# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 5 Evaluation
### Comparison of Search Paths  
In order to comprehensively demonstrate the superiority of the curve search path based on square wave signals, we established six square working areas with side lengths \(SL\). We set the moving speed of the UAV as \(V=5\) m/s, which hovers for 1 s upon arriving at a target node area for information collection.  
Figure 12: Comparison of MUCS-BSAE for the same number of UAVs based on different routes for a square area of \((16D\times 16D)^{2}\).  
Figure 13: Comparison of MUCS-BSAE with MUCS-GD \((16D\times 16D)^{2}\).  
\(SL=(1350m=27D\), \(1400m=28D\), \(1450m=29D\), \(1500m=30D\), \(1550m=31D\), \(1600m=32D\)).  
Figure 14 shows the working performance of different search paths in six square work areas of different sizes, where the bar chart represents the time required for the UAVs to complete the tasks. The point chart in the corresponding color of the bar chart represents the number of UAVs under the working time. If a certain search path cannot be applied in the work area of that size, both the bar chart and the point chart at the corresponding position will have a value of 0. From Figure 14, we can easily find that the MTSP path, the \"square wave signal\" curve search path, and the snake curve search path have better practicality and can adapt to work areas of any size, while the Peano curve path, the Hilbert curve path, and the Moore curve path can only be applied in specific work areas. Moreover, we can clearly see that the \"square wave signal\" curve path has a significant efficiency advantage when working with other search paths using the same number of UAVs. Therefore, we can demonstrate that in a square work area, the \"square wave signal\" curve search path has better applicability and can achieve the highest work efficiency.  
In Figure 14, we can observe that the MTSP path, the \"square wave signal\" curve search path, and the snake curve search path have better applicability in the square work area. We further evaluated the performance of these three path-planning algorithms in various regions. As shown in Figure 5, the square work area has the same length and width, and the entire work area has either an odd number of target detection cells multiplied by an odd number or an even number of target detection cell multiplied by an even number. In contrast, the rectangular work area has unequal length and width, and the entire work area can have an odd number multiplied by an even number of target detection cells or an even number multiplied by an odd number of target detection cells. We set the speed of UAV at \(V=5\) m/s. The working area of the odd-numbered target detection cell multiplied by the odd-numbered nodes is set to \(750m\times 750m\) (\(15D\times 15D\)). The working area of the even-numbered target detection cells multiplied by the even-numbered nodes is set to \(800m\times 800m\) (\(16D\times 16D\)). The working area of the odd-numbered target detection cell multiplied by the even-numbered nodes is set to \(750m\times 800m\) (\(15D\times 16D\)). The working area of the even-numbered target detection cells multiplied by the odd-numbered nodes is set to \(800m\times 750m\) (\(16D\times 15D\)). In these four cases, Figure 15 shows the performance of the MTSP path, the \"square wave signal\" curve search path, and the snake curve search path.  
Figure 14: Comparison of working time based on different areas.  
path. It is easy to see that, in any situation, the \"square wave signal\" curve search path is significantly superior to the snake curve search path and the MTSP path.