# A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection
## 3 Results and Discussion

Table 4: Comparison between the variation of \(Num\_WaY\) and its impact on the amount of points of 3D Reconstruction. \begin{table}
\begin{tabular}{c c c} \hline \hline \(D_{S}\) **[m]** & **Number of Points** & **Mission Time [s]** \\ \hline
1 & 133,702 & 508 \\ \hline
3 & 62,438 & 238 \\ \hline
5 & 59,465 & 179 \\ \hline
3 & 94,323 & 270 \\ \hline
5 & 91,696 & 213 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between the variation of \(D_{S}\) and its impact on the amount of points of 3D Reconstruction and the Mission Time. With the results presented in Tables 3-5, it is possible to note that, for the production of an adequate mission, the UAV must make more horizontal transfers, as close as possible to the surface. Besides, points of image capture close. However, this photometric objective to 3D reconstruction increases the mission time, as the UAV will not cover the entire target surface in one mission. In this way, metaheuristic algorithms balance these objectives, enabling a 3D reconstruction with quality and adapting the whole surface's mission time. Another detail is the possibility of meeting the distance objective to the surface in some critical locations of the task, increasing the quality of 3D reconstruction in POI to the operator. In this way, the mission will have a greater distance on the surface in general and, in small parts, will have an approach to the surface. Table 6 shows the results of the mission in the Gazebo-ROS simulator. This mission consisted of three horizontal missions, the first planned in Figure 15. After each transfer, the next optimizations were performed, with the results of stages 2 and 3. It is noticed that the distance to the Slope remained with small variation, and the same occurs with the distance among the horizontal waypoints. The first stage chose a mission with three vertical waypoints between 1 and 5 m (i.e., slope height). The second was 2 points between 1 and 3.5 m. Finally, the third was a mission at an altitude of 1.5 m. The total mission time was 13 min, less than the 15 min planned for the task. The chosen speed was 0.3 m/s. The aircraft traveled the entire course of the structure in less time than was available for it. The images were taken with the UAV hovering for two seconds to capture images to avoid deformations in the images. Initially, the flight was performed with 3D reconstruction in a simulated environment to improve the mission's safety, avoiding finding results that did not meet the maximum flight time. After the simulation, the mission was relocated to the real environment. Another feature that had to be implemented is the conversion of distances to meters, as seen in Figure 15 for GPS, showed in Figure 16. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stage** & \(D_{min}\) **[m]** & \(D_{S}\) **[m]** & \(N_{Vert}\) & **Mission Time** & **Coverage Hor** & **Coverage Vert** \\  & & & **[Min]** & **[\%]** & **[\%]** \\ \hline
1 & 1.04 & 1.71 & 3 & 10.59 & 82.38 & 47.82 \\ \hline
2 & 0.89 & 1.96 & 2 & 7.40 & 86.80 & 49.97 \\ \hline
3 & 1.37 & 2.05 & 1 & 4.60 & 80.66 & 46.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Optimization in Gazebo-ROS. \begin{table}
\begin{tabular}{c c c} \hline \hline \(D_{min}\) & **Number of Points** & \(Coverage_{Hor}\) **[\%]** \\ \hline
0.6 & 112,257 & 85 \\ \hline
1.2 & 62,147 & 70 \\ \hline
1.71 & 48,927 & 57.1 \\ \hline
2 & 43,781 & 50 \\ \hline
2.4 & 37,080 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the variation of \(D_{min}\) and its impact on the amount of points of 3D Reconstruction. Figure 16 presents a mission test performed in a real Slope. The drone used was a DJI Phantom 4 (Nanshan, Shenzhen, China). Figure 17 shows the 3D reconstruction using 80 images. It is possible to conclude that the objective of an optimized autonomous mission for 3D reconstructions has been achieved successfully.