Dexterity Optimization of a Three Degrees of Freedom DELTA Parallel Manipulator

  • Vitor Gaspar Silva
  • Mahmoud Tavakoli
  • Lino Marques
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 253)

Abstract

This paper demonstrates dexterity optimization of a Delta-like three degrees of freedom (3 DOF) spatial parallel manipulator. The parallel manipulator consists of three identical chains and is able to move on all three translational axes. In order to optimize the manipulator in term of dexterity, a Genetic Algorithm (GA) global search method was applied. This algorithm aims to propose the best design parameters such as the length of the links which results in a better dexterity. Results of the optimization are presented.

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References

  1. 1.
    Merlet, J.P.: Parallel Robots. Solid Mechanics and Its Applications, vol. 128. Springer, Heidelberg (2006)MATHGoogle Scholar
  2. 2.
    Pierrot, F., Fournier, A., Dauchex, P.: Towards a fully-parallel 6 dof robot for high-speed applications. Robotics and Automation (April 1991)Google Scholar
  3. 3.
    Gosselin, C.: A new architecture of planar three-degree-of-freedom parallel manipulator. Robotics and Automation, 3738–3743 (April 1996)Google Scholar
  4. 4.
    Stewart, D.: A platform with six degrees of freedom. Proceedings of the Institution of Mechanical Engineers 180(1), 371–386 (1965)CrossRefGoogle Scholar
  5. 5.
    Hunt, K.H.: Structural Kinematics of In-Parallel-Actuated Robot-Arms. Journal of Mechanisms Transmissions and Automation in Design 105 (1983)Google Scholar
  6. 6.
    Clavel, R.: DELTA, a fast robot with parallel geometry. In: Burckhardt, C.W. (ed.) Proc of the 18th International Symposium on Industrial Robots, pp. 91–100. Springer, New York (1988)Google Scholar
  7. 7.
    Agrawal, S.: Workspace boundaries of in-parallel manipulator systems. In: Fifth International Conference on Advanced Robotics, Robots in Unstructured Environments, ICAR 1991, vol. 2, pp. 1147–1152 (1991)Google Scholar
  8. 8.
    Gallant, M., Boudreau, R.: The synthesis of planar parallel manipulators with prismatic joints for an optimal, singularity-free workspace. Journal of Robotic Systems 19(1), 13–24 (2002)CrossRefMATHGoogle Scholar
  9. 9.
    Stamper, R., Tsai, L.W., Walsh, G.: Optimization of a three dof translational platform for well-conditioned workspace. In: Proceedings of the1997 IEEE International Conference on Robotics and Automation, vol. 4, pp. 3250–3255 (April 1997)Google Scholar
  10. 10.
    Tavakoli, M., Zakerzadeh, M., Vossoughi, G., Bagheri, S., Salarieh, H.: A novel serial/parallel pole climbing/manipulating robot: Design, kinematic analysis and workspace optimization with genetic algorithm. In: 21st International Symposium on Automation and Robotics in Construction, Korea (2004)Google Scholar
  11. 11.
    Gosselin, C., Angeles, J.: A global performance index for the kinematic optimization of robotic manipulators. Journal of Mechanical Design 113, 220 (1991)CrossRefGoogle Scholar
  12. 12.
    Salisbury, J.K., Craig, J.J.: Articulated hands force control and kinematic issues. The International Journal of Robotics Research 1(1), 4–17 (1982)CrossRefGoogle Scholar
  13. 13.
    Alciatore, D., Ng, C.: Determining manipulator workspace boundaries using the monte carlo method and least squares segmentation. In: 23rd ASME Mechanisms Conference, pp. 141–146. American Society of Mechanical Engineers, Minneapolis (1994)Google Scholar
  14. 14.
    Koza, J.R., Keane, M.A., Streeter, M.J., Mydlowec, W., Yu, J., Lanza, G.: Genetic programming IV: Routine human-competitive machine intelligence. Springer (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Vitor Gaspar Silva
    • 1
  • Mahmoud Tavakoli
    • 1
  • Lino Marques
    • 1
  1. 1.Department of Electrical and Computer Engineering, Institute of Systems and RoboticsUniversity of CoimbraCoimbraPortugal

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