Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1631/jzus.C0910528 | 浙江大学学报C辑(计算机与电子)(英文版) |
Keywords | Field | DocType |
neural network,kalman filter,trajectory tracking,calibration,ping pong robot | Least squares,Control theory,Computer science,Artificial intelligence,Ellipse,Artificial neural network,Monocular vision,Computer vision,Shadow,Simulation,Kalman filter,Triangulation (social science),Robot | Journal |
Volume | Issue | ISSN |
12 | 2 | 1869196X |
Citations | PageRank | References |
11 | 0.72 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuan-hui Zhang | 1 | 47 | 5.80 |
| Wei Wei | 2 | 182 | 10.48 |
| Dan Yu | 3 | 11 | 0.72 |
| Cong-wei Zhong | 4 | 31 | 2.39 |