# A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection
## 3 Results and Discussion
Our missions were around 200 m long, speeds around 0.3 m/s were chosen so that, together with the aircraft's image capture and rotation, the total mission time was less than the maximum battery. In order to verify the impact of the \(G_{time}\) and \(G_{Photometric}\) gains on the proposed objectives, we create some scenarios to simulate the ratio with the gains, \(\frac{G_{time}}{G_{Photometric}}\), varying between [0.1 and 10]. The simulation creates 50 populations for each ratio between the gains, and the result presented is the average among the answers. Figure 14 shows the result. When the ratio favors the photometric objective, the mission time goes to values close to 150 min, requiring at least eight flights with 20 min (battery safety time). When the relationship tends towards time, the mission manages to behave in a single flight, approximately eight minutes. The main factor controlled by the relation is \(N_{Vert}\), which directly relates to vertical coverage. Missions with robust photometric criteria had \(N_{Vert}\) varying between 9 and 15, while in missions with time as the main factor, \(N_{Vert}\) varied between 3 and 4. Figure 14: Comparison between \(G_{Time}\) and \(G_{Photometric}\) and its impact in objectives of the meta heuristics: time of mission, horizontal, and vertical coverage. Figure 13: Comparison between GA and BA and its impact comparing numbers of individuals in the population with the time to find the results and objectives of the meta heuristics: time of mission, horizontal, and vertical coverage. Flights were performed with different distances to understand the impact of distance to surface on reconstruction. There were five flights for each distance; the result is the average. Table 3 shows the results for missions with and without the presence of local missions. It is noticed that, for flights without local missions, the closer to the surface, the more points were created in 3D reconstruction. However, missions with this short distance and causing security problems with GPS errors significantly increase the mission time. Thus, it was proposed to use local missions to increase density in some specific points. Vegetation on the surface was chosen as a point of interest. In the reconstruction with three meters of distance, the point density increase was around 51%, while, in the 5 m, it was 54%. The increase in time to perform the missions was 10% to 20% of the time spent. The same study was carried out for the number of waypoints, for missions with a distance of two meters from the surface of five meters high and a 60\({}^{\circ}\) camera opening. Table 4 shows the results. Note that the greater number of waypoints increases the density of points in 3D reconstruction, justifying the increase in vertical coverage being one factor of the fitness function. However, the increase of \(Num_{W}ay\) drastically increases the mission time. The number of optimizations made at the end of each horizontal transfer generates missions with much longer times without a significant density increase. The care that should be taken is to avoid minimal transfers, as \(Num_{W}ay\) is equal to 2, where the drop in density was significant. In relation to the parameter \(D_{min}\), we have the results presented in Table 5. The results were made with \(D_{S}\) = 2 m, \(N_{Vert}\) = 6 and horizontal opening of the camera of 100. The value is strongly linked to the number of points in the 3D reconstruction. Note that the greater the horizontal coverage, the greater is the density of points. Suppose the camera can capture the image without stopping the UAV. It is recommended to increase the maximum number of images possible since increasing the number of points with the capture stop increases the mission time. \begin{table}
\begin{tabular}{c c c} \hline \hline \(N_{Vert}\) & **Number of Points** & \(Coverage_{Vert}\) **[\%]** \\ \hline
6 & 133,702 & 57.4 \\ \hline
5 & 121,654 & 50.3 \\ \hline
4 & 116,096 & 40.4 \\ \hline
3 & 103,124 & 25.5 \\ \hline
2 & 51,980 & 0.7 \\ \hline \hline \end{tabular}
\end{table}