Defocus Force-Guided Precision Autofocus of Hand–Eye Robot System

Abstract

Autofocus is a crucial prerequisite for precision vision sensing of industrial workpieces. Existing methods mainly utilize image sharpness to guide single-axis camera adjustment for focusing. However, they fail to deal with larger workpieces with uneven surfaces, which require multidirection sensing. To solve this challenge, the focusing is abstracted as a force movement acting on the camera focus plane, and a defocus force-guided multi-DOF (degree of freedom) autofocus method with a hand–eye robot system is proposed. For better force guidance, the image is divided into blocks and a defocus degree from a defocus evaluation network is associated with each block. By taking advantage of the dilated convolution with an attention, the defocus evaluation network achieves high precision while satisfying lightweight characteristics. For each image block, its corresponding defocus force is constructed with the amplitude of its defocus degree. All block defocus forces are then synthesized to the resultant force and torque acting on the center of the focus plane, which are employed to drive the manipulator to adjust the position and posture of the camera, respectively. This promotes the high-precision multi-DOF autofocus on uneven surfaces. Experimental results on industrial workpieces demonstrate the effectiveness of the proposed method.