Adaptive Visual Tracking for Robotic Systems Without Visual Velocity Measurement

In this paper, we investigate the visual tracking for robotic systems without visual velocity measurement, simultaneously taking into account the uncertain depth information, the kinematic and the dynamic uncertainties. We propose a new image-space observer that exploits the image-space velocity information contained in the unknown kinematics, upon which, we design an adaptive controller without using the visual velocity signal where the estimated kinematic and depth parameters are driven by both the image-space tracking errors and observation errors. The major superiority of the proposed observer-based adaptive controller lies in its simplicity and the decomposition of the handling of the multiple uncertainties in visually servoing robotic systems, thus avoiding the overparametrization problem of the existing results. Using Lyapunov analysis, we demonstrate that the closed-loop system is stable and that the image-space tracking errors converge to zero asymptotically. Simulation results are provided to illustrate the performance of the proposed adaptive control scheme.