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  • Volume 2014

    A Modified Biogeography Based Optimization (BBO) Algorithm for Time Optimal Motion Planning of 5 DOF PC-based Gryphon Robot
    (International Journal of Engineering Works)

    Vol. 1, Issue 2, PP. 38-44, Nov. 2014
    Keywords: Gryphon; XPC target, Tracking Problem, PC-based, Biogeoraphy based Optimazation

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    Gryphon as a 5-DOF robot has the ability to emulate the gestures of human arm and hand. It has pivots for rotation around the elbow and wrist. The wrist has two operative pivots, for rotation and elevation. Unlike previous works which concentrate on using WALLI as Gryphon robots own software, in this paper a PC-based and XPC Target’s real-time feature has been used.  . Recently, techniques based on metaheuristics of natural computing, mainly evolutionary algorithms (EA), have been successfully applied to a large number of robotic applications. The aim of this paper is to evaluate a modified Biogeography-based Optimization (BBO) approach based on mutation operator to solve the trajectory planning of a Gryphon. Computer simulation results and practical experiments demonstrate that accurate trajectory tracking can be achieved by using the proposed method.


    A.A. Ghavifekr: Faculty of Electrical and Computer Engineering , University of Tabriz,

    S. Ghaemi: Faculty of Electrical and Computer Engineering , University of Tabriz, Iran,

    R. Behinfaraz: Faculty of Electrical and Computer Engineering , University of Tabriz, Iran,



    Full Text


    A.A. Ghavifekr , S. Ghaemi, R. Behinfaraz "A Modified Biogeography Based Optimization (BBO) Algorithm for Time Optimal Motion Planning of 5 DOF PC-based Gryphon Robot" International Journal of Engineering works, Vol. 1, Issue 2, PP. 38-44, Nov. 2014  


    [1]     Atef A. Ata ”Optimal trajectory planning of manipulators: a review” Journal of engineering Science and Technology, voI.2, No.I, 2007, pp.32-54.

    [2]     H. Asare, and D. Wilson, “Computed Torque Method for the Control of Robotic Manipulators,” Internal Report AWN-86-SSSD-054,  Space Station Systems Division, Rockwell International, Downey, CA, May 1986.

    [3]     M. Wilson, “The role of seam tracking in robotic welding and bonding”, The Industrial Robot. vol. 29, no. 2, pp.132-137, 2002.

    [4]     Fan-Tien Cheng ; Tzung-Liang Hour ; York-Yin Sun ;Fan-Chu Kung,“Analysis and resolution of singularities for a 5-DOF GRYPHON manipulator”1995 IEEE Int. Conf. on Intelligent systems., pp. 4416–4421.

    [5]      S. Mootien, Ah King, R.T.F. , Rughooputh, H.C.S. “A Web-Based Interface for the Gryphon Robot” 2004 IEEE Int. Conf. on Industrial Technology., pp. 842–847.

    [6]     Z. S. Abo-Hammour, N. M. Mirza, S. M. Mirza, M. Arif, “Cartesian  path generation of robot manipulators using continuous genetic algorithms”, Robotics and Systems Vol. 41, No. 4, pp. 179-223,2002.

    [7]      K. Sugihara, J.Smith, “Genetic algorithms for adaptive motion   of an autonomous mobile robot,” In: Proc. Of IEEE Intl. Symposium on Computational Intelligence in Robotics and Automation, 1997,pp. 138– 143.

    [8]     I.ALtaharwa, A. Sheta, and M.Alweshah,. “ A mobile robot path planning using genetic algorithm in static environment,” Journal of Computer Science, vol.4, pp.341-344, 2008

    [9]      X. S. Wang, M. L. Hao, Y. H. Cheng, “On the use of differential evolution for forward kinematics of parallel manipulators”, Applied Mathematics and Computation, Vol. 205, No. 2, pp. 760-769, 2008.

    [10]  M. A. P. Garcia, O. Montiel, O. Castillo, R. Sepúlveda, P. Melin, “Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation”, Applied Soft Computing, Vol. 9, No. 3, pp. 1102-1110, 2009.

    [11]  M.Saska, M. Macas, L. Preucil, L. Lhotska, “Robot path planning using particle swarm optimization of ferguson splines,” in Proceedings of the IEEE Conference on Emerging Technologies and Factory Automation, Prague, 2006,pp. 833-839..

    [12]  D.Simon, “Biogeography-based optimization,” IEEE Trans. on Evo. Com. vol.12,pp.702-713, 2008..

    [13]  H. Ma, D. Simon, “Blended Biogeography-based optimization for constrained optimization”, Evolutionary Comp., Vol. 24, pp. 517-525, 2011.

    [14]  H. Ma M. Fei, Z. Ding, J. Jin, “Biogeography-based optimization  ensemble of migration models for global numerical optimization”, Proc. IEEE Congress on Evolutionary Computation, June 2012.

    [15]  Mark W.Spong, Seth Hutchinson, M Vidyasagar, Robot Modeling and Control. Wiley Press, 2006