###### Abstract
affect the noise level of a single image [19]. In a manufacturing environment, the quality of estimations from a camera may vary significantly from one observation location to another. For instance, the precision of an estimation improves greatly if the camera moves from a location where it is placed within the shade of machines to a location that has better illumination. Also, places near machines may be surrounded by strong electrical signals that introduce extra noise into the camera sensors. Many papers in visual servoing consider applying filters to reduce image noise and, therefore, increase the precision of observations [20; 21; 22; 23]. Gaussian white noise can be dealt with using spatial filters, e.g., the Gaussian filter, the Mean filter, and the Wiener filter [24]. In addition, wavelet methods [25; 26] have the benefit of keeping more useful details but at the expense of computational complexity. However, filters that operate in the wavelet domain still inevitably filter out (or blur) some important high-frequency useful information of the original image, even though they succeed in preserving more edges compared with spatial filters. Especially at locations where the signal-to-noise ratio (SNR) in an image is low, no matter what filters are applied, it is difficult to safeguard the edges of the noise-free image as well as reduce the noise to a desirable level. Thus, developing an algorithm that searches for locations of the camera where the SNR is high is beneficial. We can approach the denoising problem with multiple noisy images taken from the same perspective. This method is called signal averaging (or image averaging in the application of image processing) [27]. Assume images taken from the same perspective but at different times have the same noise levels. Then, averaging multiple images at the same perspective will reduce the unwanted noise as well as retain all the original image details. We can also assume the robot's end-effector holds the camera rigidly so that any shaking and drift are negligible when shooting pictures. Moreover, the number of images required for averaging has a quadratic dependence on the ratio of an image's original noise level to the reduced level that is desirable for observations. Therefore, the number of images required is a measurement of the noise level in a single image. Furthermore, in the denoising process, precise estimations require that the original image details are retained as much as possible. Based on previous statements, we decided to choose image averaging over all other denoising techniques in this work. Frame-based cameras, which detect, track, and match visual features by processing images at consecutive frames, cause a fundamental problem of delays in image processing and the consequent robot action [28]. By averaging more images for an observation, fewer noises are maintained in the averaged image, but more processing time is required for acquiring visual features; it is a typical tradeoff between high-rate and high-resolution in cameras [18]. The control loop frequency is limited in many cases due to the low rate of imaging, which undermines the capability/usage of visual servoing techniques in high-speed operations. Therefore, it is rewarding for an eye-in-hand configuration to find the location of the camera where the original image noise is lowest; thus, the least number of images is needed in order to facilitate the manufacturing speed. Brute-force search [29] is a simple and straightforward approach to looking for the optimal location of the camera. It systematically searches all possible locations in space and gives the best solution. This method is only feasible when the size of the search space is small enough. Otherwise, brute force, which checks every possibility, is usually inefficient, especially for large datasets or complex problems, where the number of possibilities can grow exponentially. An intelligent algorithm is necessarily developed for the sake of energy efficiency and time consumption.