Research on uncalibrated adaptive visual servoing control based on dual-camera fusion

Abstract

This paper proposes an uncalibrated adaptive visual servoing (VS) control framework based on dual-camera fusion to address key technical challenges in robotic visual servoing systems, including real-time state estimation, multi-space coordination, and dynamic target tracking. By combining the complementary advantages of “eye-in-hand” and “eye-to-hand” camera configurations, an adaptive switching mechanism is designed to achieve coordinated control between image space and Cartesian space, addressing convergence problems of traditional methods during target occlusion or field-of-view loss. Key features of the framework include: an uncalibrated control method based on the image Jacobian matrix; adaptive parameter adjustment based on Kalman filtering (KF); and a dual-camera fusion switching strategy. Experiments show the method achieves a positioning accuracy of 1.197 mm and an orientation accuracy of 0.149° in a representative static positioning task; demonstrates effective performance in scenarios involving out-of-view target acquisition and occlusion recovery; and reduces tracking errors by 13%–28% while shortening convergence time by 5%–32% in dynamic tracking tasks. This framework provides a practical technical approach for visual servoing systems in complex environments, showing potential for broad industrial applications.