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.

相机校准误差，视觉潜伏期，非线性动力学等对设计视觉伺服系统的控制方案提出了重大挑战。尽管已经提出了关于视觉伺服的许多方法，但是令人惊讶的是，在视觉伺服系统的控制设计中，只有少数几种方法考虑了系统动力学。另外，特征点的深度信息在基于图像的视觉伺服架构中至关重要。因此，为解决上述问题，本文提出了一种基于卡尔曼滤波器的深度和速度估计器，以及一种改进的基于图像的动态视觉伺服架构，该架构在控制设计中考虑了系统动力学。特别是，卡尔曼滤波器被用来处理视觉潜伏期和图像噪声引起的问题，以便于仅使用图像信息来估计机器人的关节速度。此外，在改进的基于图像的动态视觉伺服架构中，计算出的转矩控制方案用于补偿系统动力学，而卡尔曼滤波器用于提供特征点的准确深度信息。在两自由度平面机器人上进行的视觉伺服实验结果验证了该方法的有效性。计算出的转矩控制方案用于补偿系统动力学，而卡尔曼滤波器用于提供特征点的准确深度信息。在两自由度平面机器人上进行的视觉伺服实验结果验证了该方法的有效性。计算出的转矩控制方案用于补偿系统动力学，而卡尔曼滤波器用于提供特征点的准确深度信息。在两自由度平面机器人上进行的视觉伺服实验结果验证了该方法的有效性。