Motion Planning of a Dual Manipulator System for Table Tennis

  • Guowei Zhang
  • Cong Wang
  • Bin Li
  • Huaibing Zheng
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 194)


This paper describes the design and development of a novel dual manipulator system for table tennis as an application of Human-Robot Interaction (HRI). To hit table tennis quickly, a method to obtain time-constrained trajectory joining two way-points is developed and implemented. Because the quintic polynomial trajectory is a smooth curve and can reduce jerk, it appears to be excellent choice for hitting task. Five phase quintic polynomials are adopted to fit the smooth trajectory in joint space under the constraint of robotic kinematics parameters. The boundary conditions of five phase qunitic polynomials used to compute the trajectories are discussed under different initial kinematics conditions. Experimental results of actual robotic system with dual manipulators and vision system show that the proposed method works well.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Andersson, R.L.: A robot ping-pong player: Experiment in real-time intelligent control. The MIT Press, Cambridge (1988)Google Scholar
  2. 2.
    Ata, A.A.: Optimal trajectory planning of manipulators: a review. Journal of Engineering Science and Technology 2(1), 32–54 (2007)Google Scholar
  3. 3.
    Buttazzo, G.C., Allotta, B., Fanizza, F.P.: Mousebuster: a robot system for catching fast moving objects by vision. In: The 1993 IEEE International Conference on Robotics and Automation, pp. 932–937 (1993)Google Scholar
  4. 4.
    Gong, H.L., Li, B., Zhang, G.W., et al.: Design of a modular control system for humanoid robot dexterous servo arm. Chinese Journal of Scientific Instrument 31(8), 106–110 (2010)MathSciNetGoogle Scholar
  5. 5.
    Hartley, J.: Toshiba progress towards sensory control in real time. Indust. Robot 14(1), 325–329 (1999)Google Scholar
  6. 6.
    Hujic, D., Croft, E.A., Zak, G., et al.: The robotic interception of moving objects in industrial settings: strategy development and experiment. IEEE/ASME Transactions on Mechatronics 3(3), 225–239 (1998)CrossRefGoogle Scholar
  7. 7.
    Kober, J., Mlling, K., Kroemer, O., et al.: Movement templates for learning of hitting and batting. In: The 2010 IEEE International Conference on Robotics and Automation, pp. 853–858 (2010)Google Scholar
  8. 8.
    Macfarlane, S.: Online smooth trajectory planning for manipulators. Dissertation, The University of New Brunswick (1999)Google Scholar
  9. 9.
    Matsushima, M., Hashimoto, T., Miyazaki, F.: Learning to the robot table tennis task – Ball control & rally with a human. In: The 2003 IEEE International Conference on Systems Man and Cybernetics, vol. 3, pp. 2962–2969 (2003)Google Scholar
  10. 10.
    Rajan, R.: Motion Planning of Dynamic Systems. Dissertation, The University of Texas (2001)Google Scholar
  11. 11.
    Zhang, B., Xiong, R., Wu, J.: Kinematics analysis of a novel 7-DOF humanoid manipulator for table tennis. In: The 2011 International Conference on Electronics, Communications and Control (ICECC), pp. 1524–1528 (2011)Google Scholar
  12. 12.
    Zhang, Q., Xie, Z.W., Liu, Y.W., et al.: High Dynamic Humanoid Robot Arm for Ping-Pong Playing. Applied Mechanics and Materials 80-81, 1081–1085 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Guowei Zhang
    • 1
    • 2
  • Cong Wang
    • 3
  • Bin Li
    • 3
  • Huaibing Zheng
    • 3
  1. 1.Shenyang Institute of AutomationChinese Academy of SciencesShenyangChina
  2. 2.Graduate School of the Chinese Academy of SciencesBeijingChina
  3. 3.Institute Shenyang Institute of AutomationChinese Academy of SciencesShenyangChina

Personalised recommendations