## 5 Simulation Results and Discussion
Figure 5: Different parts of fitness value in each waypoint, (**a**) constraints value, (**b**) objective value, and (**c**) constraints value and objective value. Figure 6: Impact of the weighted parameter value, (**a**) as SAI parameter weight value increases, SAI value increases proportionally; (**b**) selection of the optimal weight value for SAI. level-2 priority, and the center having least priority as level-1. We gave the cell edge area more importance by considering that, generally when we design anything, we give more importance to the center as our valuable infrastructure is situated in the center. In that sense, edge areas received less importance, which may pose a great threat. Whenever any threats begin, they commence from the outside region first not from the inside area. Moreover, if we can prevent it earlier, before entering the sensitive area, we will be able to lessen the threat much. That is the reason why we gave cell edge more importance. We observed that the trajectories, generated for each flight time, avoid all the restricted areas and reach the destination safely after full coverage of the surveillance area. Our operational area is based on a 3-D environment, while covering the distance from one waypoint to another waypoint, UAV made changes in the \(X\), \(Y\), and \(Z\)-axis. Altitude changes for all the UAVs during flight time are shown in Table 5 and the corresponding flight times for the UAVs can be seen in Table 6. Figure 7b also shows that the ability of UAVs to decide to change in altitude and turning angle when hills or unstructured environment appear in path planning shows that our system has dynamic environment adaptability. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No. of Waypoints**} & \multicolumn{4}{c}{**Altitude (Meter)**} \\ \cline{2-5}  & **UAV1** & **UAV2** & **UAV3** & **UAV4** \\ \hline
1 & 0.4827 & 0.4792 & 0.3403 & 0.5184 \\ \hline
2 & 1.3508 & 0.3393 & 1.3211 & 0.8365 \\ \hline
3 & 1.1563 & 0.9815 & 1.2727 & 0.4283 \\ \hline
4 & 1.3724 & 0.2747 & 1.1685 & 0.9781 \\ \hline
5 & 0.8549 & 0.8525 & 1.3761 & 0.0475 \\ \hline
6 & 0.8316 & 0.1218 & 0.8848 & 0.1951 \\ \hline
7 & 0.8528 & 0.0602 & 1.0310 & 0.5889 \\ \hline
8 & 0.5366 & 0.5191 & 0.4815 & 0.5165 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Altitude changes of the UAVs. Figure 7: Distributed Path Planning for Multi-UAVs (**a**) 2-D environment, (**b**) 3-D environment. The proposed trajectory planner also ensures that the multiple UAVs do not collide with each other while surveilling. The respective Fitness values of all four UAVs for full coverage are shown in Figure 8.  
In our simulation, the operating area was assumed to be 1 km \(\times\) 1 km, with a per cell value of 50 m. During the surveillance, UAVs took off from the starting point and returned to the ending point. Table 6 shows the total distances covered by the UAVs and the necessary flight time for both 2-D and 3-D cases and it is observed that in a 3-D environment, UAVs cover more distance than in a 2-D environment. In our implementation, we assume that a UAV can cover up to four cells from a single location. As a result of this assumption, we can see that all of the UAVs have covered the entire operational region, except the restricted area, which is not allowed to be surveilled. Distributed Path Planning for Multi-UAVs in the 2-D and 3-D environment for the larger-sized area is shown in Figure 9. To consider a larger size for our simulation we have selected a 30 \(\times\) 30 grid size having the area of 3 km \(\times\) 3 km (per cell 100 m). In our simulation where grid size was 20 \(\times\) 20, each of the four UAV needs 8 waypoints to