as the second-order cone program. The semismooth Newton method with the Fischer-Burmeister function is then used to efficiently solve the second-order cone program. The neural network manipulation system is obtained via the fitting of the data that are generated from the optimal manipulation simulations. The simulations of optimal grasping manipulation are performed to demonstrate the effectiveness of the proposed approach.

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Mathematics Subject Classification: 70E60, 90C90.

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References

[1]	P. H. Borgstrom, M. A. Batalinx, G. S. Sukhatmez and W. J. Kaiser, Weighted barrier functions for computation of force distributions with friction cone constraints, IEEE Int. Conf. Robot. Autom., (2010), 785-792.
[2]	S. P. Boyd and B. Wegbreit, Fast computation of optimal contact forces, IEEE Trans. Robot., 23 (2007), 1117-1132.
[3]	W. Chung, C. Rhee, Y. Shim, H. Lee and S. Park, Door-opening control of a service robot using the multifingered robot hand, IEEE Trans. Ind. Electron., 56 (2009), 3975-3984.
[4]	L. Han, J. C. Trinkle and Z. X. Li, Grasp analysis as linear matrix inequality problems, IEEE Trans. Robot. Autom., 16 (2000), 663-674.
[5]	M. H. Hassoun, Fundamentals of Artificial Neural Networks, MIT Press, 1995.
[6]	U. Helmke, K. Huper and J. B. Moore, Quadratically convergent algorithms for optimal dexterous hand grasping, IEEE Trans. Robot. Autom., 18 (2002), 138-146.
[7]	A. Kawamura, K. Tahara, R. Kurazume and T. Hasegawa, Dynamic grasping for an arbitrary polyhedral object by a multi-fingered hand-arm system, IEEE/RSJ Int. Conf. Intel. Robots Syst., (2009), 2264-2270.
[8]	C. H. Ko, J. S. Chen and C. Y. Yang, Recurrent neural networks for solving second-order cone programs, Neurocomputing, 74 (2011), 3646-3653.
[9]	C. H. Ko, S. H. Lin and J. K. Chen, Motion planning of multifingered hand-arm system with optimal grasping force, IEEE 2nd Int. Symp. Next-Generation Electron., (2013), 262-265.
[10]	V. Lippiello, B. Siciliano and L. Villani, A grasping force optimization algorithm for multiarm robots with multifingered hands, IEEE Trans. Robot., 29 (2013), 55-67.
[11]	Y. H. Liu, Qualitative test and force optimization of 3D frictional form-closure grasps using linear programming, IEEE Trans. Robot. Autom., 15 (1999), 163-173.
[12]	R. Michalec and A. Micaelli, Optimal tightening forces for multi-fingered robust manipulation, IEEE/RSJ Int. Conf. Intel. Robots Syst., (2009), 4160-4167.
[13]	R. M. Murray, Z. Li and S. S. Sastry, Mathematical Introduction to Robotic Manipulation, CRC Press, 1994.
[14]	S. H. Pan and J. S. Chen, A semismooth Newton method for SOCCPs based on a one-parametric class of SOC complementarity functions, Comp. Optim. Appl., 45 (2010), 59-88.doi: 10.1007/s10589-008-9166-9.
[15]	D. Prattichizzo, M. Malvezzi, M. Aggravi and T. Wimböck, Object motion-decoupled internal force control for a compliant multifingered hand, IEEE Int. Conf. Robot. Autom., (2012), 1508-1513.
[16]	L. Qi and J. Sun, A nonsmooth version of Newtons method, Mathematical Programming, 58 (1993), 353-367.doi: 10.1007/BF01581275.
[17]	H. Scharfe, N. Hendrich and J. Zhang, Hybrid physics simulation of multi-fingered hands for dexterous in-hand manipulation, IEEE Int. Conf. Robot. Autom., (2012), 3777-3783.
[18]	T. Tsuji, K. Harada and K. Kaneko, Easy and fast evaluation of grasp stability by using ellipsoidal approximation of friction cone, IEEE/RSJ Int. Conf. Intel. Robots Syst., (2009), 1830-1837.
[19]	J. Xu and Z. Li, A kinematic model of finger gaits by multifingered hand as hybrid automaton, IEEE Trans. Autom. Sci. Eng., 50 (2008), 467-479.