# Emergent Search of UAV Swarm Guided by the Target Probability Map
## 4 The Steps of ESUSTPM
The simulation environment is written in Python, and the animation module in Matplotlib library is used to simulate the movement of UAV agents, the communication connectivity and the target existing probability. According to the proposed novel flocking rule, the agents repel each other when the distance between them is less than the safe distance. Therefore, the collision between agents can be avoided by the proposed local rule. When the agent meets the boundary, the velocity is reversed. Each agent is equipped with a radio for communication with other agents. For these experiments, the agents are assumed to have a downward-facing camera, and this setting can capture the below and eight neighboring grids for finding the objects of interest. This simulation does not emulate internal/external camera geometry and instead simply assumes that the area of interest can be divided into cells, and each agent can move freely and continuously across the entire area of interest. Each cell is smaller than the area covered by the camera at any given time. The mission area is evenly divided as \(10\times 10\) grids in the simulation of this paper. A total of 10 UAVs start from the coordinates (100, 100), (100, 200), (100, 300), (100, 400), (100, 500), (200, 100), (200, 200), (200, 300), (200, 400), (200, 500) to execute the cooperative searching mission separately. The maximum velocity of UAV agents is 10 m/s. UAV swarm does not know the target positions at the initial time. UAVs should find the targets and cover the area in the emergent searching process as far as possible. The initial target probability of each grid shown in Figure 5 is set as 0.5, and it indicates that the environment is unknown to UAV swarm at the initial time. The simulation interval is 10 ms. The total simulation time cycle is 200. When the UAV and the target are in the same cell at a certain time, the target is considered to be detected by the airborne sensors. Then, the onboard computer updates the target existing probability of the corresponding grid and determines the target existence of the grid by comparison with the given threshold. Figure 5 is the experimental scenario of the single UAV swarm at the initial time. The red points are UAV agents, and the black lines represent communication connectivity. When the distance between agents is less than the given value, communication connectivity is built. The blue color of the grid shown in Figure 5 represents the target existing probability of each grid, and the color degree denotes that the mission area is unknown to UAV agents. ### Unknown Environment with No Target

#### 5.1.1 Cooperative Search Based on the Proposed ESUSTPM Model

According to the ESUSTPM mechanism, the agents tend to move to the grids with the highest target existing probability. If the sensor on the UAV cannot find the objects of interest, the TEP of the grid decreases. The parameters of the ESUSTPM algorithm are listed in Table 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Searching Method** & \(\mathbf{k_{i}}\) & \(\mathbf{t_{i}}\) & \(\mathbf{c_{i}}\) & \(\delta_{i}\) & \(\delta_{i}\)\({}^{{}^{\prime}}\) \\ \hline ESUSTPM & \([-80,-80,-80,-80]\) & [5,5,5,5] & [120,120,120,120] & [40,40,40,40] & [10,10,10,10] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the ESUSTPM algorithm. Figure 6 is the simulation result at the 200 th time cycle, and the color degree of each grid represents the target existing probability. Compared to Figure 5, the color of most grids becomes lighter in the emergent searching process. It indicates that most grids are covered, and the environmental uncertainty decreases significantly. The experimental results verify the effectiveness of the ESUSTPM algorithm in the unknown environment with no target. #### 5.1.2 Cooperative Search Based on the Classical Random Searching Algorithm

In the classical random searching experiment, the directions of the UAV agents are random, and the step size is constant in each cycle.