Coordinating multiple unmanned aerial vehicles (multi-UAVs) is a challenging technique in highly dynamic and sophisticated environments. Based on digital pheromones as well as current mainstream unmanned system controlling algorithms, we propose a strategy for multi-UAVs to acquire targets with limited prior knowledge. In particular, we put forward a more reasonable and effective pheromone update mechanism, by improving digital pheromone fusion algorithms for different semantic pheromones and planning individuals’ probabilistic behavioral decision-making schemes. Also, inspired by the flocking model in nature, considering the limitations of some individuals in perception and communication, we design a navigation algorithm model on top of Olfati-Saber’s algorithm for flocking control, by further replacing the pheromone scalar to a vector. Simulation results show that the proposed algorithm can yield superior performance in terms of coverage, detection and revisit efficiency, and the capability of obstacle avoidance.

在高度动态和复杂的环境中，协调多个无人机（multi-UAV）是一项具有挑战性的技术。基于数字信息素以及当前主流的无人系统控制算法，我们提出了一种用于多无人机的战略，以获取先验知识有限的目标。特别地，通过改进针对不同语义信息素的数字信息素融合算法并规划个人的概率行为决策方案，我们提出了一种更合理有效的信息素更新机制。此外，受自然界中的植绒模型启发，考虑到某些人在感知和交流方面的局限性，我们通过进一步将信息素标量替换为矢量，在Olfati-Saber的植绒控制算法基础上设计了一个导航算法模型。