# Emergent Search of UAV Swarm Guided by the Target Probability Map
###### Abstract
UAVs often execute the cooperative search in an unknown environment with no prior information. In order to improve the searching efficiency and reduce the uncertainty of the mission area, many cooperative searching models based on the bio-heuristic algorithms, including the pigeon-inspired optimization (PIO), particle swarm optimization (PSO), bean optimization (BO), multiple ant colony optimization (MACO) and co-evolution genetic algorithm (CEGA), are built, as shown in references [13, 14, 15, 16, 17, 18, 19]. In these heuristic algorithms, UAVs are analogized as some particles or gene segments. The intelligent cooperative searching behaviors of UAV swarm under many constraints are generated by the interaction between the particles and the cross variation of gene segments for achieving the feasible rewarding function, such as the coverage rate, targets discovering profits, cooperation profits and so on. The designed algorithms can be adopted to obtain the optimal results for realizing the overlaying search and targets discovery of the region effectively. The reinforcement learning (RL) scheme is used to study the cooperative search of multi targets in the unknown sea areas by UAVs in reference [20]. In order to ensure searching efficiency, a reward and punishment function is built to generate a multi-UAV cooperative search path. In view of the trial-and-error mechanism of RL, this mechanism increases the computing burden. Therefore, it is not suitable for large-scale scenarios. The cooperative searching algorithms based on the heuristic and RL mechanisms usually draw the conclusion by performing multi-objective iteration. The process often takes a long time to deal with the dynamic path planning task. With the increase in the agent numbers and the environmental complexity, the computational burden of UAV onboard the computer will increase. Therefore, a decentralized searching algorithm with real-time computation and sufficient intelligence becomes positively significant. With the rapid development of UAVs and sensors, robotic swarms can conduct some complex mission or specific tasks by interacting with each other. In a decentralized swarm system, all agents can receive information from the other agents and the environment to decide on what to do next. [PERSON] et al. in reference [21] propose typical local rules of swarm behavior. It is named the Boids model, which follows three local rules: collision avoidance, velocity matching and flocking centering. The Boids model can realize the flocking state and velocity alignment. In most collective behaviors, it is hypothesized that each agent in the group tends to align its direction of motion with those of its neighbors. [PERSON] et al. in reference [22] bring the nearest neighbor principle into the Boids model. This model considers the change of the moving direction of the monomer. Therefore, the monomer can finally achieve the same moving direction, but this model cannot guarantee collision avoidance. [PERSON] et al. in [23] suggest that the collective polarization arises from interactions other than the explicit alignment rule. Thus, a new lattice-based computational model that is based on mutual anticipation and asynchronous updating is proposed. The simulation shows that this model can realize densely collective motion with high polarity. In order to overcome the drawbacks of recent papers on motion control for swarm, including the use of unbounded forces for collision avoidance, lack of scalability, irregular fragmentation and collapse, [PERSON] in reference [24] presents a theoretical and computational framework for the design and analysis of scalable flocking algorithms in a multi-dimension space in the presence or lack of obstacles. This analysis sheds light on the cooperation and emergence of collective behavior in complex organizations. A bottom-up flocking model is suitable to improve the survivability of swarm in complex environment with obstacles. [PERSON] et al.