# Emergent Search of UAV Swarm Guided by the Target Probability Map
## 4 The Steps of ESUSTPM
In Figure 27b, the color of grid (300, 200) becomes red in the emergent searching process, but the color of grids (400, 500), (400, 700), (700, 500) becomes lighter. The target is considered unlikely to exist in the grid (300, 200), but the target is detected and does exist in this grid. The results in Figure 27 indicate that the proposed ESUSTPM algorithm can discover the hidden targets, and it has fault tolerance to wrong prior information. \[\stackrel{{\rightharpoonup}}{{P_{i}}}(t+1)=\stackrel{{ \rightharpoonup}}{{P_{i}}}(t)+\stackrel{{\rightharpoonup}}{{dir}} \cdot S\cdot\Delta t, \tag{13}\]9 The target searching results of ESUSTPM in the environment with inaccurate information. (**a**) The target is expected to exist in the grid (700, 500), but it does not actually exist; (**b**) The target is not expected to exist in the grid (300, 200), but it does actually exist. Figure 5:0 The statistical target searching results of ESUSTPM with and without prior information. For intuitively displaying the pros and cons of the above searching methods, Table 2 lists the comparative results of three metrics, including the visited grid number, environmental uncertainty and target found number at the 200 th cycle. The results in Table 2 indicate that the proposed algorithm can minimize the environmental uncertainty within the 200 time cycles. Furthermore, the efficiency of the proposed algorithm is also the highest among these searching methods within 200 time cycles. ### Statistical Analysis and Summary

To verify the robustness of the proposed ESUSTPM algorithm, five simulations with different indices, including the time cost of full coverage, environmental uncertainty and time cost of target search, are executed 10 times. Figure 28 is the full coverage time distribution of the five searching methods. The boxplots show the simulation time cycle distribution of the full area coverage based on the classical random algorithm, the mower searching algorithm, the searching algorithm based on the Boids model without guidance, the searching algorithm based on the Boids model with guidance and the proposed ESUSTPM algorithm, from the left to the right side. The statistical results verify that the searching algorithms with a guidance mechanism are better than the algorithms without guidance. The time consumption of the proposed ESUSTPM algorithm is very close to the mower searching algorithm, and the time distribution of the proposed ESUSTPM algorithm is relatively concentrated. The results shown in Figure 28 prove that the proposed ESUSTPM method has a good efficiency in the searching mission of the area coverage. The boxplots in Figure 29 are the distributions of the environmental uncertainty in the area coverage searching scenario at 200 time cycles. The median value of the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Searching Methods**} & \multicolumn{3}{c}{**Grid with Target**} & \multicolumn{3}{c}{**Grid without Target**} \\ \cline{2-6}  & **Visited Grid** & **Environmental** & **Target Found** & **Visited Grid** & **Environmental** \\  & **Number** & **Uncertainty** & **Number** & **Number** & **Uncertainty** \\ \hline The classical random searching algorithm & 26 & 43.92 & 0 & 29 & 42.76 \\ The mower searching algorithm & 100 & 52.34 & 0 & 100 & 52.34 \\ The searching algorithm based on Boids model without guidance & 53 & 42.86 & 1 & 40 & 43.93 \\ The searching algorithm based on Boids model with guidance & 75 & 42.11 & 2 & 82 & 43.53 \\ The proposed algorithm & 89 & 40.23 & 3 & 95 & 40.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the above five searching methods. Figure 28: Full coverage time cost distribution of the five algorithms. proposed ESUSTPM algorithm is the lowest, and the environment uncertainty distribution is concentrated. This indicates the effectiveness of the proposed ESUSTPM algorithm in the index of environmental uncertainty. Figure 30 shows the statistical results of the time cycles distribution from one found target to three found targets.