Improving vision-based control using efficient second-order minimization techniques

In this paper, several vision-based robot control methods are classified following an analogy with well known minimization methods. Comparing the rate of convergence between minimization algorithms helps us to understand the difference of performance of the control schemes. In particular, it is shown that standard vision-based control methods have in general low rates of convergence. Thus, the performance of vision-based control could be improved using schemes which perform like the Newton minimization algorithm that has a high convergence rate. Unfortunately, the Newton minimization method needs the computation of second derivatives that can be ill-conditioned causing convergence problems. In order to solve these problems, this paper proposes two new control schemes based on efficient second-order minimization techniques.