An uncalibrated visual servoing method using multiple cameras for large targets

Abstract

When facing large-size targets, for the problem that the target imaging field of monocular camera is small and the target imaging is not clear at a long distance, this paper proposes an uncalibrated visual servoing method using multiple cameras for large targets. On the basis of image-based visual servo control, an uncalibrated visual servo model of an arrayed multiple cameras is constructed, and the nonlinear mapping relationship between image features of multi-view fusion and robot end six-degree-of-freedom motion velocity is constructed by using image moments. Visual servoing operation for large-size targets by predicting robot end-motion velocity using a neural network based on genetic particle swarm optimization without calibrating the camera parameters and hand-eye relationship. The experimental results show that the proposed method can effectively realize multi-view fusion visual servoing operation for large-size targets with high operation accuracy and robustness.