###### Abstract  
Visual servoing technology is well developed and applied in many automated manufacturing tasks, especially in tools' pose alignment. To access a full global view of tools, most applications adopt an eye-to-hand configuration or an eye-to-hand/eye-in-hand cooperation configuration in an automated manufacturing environment. Most research papers mainly put efforts into developing control and observation [PERSON] in various scenarios, but few have discussed the importance of the camera's location in the eye-to-hand configuration[PERSON] In a manufacturing environment, the quality of camera estimations may vary significantly from one observation location to another, as the combined effects of environmental conditions result in different noise levels of a single image shot in different locations. In this paper, we propose an algorithm for the camera's moving policy so that it explores the camera workspace and searches for the optimal location where the image's noise level is minimized. Also, this algorithm ensures the camera ends up at a suboptimal (if the optimal one is unreachable) location among the locations already searched with the limited energy available for moving the camera. Unlike a simple brute-force approach, the algorithm enables the camera to explore space more efficiently by adapting the search policy by learning the environment. With the aid of an image-averaging technique, this algorithm, in the use of a solo camera, achieves observation accuracy in eye-to-hand configurations to a desirable extent without filtering out high-frequency information in the original image. An automated manufacturing application was simulated, and the results show the success of this algorithm's improvement in observation precision with limited energy. * [leftmargin=*] *  
sensors, two configurations can be employed in a cooperative scheme, which can be seen in some studies [11; 12; 13]. In recent years, many works have been published to show visual servoing paradigms being applied in industrial environments. For example, [PERSON] et al. [14] presented a position-based visual servoing approach for a multi-arm robotic cell equipped with a hybrid eye-in-hand/eye-to-hand multicamera system. [PERSON] et al. [15] discussed Abbe errors in a 2D vision system for robotic drilling. Four laser displacement sensors were used to improve the accuracy of the vision-based measurement system. [PERSON] et al. [16; 17] proposed a visual servoing method for the automative assembly of aircraft components. With the measurements from two CCD cameras and four distance sensors, the proposed method can accurately align the ball-head, which is fixed on aircraft structures in a finite time. Sensor accuracy is a key element in many studies as it is not only a requirement for some delicate applications but also affects the robustness of visual servoing techniques [18]. However, gaps exist between real-world applications and the open-space experiments proposed in the above studies. Real-world products are usually manufactured in a confined and complicated environment where complex factors may significantly degrade the quality of observed signals given from even the most precise sensors. For example, illumination changes in space [18] can be a serious issue for camera estimation in real manufacturing processes, which is not normally addressed in research experiments where illumination is usually sufficient and equally distributed. In this paper, we would like to address the importance of camera location in ETH or ETH/EIH cooperative configurations and discuss the benefits of developing algorithms that place the camera in an optimal location. To observe a single point in the workspace, there are an infinite number of poses that a fixed camera can be placed in the space. Among all possible poses of the camera that keep the same target within the field-of-view limit, the image noise at different locations may play a great role in the precision of observations. Many environmental conditions (such as illumination, temperature, etc.)