Using neural network technique in vision-based robot curve tracking

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.