Abstract
Trajectory prediction is a key issue to a ping-pong robot, many algorithms have been developed. To get more robust and accuracy prediction under different serving conditions, this paper presents a new prediction method. The proposed method establishes two equivalent forms of the dynamic model of flying ball, where the discrete form for state estimation and the continuous form for trajectory prediction. The two forms share the same parameters’ value. According to force analysis, It is found that the model parameters are deeply related to ball’s state (position, velocity) . So we train the model parameters offline respect to ball’s state, instead of setting them to a constant value. This enables the model to be adapted accordingly online. Experimental results show the effectiveness and accuracy of the proposed method for the ball with different velocities.
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References
Acosta, L., Rodrigo, J.J., Mendez, J.A., Marichal, G.N., Sigut, M.: Ping-Pong player prototype. IEEE Robotics & Automation Magazine 10, 44–52 (2003)
Andersson, R.L.: Understanding and applying a robot ping-pong player’s expert controller. In: IEEE International Conference on Robotics and Automation, vol. 3, pp. 1284–1289 (1989)
Chen, X., Tian, Y., Huang, Q., Zhang, W., Yu, Z.: Dynamic model based ball trajectory prediction for a robot ping-pong player. In: IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 603–608 (2010)
Cross, R.: Bounce of a spinning ball near normal incidence. American Journal of Physics 73, 914–920 (2005)
Cross, R.: Measurement of horizontal coefficient of restitution for a superball and a tennis ball. American Journal of Physics 70, 482–489 (2001)
Huang, Y., Xu, D., Tan, M., Su, H.: Trajectory Prediction of Spinning Ball for Ping-Pong Player Robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3434–3439 (2011)
Matsushima, M., Hashimoto, T., Takeuchi, M., Miyazaki, F.: A learning approach to robotic table tennis. IEEE Transactions on Robotics 21, 767–771 (2005)
Matsushima, M., Hashimoto, T., Miyazaki, F.: Learning to the robot table tennis task - ball control & rally with a human. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 2962–2969 (2003)
Mulling, K., Kober, J., Peters, J.: A Biomimetic Approach to Robot Table Tennis. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1921–1926 (2010)
Nakashima, A., Ogawa, Y., Kobayashi, Y., Hayakawa, Y.: Modeling of Rebound Phenomenon of a Rigid Ball with Friction and Elastic Effects. In: American Control Conference (ACC), pp. 1410–1415 (2010)
Wang, Z., Lampert, M.K., Scholkopf, B., Peters, J.: Learning anticipation policies for robot table tennis. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 332–337 (2011)
Wang, Y., Sun, L., Liu, J., Yang, Q., Zhou, L., He, S.: A novel trajectory prediction approach for table-tennis robot based on nonlinear output feedback observer. In: IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1136–1141 (2010)
Zhang, Y., Wei, W., Yu, D., Zhong, C.: A tracking and predicting scheme for ping pong robot. Journal of Zhejiang University-SCIENCE C (Computer & Electronics) 12, 110–115 (2011)
Zhang, Z., Xu, D., Tan, M.: Visual Measurement and Prediction of Ball Trajectory for Table Tennis Robot. IEEE Transactions on Instrument and Meansurement 59, 3195–3205 (2010)
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© 2012 Springer-Verlag Berlin Heidelberg
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Zhang, Y., Xiong, R., Zhao, Y., Chu, J. (2012). An Adaptive Trajectory Prediction Method for Ping-Pong Robots. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7508. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33503-7_44
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DOI: https://doi.org/10.1007/978-3-642-33503-7_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33502-0
Online ISBN: 978-3-642-33503-7
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