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CLC number: TP242.6

On-line Access: 2011-02-08

Received: 2009-08-25

Revision Accepted: 2010-12-16

Crosschecked: 2010-12-30

Cited: 10

Clicked: 3435

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.2 P.110-115


A tracking and predicting scheme for ping pong robot

Author(s):  Yuan-hui Zhang, Wei Wei, Dan Yu, Cong-wei Zhong

Affiliation(s):  School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   zhangyh23@gmail.com, wwei@cee.zju.edu.cn

Key Words:  Ping pong robot, Calibration, Trajectory tracking, Kalman filter, Neural network

Yuan-hui Zhang, Wei Wei, Dan Yu, Cong-wei Zhong. A tracking and predicting scheme for ping pong robot[J]. Journal of Zhejiang University Science C, 2011, 12(2): 110-115.

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T1 - A tracking and predicting scheme for ping pong robot
A1 - Yuan-hui Zhang
A1 - Wei Wei
A1 - Dan Yu
A1 - Cong-wei Zhong
J0 - Journal of Zhejiang University Science C
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C0910528

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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