## 7 Conclusions  
In this article, we developed an algorithm so that a camera can explore the workspace in a manufacturing environment and search for a location so that the image noise is minimized within the space that it can reach. This article also provides a detailed development of the camera's operational space for a specific application. The results in a virtual environment show the algorithm succeeds in bringing the camera to the optimal (or suboptimal if the optimal one is unreachable) position in this specific scenario. By reducing the processing time of images, this algorithm can be used for various visual servoing applications in high-speed manufacturing. However, challenges arise in some possible scenarios of real applications. For instance, if environmental factors vary too rapidly across the space, a fine gridding of the space is required to manifest those variations in the estimation function, which increases computational complexity, resulting in poor real-time performances. The same issues exist in cases where the operational space of the camera is too large. Other limitations include that this algorithm is incapable of differentiating contrast in images at different locations. Low contrast other than image noises occurs as another issue in the visual servoing environment; for instance, low contrast in soft tissues has been shown to have negative effects on the performance of visual servoing controllers in medical applications [38]. In addition, this algorithm does not account for cases when the object is partially obstructed, which is very common in manufacturing environment. Limitations in the camera hardware that cannot account for additive noise are also not addressed in this paper. Those effects cannot be eliminated by any image denoising processes. For example, \begin{table}
\begin{tabular}{c c c c} \hline \hline \(K_{est}\) & Iteration Numbers & \(Avg\_Dis_{new}\) (m) & Stop at Global Minimum \\ \hline
90 & 93 & 0.059 & NO \\ \hline
70 & 83 & 0.066 & NO \\ \hline
50 & 67 & 0.073 & YES \\ \hline
30 & 46 & 0.085 & YES \\ \hline
10 & 38 & 0.097 & YES \\ \hline
5 & 47 & 0.102 & YES \\ \hline
1 & 20 & 0.131 & NO \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sensitivity analysis with varying \(K_{est}\). (\(K_{sd}=50\) (m\({}^{-1}\))). \begin{table}
\begin{tabular}{c c c c} \hline \hline \(K_{sd}\) (m\({}^{-1}\)) & Iteration Numbers & \(Avg\_Dis_{new}\) (m) & Stop at Global Minimum \\ \hline
90 & 36 & 0.086 & NO \\ \hline
70 & 62 & 0.079 & YES \\ \hline
50 & 67 & 0.073 & YES \\ \hline
30 & 84 & 0.066 & YES \\ \hline
10 & 127 & 0.060 & YES \\ \hline
5 & 176 & 0.046 & YES \\ \hline
1 & 264 & 0.037 & NO \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensitivity Analysis with Varying \(K_{sd}\). (\(K_{est}=50\)). sensors with a lower dynamic range may produce images where the details are lost in extreme lighting conditions (bright sunlight or deep shadows). Another example is that some lenses may cause vignetting (darkening around the edges) or distortion, which can degrade the perceived image quality. The simulation results show how the energy threshold \(E_{T}\) affects the behavior of the algorithm. A moderate threshold is suggested so that the algorithm can search for enough amount of area but enables the camera to be driven back to the minimum with the limited energy available. The analysis of \(E_{T}\)'s value in different applications is a focus of our future work. This may also include adaptive algorithms that update \(K_{est}\) and \(K_{sd}\) online and an adaptive function for energy estimation. Also, orientations of the camera are fixed in this paper. In the future, we would like to develop advanced algorithms that provide not only the optimal of positions but also the orientations inside the space. In summary, this paper initially explores an adaptive algorithm to search for the optimal location in space with respective to image noises for observation in ETH or ETH/EIH cooperative configurations.