# Emergent Search of UAV Swarm Guided by the Target Probability Map
###### Abstract  
In the cooperative searching scenario, most traditional methods are based on the top-down mechanisms. These mechanisms are usually offline and centralized. The characteristics limit the adaptability of unmanned aerial vehicle (UAV) swarm to the complex mission environments, such as those with inaccurate information of the targets and grids. In order to improve the searching ability of UAV swarm, a novel searching method named emergent search of UAV swarm guided by the target probability map (ESUSTPM) is proposed. ESUSTPM is based on local rules to organize and guide UAV agents to achieve the flocking state, search the mission area and detect the hidden targets concurrently. In ESUSTPM, local rules contain the flocking rules and the guiding rules. The flocking rules are the interactions between the agents, which are designed by a novel constructed function based on two exponential functions in this paper. The new constructed function can better maintain the relatively stable distances between the agents and realize the smooth transition of the positions at the given centers. The local guiding rules based on the target probability information of the nearby grids are firstly designed to realize the multi-function of the swarm, including full area coverage, target detection and reduction in environmental uncertainty (EU). Finally, the simulations verify that ESUSTPM can achieve the full coverage of the mission area while taking into account the target search. The statistical results also indicate that the searching efficiency of the proposed ESUSTPM is higher than the traditional searching algorithms based on the division and allocation of the area or the heuristic algorithms. U 2022 16 May 2022 16 May 2022 16 May 2022 16 May 20222 16 May 20222 16 May 20222 16 May 20222 1  
The early classical cooperative searching algorithms focus on the division and distribution of the searched area. In the searching process, the coordination between UAV agents in different grids is weak. The mission environment is usually complex and, due to a lack of prior information, a set of mechanisms, such as the digital pheromone map (DPM) and the target probability map (TPM) are constructed for determining the flight directions [8,9]. The DPM is adopted to solve the swarm coordination problem, and the TPM accounts for the target guidance. The two information maps are updated in the moving process of UAVs, and the coordinates of UAV agents are recalculated by taking the cognitive information as the decision function. The cooperative searching algorithms based on the two information maps, including DPM and TPM, can avoid collision and repeated search as much as possible. However, factors such as resource consumption and path optimality are not considered in these cooperative searching strategies. Therefore, the heuristic algorithms attract the attention of many researchers. Considering that multiple targets are usually randomly distributed in the mission area, the cooperative search is regarded as the traveling salesman problem (TSP) in some literature works. In the TSP, resource consumption and path optimization are optimized during optimization iteration. In reference [10], an improved cooperative searching algorithm based on the non-dominated sorted genetic algorithm-II is put forward to solve a multi-objective optimization problem. The objective function combines the detection profit, energy consumption and flight distance criteria for obtaining the optimized searching routes of unmanned aerial vehicles (UAVs). In reference [11], an improved harmony searching algorithm, taking maximum total reconnaissance as the fitness function, is adopted to search lots of targets in a given area under the condition of resource constraints. Similarly, a novel cooperative patrol task algorithm based on the improved push forward insertion heuristic (PFIH) algorithm is presented in reference [12] to calculate the feasible patrolling routes for maximizing the patrol profit under the limited resources conditions.