Camera calibration error, vision latency, nonlinear dynamics, and so on present a major challenge for designing the control scheme for a visual servoing system. Although many approaches on visual servoing have been proposed, surprisingly, only a few of them have taken into account system dynamics in the control design of a visual servoing system. In addition, the depth information of feature points is essential in the image-based visual servoing architecture. As a result, to cope with the aforementioned problems, this article proposes a Kalman filter-based depth and velocity estimator and a modified image-based dynamic visual servoing architecture that takes into consideration the system dynamics in its control design. In particular, the Kalman filter is exploited to deal with the problems caused by vision latency and image noise so as to facilitate the estimation of the joint velocity of the robot using image information only. Moreover, in the modified image-based dynamic visual servoing architecture, the computed torque control scheme is used to compensate for system dynamics and the Kalman filter is used to provide accurate depth information of the feature points. Results of visual servoing experiments conducted on a two-degree of freedom planar robot verify the effectiveness of the proposed approach.
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