Path Trajectory Prediction of Rapidly Rotated Ping Pong Ball after Hitting

Y Q Wang

Abstract


The path trajectory prediction of rapidly rotated ping pong ball after hitting plays a great role in the training of athletes and even in the competition. It can improve the level and efficiency of training. Therefore it is necessary to optimize the parameters of ping pong ball such as position, angle and speed after hitting to complete data collection. This study established a prediction model of the path trajectory of rapidly rotated ping pong ball after hitting and predicted the path trajectory using extreme learning machine (ELM) algorithm and back propagation (BP) neural network. The results were compared to find out the better algorithm. Moreover the two algorithms were improved. The result demonstrated that the improved EML algorithm could realize minimum error.


Full Text:

PDF

References


Zhao, Y., R. Xiong, and Y. Zhang, Model based motion state estimation and trajectory prediction of spinning ball for ping-pong robots using expectation-maximization algorithm. Journal of Intelligent & Robotic Systems, 2017. 87(3-4): p. 407-423. https://doi.org/10.1007/s10846-017-0515-8

Pan, M. and I. Mizuuchi, 1P1-P08 Trajectory Prediction Method for a Table Tennis Robot based on the Observation of Translational and Rotational Speed of the Ball(Robots for Amusement and Entertainment). The Proceedings of JSME annual Conference on Robotics and Mechatronics, 2012. 2012: p. _1P1-P08_1-_1P1-P08_4. https://doi.org/10.1299/jsmermd.2012._1p1-p08_1

Tamaki, S. and H. Saito, Reconstruction of 3D Trajectories for Performance Analysis in Table Tennis. Computer Vision and Pattern Recognition Workshops. IEEE, 2013: p. 1019-1026. https://doi.org/10.1109/cvprw.2013.148

Koç, O., G. Maeda, and J. Peters, A new trajectory generation framework in robotic table tennis. Ieee/rsj International Conference on Intelligent Robots and Systems. IEEE, 2016: p. 3750-3756. https://doi.org/10.1109/iros.2016.7759552

Asano, T., Y. Serikawa, K. Ishiguro, and S. Itoh, Analysis of Tennis Ball Rotation and Trajectory by Image Processing. Journal of the Japan Society for Precision Engineering, 2016. 82(2): p. 54-7. https://doi.org/10.2493/jjspe.82.168

Elsaadany A, Yi W J. Accurate trajectory prediction for typical artillery projectile. Control Conference. IEEE, 2014:6368-6374. https://doi.org/10.1109/chicc.2014.6896037

Zhao, Y., Y. Zhang, R. Xiong, and J.J. Wang. Optimal state estimation of spinning ping-pong ball using continuous motion model. IEEE Transactions on Instrumentation and Measurement, 2015. 64(8): p. 2208-2216. https://doi.org/10.1109/tim.2014.2386951

Deo, N. and M. M. Trivedi. [IEEE 2018 IEEE Intelligent Vehicles Symposium (IV) - Changshu (2018.6.26-2018.6.30)] 2018 IEEE Intelligent Vehicles Symposium (IV) - Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs. 2018. p. 1179-1184. https://doi.org/10.1109/ivs.2018.8500493

Wang, Q., Z. Zhang, Z. Wang, Y. Wang, and W. Zhou. The trajectory prediction of spacecraft by grey method. Measurement Science and Technology, 2016. 27(8): p. 085011. https://doi.org/10.1088/0957-0233/27/8/085011

Cooper, G.R. and M. Costello. Trajectory prediction of spin-stabilized projectiles with a liquid payload. Journal of Spacecraft and Rockets, 2011. 48(4): p. 664-670. https://doi.org/10.2514/1.52564

Huang, G.B., Q.Y. Zhu, and C.K. Siew. Extreme learning machine: a new learning scheme of feed forward neural networks. In Proceedings of the International Joint Conference on Neural Net works (IJCNN 2004). Budapest, Hungary, July 2004: p. 985–990. https://doi.org/10.1109/ijcnn.2004.1380068

Huang, G.B., Q.Y. Zhu, nad C.K. Siew, Extreme learning machine: Theory and applications. Neurocomputing, 2006. 70: p. 489–501. https://doi.org/10.1016/j.neucom.2005.12.126

Gould, C.M., F. Diella, A. Via, nad P. Puntervoll, ELM: the status of the 2010 eukaryotic linear motif resource. Nucleic Acids Research, 2010. 38(Database issue): p. 167-80.

Gouw, M., S. Michael, H. Sámanosánchez, and M. Kumar, The eukaryotic linear motif resource - 2018 update. Nucleic Acids Research, 2017. 46(Database issue).

Dinkel, H., S. Michael, R.J. Weatheritt, and N. Davey, ELM--the database of eukaryotic linear motifs. Nucleic Acids Research, 2012. 40(Database issue): p. D242-D251. https://doi.org/10.1093/nar/gkr1064

Guan, K., Z. Wei, and B. Yin, SOC Prediction Method of a New Lithium Battery Based on GA-BP Neural Network. Proceedings of the 4th International Conference on Computer Engineering and Networks. Springer International Publishing, 2015: p. 141-153. https://doi.org/10.1007/978-3-319-11104-9_17

Zhao, L., J. Wang, and X. Chen, BP Neural Network with Regularization and Sensor Array for Prediction of Component Concentration of Mixed Gas. International Symposium on Neural Networks. Springer, Cham, 2018: p. 541-548. https://doi.org/10.1007/978-3-319-92537-0_62

Song, J. Engine emissions predicted particle swarm optimization of BP neural network. International Journal of Earth Sciences & Engineering, 2015. 8(1): p. 319-324.




DOI: http://dx.doi.org/10.21152/1750-9548.13.4.351

Copyright (c) 2019 Y Q Wang

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.