## 6 Discussion  
The proposed uncalibrated IBVS system is based on the DQN feature constraint method, enabling it to fulfill the robot's high-precision positioning tasks. In these application scenarios, the system can effectively satisfy the linearization assumption of the feature-motion velocity relationship. Therefore, the long-term stability of the system is mainly demonstrated under these large-scale operational conditions. In our tests, we evaluated the stability of the system over a certain period. The experimental results show that the system can stably converge within the specified accuracy error range, and no significant feature drift or degradation in convergence performance was observed during the convergence process. Although DQN is primarily used for discrete control, our method adapts it by defining the action space of DQN as the linear velocity vector in the x and y directions of the camera coordinate system. These velocity vectors are then converted into corresponding joint velocity commands, which are compensated and added to the joint variables' output by  
Figure 14: Robot localization and grasping of physical objects. (**a**) Manipulator’s initial position. (**b**) Manipulator’s desired position. (**c**) Feature convergence trajectory. (**d**) Feature convergence error. (**e**) End-effector trajectory. the uncalibrated IBVS controller, making it applicable to continuous motion control in a visual servoing system. In Section 5.1, we first analyze the experimental results when the initial feature state is within the camera's FOV constraint region. The tests indicate that, along the converging path of continuous motion, the proposed method demonstrates good image feature convergence trajectories and reasonable spatial motion trajectories. Furthermore, during the servo motion, we tested the feature constraint capability of DQN. The experimental results show that when the features tend to drift out of the camera's FOV, DQN plays a crucial role in feature constraint, enabling the uncalibrated IBVS to complete the convergence task within the specified error range. In our experiments, we focus on the applicability of the uncalibrated IBVS under varying lighting conditions and environments with changing object texture features, though the experimental scenarios do not include obstacles. We believe that the developed method remains stable on a desktop with a textured background and in low-light environments, primarily because changes in illumination and background texture do not significantly impact the extraction of point features. It should be noted that our research focuses on image feature constraints visible within the camera's FOV and the subsequent error-converging localization process. In the presence of obstacles, two scenarios may arise. First, during the convergence motion of the proposed system, obstacles may block the object, causing a shift or loss of image features, which can lead to task interruption. Second, if the manipulator comes into contact with an obstacle during motion, it could cause the convergence time to be indefinitely extended, prevent convergence, or stop the manipulator from advancing, potentially resulting in a task failure. To address these challenges, we plan to further investigate feature prediction and obstacle-avoidance planning to improve these aspects in our future research. Likewise, distortion in image signals and sudden changes in lighting conditions can not only lead to erroneous actions in the DQN feature constraint control but also affect the overall performance of the uncalibrated IBVS. Specifically, a sudden increase in observation noise can result in a larger estimation error of the feature-motion mapping matrix by the Kalman filter, thereby increasing the uncertainty in IBVS motion and potentially causing task convergence failure or interruption. This occurs because the visual feedback loop obtains incorrect feature information, which in turn affects the stability of the entire control system.