## 3 Study Parameters
### Area Specifications  
For the following study, a large area with several static obstacles (buildings) was considered. For logistical reasons, the large area was divided into several smaller sub-areas  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Travel Speed & 15 km/h \\ \hline Minimum Turning Radius & 5.60 m \\ \hline Scanning Range & 25.0 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle simulation parameters.  
Figure 11: The path-updating scheme when a vehicle breakdown occurs for an arbitrary path in a sub-area. The original path can be seen in (**a**) in green, and the initial starting and ending points are denoted by the yellow and red ‘x’, respectively. When the breakdown occurs, the portion of the path that was covered before the breakdown is colored red, while the remaining path is left green in (**b**). The remaining path is used as an input to the higher-level path generation, where the optimal start location will be selected at either end of the remaining path, denoted as the purple ‘x’ in (**b**).  
Figure 12: The dimensions of an autonomous vehicle used in the study.  
that the vehicles should scan. This process can be performed manually or automatically, but it was performed manually to preserve the pre-defined areas outlined by our research partners. A comparison of the satellite image and the GPS data representation of the targeted area can be seen in Figure 13.  
For this study, the buffer distance used for generating the graph was set to 2.24 m, which is 0.4 times the vehicle's turn radius. This buffer distance allows the vehicle to perform turning maneuvers near obstacles while ensuring that the vehicle does not collide with the obstacles. In this study, the buffer distance is significantly less than the scanning radius of 12.5 m (scanning range: 25 m), so this does not have any effect on the coverage. However, this parameter can vary in different case studies and should be carefully selected. Additionally, a resolution of 8 m was selected for generating the graphs. This is also equivalent to twice the length of the vehicle. To put this into perspective, there is 58,049 m\({}^{2}\) of scannable area for the case study in Goheung. Like the buffer distance, the resolution is still smaller than the scanning radius, so the overall coverage is not affected by this. This value (8 m) was selected through trial and error to yield a graph partition that could generate ideal results within a timely manner since finer resolutions increased the computation time.  
Because the lower-level path generation pre-processes several paths within each sub-area, several starting points in each sub-area were selected. The starting points were selected at positions where adjacent sub-areas join each other, as well as some of the expected vehicle starting/ending locations. The starting points in each sub-area can be seen in Figure 13c. Each sub-area has several possible starting points. The candidate start points for an area are visualized using colored dots, where the starting points in the same area share the same color. When the higher-level path generation algorithm is executed, it can select one of the colored dots for the corresponding sub-area to begin the scanning route, which is pre-processed using the lower-level path generation algorithm. Pre-processing several paths for each sub-area can minimize the total distance by selecting optimal starting points for each sub-area. The lower-level paths were also pre-processed to save computation time in real-time applications.  
Figure 13: A satellite image of the targeted area with obstacles colored yellow and numbered accordingly (**a**), the GPS boundary for each sub-area outlined in different colors (**b**), and the sub-area starting points selected for pre-processing the lower-level paths shown as the colored dots (**c**).