## 6 Results  
The methods for shopfloor and process perception introduced in this paper produced the following results. The navigation, although successfully completing repeatedly, had a maximum accuracy of 5-10 cm, which was too much for object manipulation at the workstations, as expected. The localization was executed repeatedly and successfully by the robot, decreasing the error to 1 cm or less. The average time of the localization was 8 s.  
The detection method presented enabled the successful manipulation of objects after navigation of the robot without damaging the parts every time the execution was repeated. In addition, the time that it took for the detection pipeline to be executed was below 1 s. Including the movements of the arms, the time for the completion of several pick-and-place operations in the case study are shown in Table 3, in comparison to the time it would take to manually teach each process. From the results, it is easy to deduce that the automatic method that used detection was much more time-efficient. Let it be noted, however, that the manual teaching time was measured for comparison only in the case of the mobile robot. In a mobile robot, manual teaching methods are not useful, since every time, due to the small deviations in object and platform locations, it would be dangerous to the objects. Another point to consider when using multiple sensors is the impact of the streamed data to the performance of the network, and by extension to the GigE cameras. The topology of the network concerning the sensors is shown in Figure 16. The RealSense camera was not included, as it was a USB camera. \begin{table}
\begin{tabular}{c c c} \hline
**Case study Processes** & **Physical View** & **Digital Twin View** \\ \hline Damper placing & & \\ \hline Screwing location localization and & & \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline
**Process** & **Time for Completion with** & **Time for Completion with** \\  & **Detection** & **Manual Teaching** \\ \hline damper grasping & 18 s & 1:52 min \\ damper placing & 28 s & 2:10 min \\ rod grasping & 26 s & 1:35 min \\ nut grasping & 17 s & 1:37 min \\ nut placing & 22 s & 1:30 min \\ \hline \end{tabular}
\end{table} 
Table 3: Time of completion for pick-and-place processes with and without detection. Because the cameras had to be connected to the same router, there were issues when all of them were enabled. In this work, several configurations were tested, in which any combination of cameras was enabled at the same time. The configurations are shown in Table 4, where the FPS of the cameras indicated in the first column are shown when the camera indicated in the first row was enabled at the same time. The results are also presented in Figure 17.  
When analyzing these results, it was evident that the Basler camera was impacted greatly when the RC cameras were enabled, but not when the RealSense camera was enabled. That seems natural, since the RC cameras and Basler were all GigE cameras, while RealSense was connected via USB. As such, the RealSense camera did not affect the network at all. To overcome the above issue, a programming node was created to enable and disable the cameras in order to only have one GigE camera enabled each time a task required it. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Cameras**} & **Enabled** & **RC\_160** & **RC\_65** & **RealSense** & **All Enabled** \\  & **Alone** & **Enabled** & **Enabled** & **Enabled** & **Enabled** \\ \hline rc\_160 fps & 25 & 25 & 25 & 25 & 25 \\ rc\_65 fps & 25 & 25 & 25 & 25 & 25 \\ Basler fps & 18 & 0 & 0 & 18 & 0 \\ RealSense fps & 30 & 30 & 30 & 30 & 30 \\ \hline \end{tabular}
\end{table} 
Table 4: Frames per second (FPS) of active cameras. Figure 16: Network topology regarding sensors. Figure 17: Frames per second (FPS) of active cameras.