Image-Based Visual Servoing for Position Alignment With Orthogonal Binocular Vision

Image-based visual servoing methods have been widely used in many areas including the position alignment for assembly. However, the parallel vision system suffers from low sensitivity to motion along the optical axis due to the camera's perspective characteristics, which can lead to a larger alignment error along the optical axis of the camera and affect subsequent tasks. To address this issue, an innovative binocular Eye-to-Hand (ETH) vision system with two cameras whose optical axes are approximate orthogonal is designed. By deriving and analyzing the interaction matrix of point feature for the proposed system, its ability is demonstrated to improve sensitivity for motion along the optical axis. Furthermore, an image-based visual servoing control method with the proposed ETH vision system is developed for position alignment in a screw grasping experiment. A calibration method for the image Jacobian matrix is developed based on active motions of the robot's end-effector. Finally, the experimental results verify the effectiveness of the proposed methods. The position alignment errors in the experiments with the proposed vision system and method are less than 0.5 mm. The results indicate that the proposed method holds great potential for industrial applications in the area of small components assembly.