The use of visual feedback for closed loop control of a robot motion termed visual servoing has received a significant amount of attention during the last two decades. Most of the key research problem have been related to the performance of visual servoing methods in the presence of measurement and system modeling errors. As a result, a number of variations and hybrid visual servoing approaches have been proposed in the literature to cope with the inherit problems of image and position based visual servoing. Specificc problems such as the effect of camera calibration errors have been studied in. The convergence properties of the control part of the systems are known for most cases.

While the convergence of the system is an essential performance property, it does not reveal much about the generated robot trajectory and its uncertainty. The procedures of camera calibration have improved enormously over the last decade. However, even perfect calibration does not overcome the restriction of the image resolution and the imaging process causes an uncertainty in the control. Motivated by this fact, in our work we have proposed the use of error propagation in the analysis and comparison of different types of visual servoing methods, i.e., position-based and hybrid. Other work related to visual servoing is cited below.

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