Dynamic visual servoing of a small scale autonomous helicopter in uncalibrated environments

This paper presents a novel adaptive controller for image-based visual servoing of a small autonomous helicopter to cope with uncalibrated camera parameters and unknown 3D geometry of the feature points. The controller is based on the back-stepping technique, but its design has two new features. First, it incorporates the visual feedback into the last step of the backstepping procedure, while existing backsteppingbased methods employ the visual feedback at the early steps. Second, the controller maps the image errors onto the actuator space via a depth-independent interaction matrix to avoid estimation the depths of the feature points. The new design method makes it possible to linearly parameterize the closed-loop dynamics by the unknown camera parameters and coordinates of the feature points in the 3D space so that an adaptive algorithm can be developed to estimate the unknown parameters and coordinates on-line. Two potential functions are introduced in the controller to guarantee convergence of the image errors and to avoid trivial solutions of the estimated parameters. The Lyapunov method is used to prove the asymptotic stability of the proposed controller based on the nonlinear dynamics of the helicopter. Simulations have been also conducted to demonstrate the performance of the proposed method.