Skip to main content
SpringerLink
Log in

Short-Baseline Binocular Vision System for a Humanoid Ping-Pong Robot

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

We develop a short-baseline vision system for a humanoid ping-pong robot. The vision system can provide four-dimensional space-time information and can predict the future trajectory of a ball. Short baseline poses special challenges for achieving sufficient 3-D reconstruction and prediction accuracy within limited processing time. We propose two algorithms including direct calibration of projection matrix and Gaussian-fitting based ball-center location to guarantee the 3-D reconstruction accuracy; we propose algorithm of five-point based ball representation and utilize the constraint of ball detecting region to guarantee the processing speed; we also propose algorithm of smoothing-based trajectory prediction to improve the prediction accuracy. Experimental results show the accuracy and the speed of our vision system can meet the requirements of a humanoid ping-pong robot.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Andersson, R.L.: Dynamic sensing in a ping-pong playing robot. IEEE Trans. Robot. Autom. 5(6), 728–739 (1989)

    Article  Google Scholar 

  2. Matsushima, M., Hashimoto, T., Takeuchi, M., Miyazaki, F.: A learning approach to robotic table tennis. IEEE Trans. Robot. Autom. 21(4), 767–771 (2005)

    Google Scholar 

  3. Takeuchi, M., Miyazaki, F., Matsushima, M., Kawatani, M., Hashimoto, T.: Dynamic dexterity for the performance of ‘wall-bouncing’ tasks. IEEE Int. Conf. Robot. Autom. 2, 1559–1564 (2002)

    Google Scholar 

  4. Matsushima, M., Hashimoto, T., Miyazaki, F.: Learning to the robot table tennis task-ball control and rally with a human. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 2962–2969 (2003)

  5. Muelling, K., Peters, J.: A computational model of human table tennis for robot application. In: Proceedings of Autonome Mobile Systeme, vol. 3, pp. 1309–1314 (2009)

  6. Angel, L., Sebastian, J.M., Saltaren, R., Aracil, R., SanPedro, J.: RoboTenis: optimal design of a parallel robot with high performance. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Canada, pp. 2134–2139 (2005)

  7. Angel, L., Traslosheros, A., Sebastian, J.M., Pari, L., Carelli, R., Roberti, F.: Vision-based control of the robotenis system. Lect. Notes Control Inf. Sci. 370, 229–240 (2009)

    Article  Google Scholar 

  8. Brunnett, G., Rusdorf, S., Lorenz, M.: V-Pong: an immersive table tennis simulation. IEEE Comput. Graph. Appl. 26(4), 10–13 (2006)

    Article  Google Scholar 

  9. Rusdorf, S., Brunnett, G., Lorenz, M., Winkler, T.: Real time interaction with a humanoid avatar in an immersive table tennis simulation. IEEE Trans. Vis. Comput. Graph. 13(1), 15–25 (2007)

    Article  Google Scholar 

  10. Acosta, L., Rodrigo, J.J., Mendez, J.A., Marichal, G.N., Sigut, M.: Ping-pong-player prototype—a PC-based, low-cost ping-pong robot. IEEE Robot. Autom. Mag. 10(4), 44–52 (2003)

    Article  Google Scholar 

  11. Zhang, Z., Xu, D.: Design of high-speed vision system and algorithms based on distributed parallel processing architecture for target tracking. In: Proceedings of the 7th Asian Control Conference, pp. 1638–1643 (2009)

  12. Sabzevari, R., Shahri, A.: Object detection and localization system based on neural networks for robo-pong. In: Proceedings of the 5th International Symposium on Mechatronics and its Applications (2008)

  13. Sorensen, V., Ingvaldsen, R.P., Whiting, H.T.: The application of co-ordination dynamics to the analysis of discrete movements using table tennis as a paradigm skill. Biol. Cybern. 85(1), 27–38 (2001)

    Article  Google Scholar 

  14. Klarquist, W.N., Bovik, A.C.: Fovea: a foveated vergent active stereo vision system for dynamic three-dimensional scene recovery. IEEE Trans. Robot. Autom. 14(5), 755–770 (1998)

    Article  Google Scholar 

  15. Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)

    Article  Google Scholar 

  16. Harris, C., Sttephens, M.: A combined corner and edge detector. In: Proceeding of 4th Alvey Vision Conference, pp. 147–151 (1988)

  17. Hartley, R.I.: In defense of the eight-point algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 19(6), 580–593 (1997)

    Article  Google Scholar 

  18. Wong, P.K.C.: Developing an intelligent table tennis umpiring system: identifying the ball from the scene. In: Second Asia International Conference on Modelling & Simulation, pp. 445–450 (2008)

  19. Chen, H., Chen, H.S., Lee, S.: Physics-based ball tracking in volleyball videos with its applications to set type recognition and action detection. In: IEEE International Conference on Pattern Recognition, vol. 1, pp. 1097–1100 (2007)

  20. Zhang, P., Lu, T.: Real-time motion planning for a volleyball robot task based on a multi-agent technique. J. Intell. Robot. Syst. 49(4), 355–366 (2007)

    Article  Google Scholar 

  21. Tong, X.F., Lu, H.Q., Liu, Q.S.: An effective and fast soccerball detection and tracking method. In: Proc. IEEE Int. Conf. Pattern Recognit., Cambridge, U.K., vol. 4, pp. 795–798 (2004)

  22. Yamada, A., Shirai, Y., Miura, J.: Tracking players and a ball in video image sequence and estimating camera parameters for 3-D interpretation of soccer games. In: Proc. IEEE Int. Conf. Pattern Recognit., Québec, QC, Canada, vol. 1, pp. 303–306 (2002)

  23. Mouhamed, M., Toker, O., Harthy, A.: A 3-D vision-based man–machine interface for hand-controlled telerobot. IEEE Trans. Ind. Electron. 52(1), 306–319 (2005)

    Article  Google Scholar 

  24. Miyazaki, F., Matsushima, M., Takeuchi, M.: Learning to dynamically manipulate: a table tennis robot controls a ball and rallies with a human being. In: Advances in Robot Control, pp. 317–341. Springer (2005)

  25. Smith, C., Bratt, M., Christensen, H.I.: Teleoperation for a ball-catching task with significant dynamics. Neural Netw. 24, 604–620 (2008) (Special Issue on Robotics and Neuroscience)

    Google Scholar 

  26. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, People’s Republic of China

    Jian-Dong Tian, Jing Sun & Yan-Dong Tang

  2. Graduate School of the Chinese Academy of Sciences, 100039, Beijing, People’s Republic of China

    Jian-Dong Tian & Jing Sun

Authors
  1. Jian-Dong Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yan-Dong Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jian-Dong Tian.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tian, JD., Sun, J. & Tang, YD. Short-Baseline Binocular Vision System for a Humanoid Ping-Pong Robot. J Intell Robot Syst 64, 543–560 (2011). https://doi.org/10.1007/s10846-011-9554-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-011-9554-8

Keywords

Search

Navigation