# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 5 Evaluation
### Performance Comparison of MUCS-GD Algorithm and MUCS-BSAE Algorithm  
First, we set up square working areas with side lengths of \(SL=800m=16D\). We set the camera surveillance radius of the UAV as \(R\), which is \(25m\). Due to the different speed limits, the UAVs receive in different countries, we set the maximum speed of the UAVs to \(V\), which is \(20\) m/s. The UAV needs to hover over each target node area for \(1\) s to collect data. We set the unmanned vehicle loaded with \(N\) UAVs as the middle position \(30m\) away from the working area. The coordinate position of the unmanned vehicle is \(S=(x_{s}=\frac{SL}{2}=400,y_{s}=-30)\). The initial energy value of the drone is \(100\%\). Table 3 shows the percentage of energy consumed per second by the UAV in relation to the corresponding speed. Speed \(0\)m/s represents the percentage of energy consumed per second while the UAV is hovering in the air. As the flight speed of the UAV increases, the percentage energy consumption of the UAV increases linearly [48]. We use the search paths proposed in Section 3 for the working area separately, and we evaluate the proposed search paths using the MUCS-GD algorithm based on that proposed in Section 4. In order to more clearly show the algorithm's performance under different paths, we use the MTSP path as the baseline path for comparison. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline \(W\): The width of the target area & \(\in\)[100, 1350] & m \\ \hline \(L\): The length of the target area & \(\in\)[100, 1350] & m \\ \hline \(D\): The side length of the square cell used to segment the target region & 50 & m \\ \hline \(S\): Coordinates of the takeoff position of the UAV & - & - \\ \hline \(C\): Target region coordinates & - & - \\ \hline \(A\): The set of coordinates of all target regions & \([C_{0,0},\ldots C_{m-1,n-1}]\) & - \\ \hline \(P\): The flight path coordinates of the UAV & \([P_{0},P_{1},\ldots,P_{mn-1}]\) & - \\ \hline \(T\): The hovering time of the UAV & 1 & s \\ \hline \(V\): The flying speed of the UAV & 5, 10, 15, 20 & m/s \\ \hline \(SEL\): Energy consumed per second by the UAV at the current speed & Table 3 & - \\ \hline \(STEL\): Energy lost per second while hovering & 0.0757 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation Parameters. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline Speed (m/s) & 0 & 5 & 10 & 15 & 20 \\ \hline Energy consumption (\%) & 0.0757 & 0.110 & 0.135 & 0.210 & 0.300 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: UAV flight speed and energy consumption comparison table. Figure 11 shows the five search paths, the MTSP path, the snake curve path, the \"square wave signal\" curve path, the Hilbert curve path, and the Moore curve path, as the UAV flight paths in a square working area of \((16D\times 16D)\)m\({}^{2}\). The UAVs fly at \(V=\) (5 m /s, 10 m/s, 15 m/s, 20 m/s) at different flight speeds. Based on using the MUCS-GD algorithm to assign the working path for each UAV, the time comparison graph for completing the search of the whole working area is shown. From Figure 11, we can see no major difference in the route planning algorithms that can be applied to this region when using the MUCS-GD algorithm. When the speed of the UAV is \(V=\) 5 m/s, using the MTSP path as a UAV flight path requires using more UAVs to show similar performance than the other path algorithms. When the UAV speed is \(V=\) 10 m/s, our \"square wave signal\" curve path algorithm demonstrates strong performance using fewer UAVs than the other path algorithms, but does not significantly increase the time to complete the task. Figure 11 shows the five search paths, the MTSP path, the snake curve path, the \"square wave signal\" curve path, the Hilbert curve path, and the Moore curve path, as the UAV flight paths in a square working area of 800 m\({}^{2}\). The UAVs fly at \(V=\) (5 m /s, 10 m/s, 15 m/s, 20 m/s) at different flight speeds.