Underactuated Dynamic Visual Servoing of Aerial Mobile Robots Using Adaptive Calibration of Camera

The dynamic visual servoing problem studied in this paper differs from existing approaches in two key aspects: the dynamics of the aerial mobile robot are underactuated, and the onboard camera is adaptively calibrated. To address the first challenge, a novel cascade visual servoing framework is developed, consisting of three control loops: the image loop, the attitude loop, and the angular velocity loop. Based on this framework, an extended eye‐in‐hand vision system is constructed, in which the perspective projection of feature points onto the image plane is decoupled from the rigid body’s attitude. This design allows the proposed visual controller to effectively compensate for image dynamics. Furthermore, unknown intrinsic and extrinsic camera parameters make compensation for image dynamics more difficult. To overcome this issue, a depth‐independent composite matrix is introduced, enabling the unknown visual dynamics to be linearly parameterized and integrated with an adaptive control technique. A novel online algorithm is developed to estimate the unknown camera parameters in real time, and an additional adaptation mechanism is incorporated to estimate the rotational inertia of the rigid body. Using Lyapunov theory and Barbalat’s lemma, it is proven that the image tracking error asymptotically converges to zero while all physical variables remain locally bounded. Experimental results confirm that the image tracking error converges to zero over time, with a maximum deviation of no more than two pixels, thereby validating the effectiveness of the proposed visual controller.