  This paper proposes a new approach to achieve direct visual servoing (DVS)
based on discrete orthogonal moments (DOMs). DVS is performed in such a way
that the extraction of geometric primitives, matching, and tracking steps in
the conventional feature-based visual servoing pipeline can be bypassed.
Although DVS enables highly precise positioning, it suffers from a limited
convergence domain and poor robustness due to the extreme nonlinearity of the
cost function to be minimized and the presence of redundant data between visual
features. To tackle these issues, we propose a generic and augmented framework
that considers DOMs as visual features. By using the Tchebichef, Krawtchouk,
and Hahn moments as examples, we not only present the strategies for adaptively
tuning the parameters and order of the visual features but also exhibit an
analytical formulation of the associated interaction matrix. Simulations
demonstrate the robustness and accuracy of our approach, as well as its
advantages over the state-of-the-art. Real-world experiments have also been
performed to validate the effectiveness of our approach.
