# Emergent Search of UAV Swarm Guided by the Target Probability Map
###### Abstract
in [25] propose a control strategy of self-organization obstacle avoidance based on the conformal module and obstacle avoidance module. The conformal module is used to realize the directional consistency. In terms of avoidance module, the principle of earth deflection is used to increase the rotational force effect for collision between agents and obstacles to reach the mission point. Constrained by the agent dynamic, communication delay, perturbations or the presence of barriers, the numerous existing flocking models cannot perform a sufficiently good collective behavior for full coverage or path generation from one point to another point. In order to make swarm better complete tasks and enhance environmental adaptability, the rule-based model and evolution algorithm are combined to generate the optimized control model for UAV swarm [26; 27; 28; 29; 30]. The corresponding research results verify that this strategy can generate multi-objective optimization conclusions. As for the area coverage of UAV swarm, [PERSON] et al. propose four local rules to achieve full coverage of the mission area and good communication connectivity [31; 32]; then, the evolution algorithm is used to generate the optimized solution. The paper realizes the best coverage strategy, but environment uncertainty and target search are not considered. The current research points mainly focus on the implementation of flocking rules and the single task with multiple constraints in the numerous existing flocking models. Considering the economy and efficiency, a multi-function swarm is needed. The tasks of the UAV swarm cooperative search are full area coverage, target detection and reduction in environmental uncertainty. The combination of these requirements makes the cooperative search of UAV swarm in a top-down manner a challenging problem. Conversely, the requirements are relatively easy to be realized in an emergent mode. For improving the adaptability of UAV swarm to the environment and the various complex requirements, ESUSTPM is proposed. ESUSTPM is a novel searching method, and swarm searching behavior is shaped in a down-top manner. The down-top manner means that the high-level swarm behaviors are generated by the agent-to-agent interactions. The generation mode of the swarm searching behaviors determines that swarm shows high intelligence, high self-organization and adaptability in the emergent process. These characteristics improve the flexibility and survivability of UAV swarm in the cooperative search of the complex mission area. As the proposed ESUSTPM is based on local rules, including the flocking and guiding rules, it enables UAV agents to realize the decentralized and online path planning. The main contributions of this study are as follows:  
* A new flocking rule based on a novel constructed function, which can realize smooth transition of the UAV states at the center points, is proposed. The constructed function is Equation (9), and it contains two exponential functions shown in Equations (7) and (8). Equation (8) is a typical flocking rule that was proposed in references [20; 21]. The slope of the corresponding curve is so high at the center point that it is difficult to avoid state oscillation of the UAV. Therefore, a novel adjusting function in Equation (7) is proposed to adjust the curve slope of Equation (8) at the center point. Thus, the proposed curve proposed in Equation (9) can transit smoothly at the center point. This design realizes a smooth transition of the UAV states at the center points in theory. Finally, a more stable space between agents can be maintained. * A new guiding rule based on TPM is proposed. The guiding mechanism always guides the UAV to grids with the highest target existing probability. During access to the grids, TPM can be calculated and updated according to Equations (3)-(5). Therefore, the multi-functions of UAV swarm, including full area coverage, target detection and reduction in environmental uncertainty, can be realized. * The fault tolerance to prior information on the TPM is realized.