Papers Published
Abstract:
The problem of camera calibration from the perspective of hand-eye integration (henceforth referred to as the Camera-Robot (CR) problem), is addressed in the paper. Mapping results obtained from a least-squares fit using a pseudo-inverse technique and a three layer neural network are compared. The calibration matrix is developed to map the image coordinates of an IRI D256 vision processor equipped with a CCD camera directly on to the coordinates for an IBM 7540-SCARA manipulator. One transformation is obtained by performing a least-squares fit using pseudo-inverse technique on a set of one hundred data points which relates two-dimensional image coordinates to corresponding two-dimensional robot coordinates. The CR problem is also approached by using the same data points on a neural network. The results not only demonstrate the ability of neural networks to `learn' the transformation to a reasonable accuracy, but also form the basis for a relatively simple method of adaptive self-calibration of robot-vision systems. In a broader sense, the proposed method can be used to calibrate a variety of robotic sensors that are typically used in a flexible manufacturing environment
Keywords:
cameras;computer vision;flexible manufacturing systems;industrial robots;least squares approximations;neural nets;self-adjusting systems;
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