On the basis of the kinematic model of a unicycle mobile robot in polar coordinates, an adaptive visual servoing strategy is proposed to regulate the mobile robot to its desired pose. By regarding the unknown depth as model uncertainty, the system error vector can be chosen as measurable signals that are reconstructed by a motion estimation technique. Then, an adaptive controller is carefully designed along with a parameter updating mechanism to compensate for the unknown depth information online. On the basis of Lyapunov techniques and LaSalle's invariance principle, rigorous stability analysis is conducted. Because the control law is elegantly designed on the basis of the polar-coordinate-based representation of error dynamics, the consequent maneuver behavior is natural, and the resulting path is short. Experimental results are provided to verify the performance of the proposed approach.The vision-based robotic system is composed of a mobile robot and an onboard camera fixed on a pan motion platform. As shown in Figure 1, the robot frame F R is attached to the center of the wheel axis, which is defined as the Y R axis, while the Z R axis is perpendicular to the motion plane of the mobile robot, and the X R axis can be then obtained easily for a standard right-handed coordinate system. The camera frame F C is defined such that the camera center O C lies in the Z R axis and the frames F R and F C are parallel to each other when the rotation angle of the pan camera is zero. The From the previous analysis, it is clear that the largest invariant set M only consists of the equilibrium point in the sense that‡ It should be noted that although P O c 0 .t/ is, as can be seen from (15), merely piecewise smooth because of the usage of the projection function, it is however continuous with respect to initial conditions. Then, on the basis of Lemma 1 in [32], we can conclude that the positive limit L C of P O c 0 .t/ is invariant, which thus makes LaSalle's invariance principle applicable to the closed-loop error system of (20).