# Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets
## 5 Simulation Experiment
### Dynamic Task Planning Simulation
#### 5.2.2 New Task Areas to Be Reconnoitered Appear  
Randomly add a certain number of task areas and assign them to the UAVs as reconnaissance tasks. In this simulation, task areas 13 and 14 are added for reassignment, and the parameters of the new task areas are shown in Table 6.  
The results of the traditional dynamic task planning strategy solution are shown in Table 7, and the corresponding path planning is shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Task Area Value** & **Coordinate (km)** & **Reconnaissance** \\
**Factor** & **Coordinate (km)** & **Areas (km\({}^{2}\))** & **Reconnaissance** \\
**Revenue** & **Revenue** \\ \hline
13 & 0.4 & (90, 130) & 28 & 0.3 \\
14 & 0.35 & (170, 220) & 36 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters of newly added task areas.  
Figure 10: Diagram of task planning for the improved assignment model with reduced UAV.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**UAV No.** & **New Sequence of Reconnaissance Task Areas** & **Reconnaissance Time (h)** \\ \hline
1 & \(\{4,5,10,2,3,6\}\) & \(\{2.159,1.024,1.588,1.585,1.585,1.585,1.585\}\) \\
2 & \(\{8,7,12,1,9\}\) & \(\{1.558,2.157,0.817,0.817\}\) \\
3 & \(\{11\}\) & \(\{3.238\}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Task planning results of improved assignment model with reduced UAV.  
The results of the dynamic task planning strategy solution proposed in this paper are shown in Table 8, and the corresponding path planning is shown in Figure 12.  
In the case of additional task areas, the traditional dynamic assignment model solves the dynamic task planning to obtain a UAV's total reconnaissance gain of 5.227 and a total flight distance of 1537.092 km. The dynamic assignment mechanism proposed in this paper solves the dynamic task planning to obtain the UAV's total reconnaissance gain of 5.245 and total flight distance of 1454.920 km, and the time consumed by the algorithm is 0.056 s. The dynamic allocation mechanism proposed in this paper is once again proved to be more advantageous.  
All the simulation results indicate that the dynamic task assignment mechanism proposed in our work has an excellent capability to handle two contingency situations, which are the reduction in the number of UAVs and the increase in the number of task areas. Furthermore, the objective of minimizing the flight distance of the UAVs can be satisfied, and the total reconnaissance gain of the UAVs is improved, which is an enhancement of the traditional dynamic task assignment model. In the case of 3 UAVs conducting reconnaissance in 12 mission areas, the time required by the algorithm for dynamic planning in the  
Figure 11: Diagram of task planning for the traditional assignment model with added task areas. (Red dot—UAV positions when contingency, triangle—new task area, dashed-line—cancelled initial flight path, dotted line—added new flight path, same below).  
Figure 12: Diagram of task planning for the improved assignment model with added task areas.  
\begin{table}
\begin{tabular}{c c c} \hline
**UAV No.** & **New Sequence of Reconnaissance Task Areas** & **Reconnaissance Time (b)** \\ \hline
1 & [4, 5, 14, 2, 3, 6] & [2.159, 1.024, 1.312, 1.312, 1.312, 1.312] \\
2 & [8, 7, 13, 9] & [1.558, 2.157, 0.923, 0.923, 0.923] \\
3 & [11, 12, 10] & [3.238, 2.058, 3.202] \\ \hline \end{tabular}
\end{table}
Table 8: Planning results of improved assignment model with added task areas.  
second stage is within 0.1s, which is much less than the time of 130s for the algorithm in the first stage of pre-allocation. Thus, the proposed algorithm is effective in real time in the second stage.