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

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 

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