TY - GEN
T1 - Using neural network technique in vision-based robot curve tracking
AU - Qingjie, Zhao
AU - Fasheng, Wang
AU - Zengqi, Sun
PY - 2006
Y1 - 2006
N2 - Robot curve tracking is needed in some industrial applications, such as automatic welding or incising. Such a robot system is usually equipped with visual sensors, which always require calibration before used. The calibration process is often complicated. In this paper a neural network is used to learn the relationship between the world coordinate information and the image information, instead of computing accurate camera parameters. The neural network is first trained based on sample data by using the 2D and 3D coordinates of some control points on a standard pattern. During the tracking stage, images captured by cameras are firstly changed into binary images. The curve is then thinned and its position on the image is recorded. From the image data, the curve's position in the world coordinate frame can be specified by using the trained neural network. The curve tracked can be arbitrary, open or closed. The experimental results illuminate that the neural network technique is satisfying and it is successfully used in the vision-based robot curve tracking.
AB - Robot curve tracking is needed in some industrial applications, such as automatic welding or incising. Such a robot system is usually equipped with visual sensors, which always require calibration before used. The calibration process is often complicated. In this paper a neural network is used to learn the relationship between the world coordinate information and the image information, instead of computing accurate camera parameters. The neural network is first trained based on sample data by using the 2D and 3D coordinates of some control points on a standard pattern. During the tracking stage, images captured by cameras are firstly changed into binary images. The curve is then thinned and its position on the image is recorded. From the image data, the curve's position in the world coordinate frame can be specified by using the trained neural network. The curve tracked can be arbitrary, open or closed. The experimental results illuminate that the neural network technique is satisfying and it is successfully used in the vision-based robot curve tracking.
KW - Curve tracking
KW - Neural network
KW - Robot vision
UR - http://www.scopus.com/inward/record.url?scp=34250688623&partnerID=8YFLogxK
U2 - 10.1109/IROS.2006.281787
DO - 10.1109/IROS.2006.281787
M3 - Conference contribution
AN - SCOPUS:34250688623
SN - 142440259X
SN - 9781424402595
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 3817
EP - 3822
BT - 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006
T2 - 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006
Y2 - 9 October 2006 through 15 October 2006
ER -