# EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs
## 4 Evaluation Metrics and Results
### Results
Figure 4.1 Subdivision for the grid Box \(G\) for the fourth obstacle. Figure 4.2 (**a**) total trajectory paths. Total path generated by our 2D basic obstacle avoidance algorithm; (**b**) total path generated in our 3D obstacle avoidance algorithm. Figure 4.3 Total trajectory path at the end of the mission. In order to understand the behavior of the work, we varied the speed and the rotation rate. The UAV speed varies between 4 and 14 m/s, and the UAV rotation rate varies between 10 and 60 degree/s. Figure 19a shows the gain in completion time while increasing both the speed and rotation rate. Figure 19b shows the gain in completion time in our 3D work vs. a 2D path while increasing the speed and decreasing the rotation rate. Results show that the mission completion time is reduced by an average of 14.46% in 3D path, with energy saving of 10.37% compared to the results obtained in the basic approach. It should be noted that the variation between the speed and the rotation rate influences on the completion time but not on the energy consumption. The gain in energy remains 10.37% due to the fact that \(\lambda\) and \(\gamma\) are the average total energy consumption with standard deviation for the distance, and turn, respectively [35]. * Scenario 2: In this scenario, we adopt the area used in [31] (see Figure 20). The area is 202 m \(\times\) 172.2 m. It is divided into a grid of 8 rows \(\times\) 10 columns (top view grid). It shows the area of interest with the presence of obstacles. Figure 21a shows a Google Maps 3D view of the cell containing the obstacles. It has two trees of height max 4 m. Figure 21b shows the Google Maps street view image for the facing area. We compared our work to [31], in which the drone flies at height of 10 m. We assume that the UAV speed varies between 4 and 14 m/s and UAV rotation rate varies between 10 and 60 degree/s. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **(a) Basic Approach** & **(b) Our Work (EAOA)** \\ \hline Route length \(\top\) in meters & 507.003 & 486.514 \\ \hline UAV speed \(v\) in m/s & 8 & 8 \\ \hline Turns in degree & 1476 & 1107 \\ \hline Rotation rate \(\omega\) in degree/s & 30 & 30 \\ \hline Completion-time \(\tau\) in sec & 112.575 & 97.714 \\ \hline Energy consumption \(E_{total}\) in KJ & 84.551 & 75.782 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Our basic approach vs. EAOA. Figure 19: Gain in completion time in EAOA compared to the basic approach. (**a**) gain in completion time while increasing both the speed and rotation rate; (**b**) gain in completion time while increasing the speed and decreasing the rotation rate. The green line in Figure 22a represents the path generated by [31]. The red path in Figure 22b is generated in our work. Table 2 shows that our path is 3.89% shorter than the path in [31]. It also shows a reduction of 37.77% in the total turn angles. This means better energy saving. Results show that the mission completion time is reduced by an average of 15.22% in our work, with energy saving of 11.30% compared to the results obtained in [31]. Figure 21: The Facing area in cell (4,4). (**a**) Google Maps 3D image for the cell containing obstacle; (**b**) Google Maps street view image for the facing area. Figure 20: Scenario 2: Area of Interest [31]. It should be noted that the area is not totally covered by the work in [31]. The cell that contains obstacle (example cell (4,4)) is discarded. However, if the obstacle belongs to a small part of the cell, it is better not to discard the whole cell if there is a possibility to avoid it while maintaining a higher coverage. In our work, by tackling the frontal view, we can have a better understanding of the obstacle location which ensures a better coverage of the area. Authors in [31] declare that the cost function presented in the original approach in [10] has not been able to achieve an optimum energy solution. Therefore, they revised the cost-effectiveness and updated the cost-function.