## 5 Automotive Case Study  
The technologies discussed in this paper have been validated in an automotive vehicle's front-axle assembly. Assemblies of this kind are manual in industry, with the human moving to different workstations and carrying objects that are heavy, causing a strain in their health and their performance. We propose the cooperation of the human operator with a mobile robot, which will allow the human to undertake easier tasks and let the robot handle the tiring tasks of heavy-object transportation. As such, in this assembly use case, a mobile robot (MRP), an AGV and a human collaborated in the completion of several tasks, such as damper preassembly, screw fastening, transportation of parts, etc. The use case aimed to present a reconfigurable fenceless shopfloor. This system used a multitude of sensors. It had 4 cameras mounted, the ROBOCEP-TION rc_160 in the right arm and the rc_65 in the left arm [56]. On top of the torso of the robot, there was a RealSense D435 [57], while on the side of the robot, there was a Basler camera [58], as shown in Figure 14. Aside from the cameras, the MRP also had two SICK laser scanners [59], one in the front-left part and one in the back-right part. Figure 14: Onboard sensors. Figure 13: Rviz frame representation. The use case began with the MRP navigating to the preassembly workstation (PT), and then docking to it using the RealSense camera and an AprilTag marker using the localization process explained in Section 3. Then, the MRP had to pick up a preassembled damper using the rc_160 camera and the detection process explained in Section 4. Then the robot had to navigate to the compression machine (CM) and dock to it, and place the damper to the corresponding fixture by performing a detection of the fixture with the rc_65. The MRP's next action was to manipulate an alignment rod and a nut, using the detection process to detect the nut, as well as the upper structure from which the alignment rod was picked up and on which the nut was placed. Next, the MRP had to pick up the compressed damper and navigate to the AGV station and dock there, in order to place the compressed damper. Then, the AGV and MRP conavigated, with the MRP keeping a steady distance from the AGV using mobile-docking functionality. While moving, the MRP had to perform a screw-fastening action on the moving workstation of the AGV. During this process, the human operator also worked in the cell, moving from station to station. The robot took their presence into account with its shopfloor perception, using 2D and 3D data from the laser scanners. The tasks necessary for the assembly are also shown in Table 1.  
The detection of several parts, necessary for the completion of the described use case, is shown in Figure 15.  
It is easily surmised that the technologies regarding shopfloor and process perception presented in this work were of vital importance for the implementation of the use case. The localization process was necessary to compensate for the navigation error, in order for the robot to be in a position from which it was possible to execute the detection and manipulation actions. The detection was necessary, since the objects to be manipulated would not always be in the exact same position, so predesignated movements would not work for the manipulation. The use of this technology enhanced the reconfigurability of the assembly line. \begin{table}
\begin{tabular}{c c c c} \hline
**Tasks** & **Working Area** & **MRP** & **Human** \\ \hline Preassembly of right damper & Pre-assembly & & X \\ Pick and place of preassembled damper & From pre-ass. to compress. & X & \\ Align damper in compression machine & Compression & & X \\ Remove alignment rod & Compression & X & \\ Pick and place nut & Compression & X & \\ Pick and place compressed damper & From compression to AGV & X & \\ Pick and insert cables/screws on the disk & Compression & & X \\ Screw damper on the disk & Compression & X & \\ \hline \end{tabular}
\end{table} 
Table 1: High-level description of the use case.