# Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets
## 5 Simulation Experiment
### Static Task Assignment Simulation  
To validate the effectiveness of the first stage to the proposed algorithm, which involves the uniformity and rationality of task assignment (ensuring that each UAV is assigned with an appropriate number of tasks, neither excessive nor insufficient, and that the assigned task areas are not far from the location of the UAV), and to assess the reasonableness of the reconnaissance sequence (minimizing the UAV's flight distance), the following scenarios are set for simulation experiments for the given UAV and task area parameters.  
We assume that there are UAVs performing reconnaissance in multiple task areas in a 300 km \(\times\) 300 km 2D operational environment, and the location of each UAV and task area is fixed. Altogether, three UAVs take off from different bases for reconnaissance tasks, and the initial status information of each UAV is shown in Table 1.  
We assume that there are 12 task areas waiting to be reconnoitered, and the reconnaissance values of different task areas are different. The value coefficients of the task areas are denoted by a random variable that follows a uniform distribution between [0,1], and the initial state information of the task areas is shown in Table 2.  
By using the MTCMO algorithm to solve the initial task assignment problem, with a population size of 200 and a maximum evaluation count of 500,000, the results of the initial task assignment for reconnaissance by 3 UAVs for 12 task areas are presented in Table 3, and the reconnaissance roadmap for each UAV is shown in Figure 8.  
The results from the MTCMO algorithm for solving the initial task assignment are as follows: the UAV's total reconnaissance gain is 5.048, the total flight distance is 1056.226 km, and the total reconnaissance time is 23.650 h. From Figure 8, we can observe that the task assignment is uniform, and there are no intersecting flight paths. The simulation results  
\begin{table}
\begin{tabular}{c c c c c} \hline
**UAV No.** & **Speed (km/h)** & **Endurance Time (h)** & **Endurance Time (h)** & **Take-Off** & **Landing Coordinate** \\  & & & & **Coordinate (km)** & **(km)** \\ \hline
1 & 200 & 10 & 0.3 & (224, 289) & (224, 289) \\
2 & 200 & 10 & 0.3 & (265, 23) & (265, 23) \\
3 & 200 & 10 & 0.3 & (71, 295) & (71, 295) \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of UAV performances.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Task Area Value Factor** & **Coordinate (km)** & **Reconnaissance Areas** & **Minimum** \\  & & & **(km\({}^{*}\))** & **Reconnaissance Revenue** \\ \hline
1 & 0.4 & (140, 116) & 50 & 0.3 \\
2 & 0.35 & (191, 169) & 15 & 0.2 \\
3 & 0.45 & (298, 197) & 32 & 0.2 \\
4 & 0.47 & (167, 296) & 38 & 0.3 \\
5 & 0.31 & (209, 218) & 15 & 0.2 \\
6 & 0.4 & (245, 243) & 18 & 0.2 \\
7 & 0.7 & (30, 62) & 49 & 0.3 \\
8 & 0.52 & (171, 54) & 41 & 0.3 \\
9 & 0.57 & (201, 120) & 49 & 0.2 \\
10 & 0.37 & (143, 257) & 57 & 0.3 \\
11 & 0.4 & (62, 254) & 59 & 0.2 \\
12 & 0.39 & (104, 177) & 30 & 0.2 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of reconnaissance task area.  
Figure 8: Diagram of the initial task assignment result. (Circle—task area, square—base, straight line—initial flight path (not including return), same below).  
show that the algorithm can effectively solve the multi-objective problem of multi-UAV task assignment under the complex constraints of a static task. The time consumed by the task allocation algorithm is 130.57 s.