# Emergent Search of UAV Swarm Guided by the Target Probability Map
## 4 The Steps of ESUSTPM
The given safe distance between the agents is also 120 m. Figure 10 shows the experimental result of the searching algorithm based on the Boids model with guidance. The color of the partial grids is still blue, and this indicates that these grids are not visited within 200 time cycles. It proves that the efficiency of the searching algorithm based on the Boids model with guidance is lower than the proposed ESUSTPM algorithm in this paper. Figure 11 shows the relationship between the grids visited number and time cycles of the five searching algorithms. The red line, purple line, green line, orange line and the indigo line represent the proposed ESUSTPM algorithm, the mower searching algorithm, the searching algorithm based on the Boids model with guidance, the searching algorithm based on the Boids model without guidance, the classical random searching algorithm, respectively. It shows that the completion time of the proposed searching algorithm is almost equal to the mower searching algorithm, but the other searching algorithms cannot achieve the area coverage search within the given time. Generally, the mower searching algorithm has the highest coverage efficiency. Therefore, the conclusion is drawn that the ESUSTPM algorithm is efficient. Figure 9: Cooperative search results based on the Boids model without guidance. Figure 12 shows the change of the environmental uncertainty derived by the five searching algorithms during 200 time cycles. The purple line represents the result of the mower searching algorithm, and the red line is the result of the proposed ESUSTPM algorithm. During the stipulated experiment time, the environmental uncertainty of the proposed ESUSTPM algorithm decreases more than with the other searching algorithms. In all, combining the results of Figures 11 and 12, the proposed method is most effective in terms of area coverage and the reduction in environmental uncertainty at the same time. ### Unknown Environment with Static Target

In the unknown searching scenario with some targets, the coordinates of three targets are (400, 500), (400, 700) and (700, 500). The initial positions of UAV agents are distributed in the lower left corner. At the initial time, the UAV swarm does not know whether there is a target in the mission area. Therefore, the initial target probabilities of all grids are set as 0.5, and the color of each grid is blue in Figure 5. In ESUSTPM, the UAV agent visits the nearby grid with the highest target probability. If the airborne sensors cannot detect the targets in

\[\stackrel{{\rightharpoonup}}{{dir}}=\frac{rnd(d,1)}{\|rnd(d,1) \|}, \tag{12}\]0 Relationship between the grid visited number and time cycles. \[\stackrel{{\rightharpoonup}}{{dir}}=\frac{rnd(d,1)}{\|rnd(d,1) \|}, \tag{12}\]1 Cooperative search results based on the Boids model with guidance. the visited grid, the target existing probability decreases, and the color of the grid becomes lighter. Conversely, the target existing probability increases when the target is detected by the airborne sensors in the corresponding grid. Meanwhile, the blue color of the grid becomes deeper. When the probability value in a grid is higher than the given value, the onboard computer confirms that the target exists in this grid, and the corresponding color becomes red. The simulation cycles are also 200 time cycles in this experiment. #### 5.2.1 Cooperative Target Search Based on the Proposed ESUSTPM Model

In target searching mission, the parameters of the local rules in ESUSTPM are the same as the parameters in \[\stackrel{{\rightharpoonup}}{{dir}}=\frac{rnd(d,1)}{\|rnd(d,1) \|}, \tag{12}\]2 Within the 200 time cycles, the number of the red grids increases from Figures 13-15, and the color of the other grids also becomes lighter, as shown in \[\stackrel{{\rightharpoonup}}{{dir}}=\frac{rnd(d,1)}{\|rnd(d,1) \|}, \tag{12}\]3 This experimental result proves that the proposed ESUSTPM algorithm is feasible, and ESUSTPM can not only find the targets but also cover 94% of the area.