## 1 Introduction
In [27], the MPC method was proposed to optimize the feature trajectory in the image frame, addressing the field-of-view (FOV) constraint issue. In recent years, the rapid development of artificial intelligence has led to the widespread application of machine learning in the field of robotics. In [28], reinforcement learning (RL) is utilized to avoid feature loss in visual servoing tasks. In [29], to solve the feature visibility issue in the camera's FOV for wheeled mobile robots (WMRs), a controller based on Q-learning is proposed to constrain visual features within the safe region of the image plane. To overcome the complexity of constructing a discretized Q-table in continuous environments, ref. [30] proposes the use of DQN to address this issue. However, these methods are only applicable to specific scenarios and have limited generalization capabilities. Compared to directly using RL as the output of the control system, [31; 32] adopt Q-learning methods to adaptively adjust the IBVS servo gains and further propose a fuzzy control approach to adjust the RL learning rate, which not only ensures feature FOV visibility but also improves the servo convergence efficiency. In [33], a continuous action space design was adopted, utilizing the Deterministic Policy Gradient (DDPG) algorithm to adjust the gain of the IBVS controller. However, these parameter-based estimation methods do not take into account the specific feature FOV constraint issue. Regarding the feature FOV constraint problem in IBVS, particularly in unknown noisy environments, the uncalibrated 6-DOF manipulator still faces challenges in handling multidimensional visual feature tasks, in order to ensure real-time performance and convergence efficiency. Therefore, to address the above issues, this paper proposes a DQN-based uncalibrated IBVS method. The main contributions of this paper are as follows:1. A novel uncalibrated IBVS framework is proposed to address the feature constraint problem in the manipulator's visual servoing task under unknown noise environments. The framework effectively mitigates the motion randomness caused by errors in the estimation of the feature-motion mapping matrix. 2. Base on DQN, the offline FOV feature mapping mechanism further is designed. Additionally, a camera FOV-based reward and punishment mechanism is established to train the visual feature agent to perform the uncalibrated visual servoing task with FOV constraints. 3. The new DQN-based uncalibrated visual servoing scheme achieves the auxiliary positioning task for 6-DOF manipulators, directly utilizing the feature states from 2D images to enforce visual feature constraints. This ensures the operational flexibility and stability of the uncalibrated visual servoing task in unknown noisy environments, making it more suitable for industrial robot applications. Further, a comparison of our work with existing relevant work is provided in Table 1.  
The structure of this paper is as follows: Section 2 discusses the nonlinear model of the IBVS manipulators. Section 3 presents the feature-constrained method based on DQN, further constructing and training the feature-constrained Q-network based on offline camera FOV environment feature mapping. Section 4 presents the DQN feature-constrained visual servoing framework. Section 5 demonstrates the proposed method through experiments