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Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (2): 110-115    DOI: 10.1631/jzus.C0910528
    
A tracking and predicting scheme for ping pong robot
Yuan-hui Zhang, Wei Wei*, Dan Yu, Cong-wei Zhong
School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
A tracking and predicting scheme for ping pong robot
Yuan-hui Zhang, Wei Wei*, Dan Yu, Cong-wei Zhong
School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
 全文: PDF(238 KB)  
摘要： We describe a new tracking and predicting scheme applied to a lab-made ping pong robot. The robot has a monocular vision system comprised of a camera and a light. We propose an optimized strategy to calibrate the light center using the least square method. An ellipse fitting method is used to precisely locate the center of ball and shadow on the captured image. After the triangulation of the ball position in the world coordinates, a tracking algorithm based on a Kalman filter outputs an accurate estimation of the flight states including the ball position and velocity. Furthermore, a neural network model is constructed and trained to predict the following flight path. Experimental results show that this scheme can achieve a good predicting precision and success rate of striking an incoming ball. The robot can achieve a success rate of about 80% to return a flight ball of 5 m/s to the opposite court.
关键词： Ping pong robot;  Calibration;  Trajectory tracking;  Kalman filter;  Neural network    
Abstract: We describe a new tracking and predicting scheme applied to a lab-made ping pong robot. The robot has a monocular vision system comprised of a camera and a light. We propose an optimized strategy to calibrate the light center using the least square method. An ellipse fitting method is used to precisely locate the center of ball and shadow on the captured image. After the triangulation of the ball position in the world coordinates, a tracking algorithm based on a Kalman filter outputs an accurate estimation of the flight states including the ball position and velocity. Furthermore, a neural network model is constructed and trained to predict the following flight path. Experimental results show that this scheme can achieve a good predicting precision and success rate of striking an incoming ball. The robot can achieve a success rate of about 80% to return a flight ball of 5 m/s to the opposite court.
Key words: Ping pong robot    Calibration    Trajectory tracking    Kalman filter    Neural network
收稿日期: 2009-08-25 出版日期: 2011-02-08
CLC:  TP242.6  
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Yuan-hui Zhang
Wei Wei
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引用本文:

Yuan-hui Zhang, Wei Wei, Dan Yu, Cong-wei Zhong. A tracking and predicting scheme for ping pong robot. Front. Inform. Technol. Electron. Eng., 2011, 12(2): 110-115.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C0910528    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2011/V12/I2/110

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