With the advancement of unmanned aerial vehicle technology, dynamic monitoring with drones has been widely adopted to enhance multi-source information awareness capabilities. The cooperative strategy among drones still poses a significant challenge. Redundant actions within the drone swarm system can lead to a noticeable decrease in awareness performance. In this work, an advanced cooperative multi-hive drone swarm system is developed, which integrates multiple drones for information awareness and multiple hives for battery replacement. The system response is modeled by a series of discrete system state-action sequence, which follows a parallel system state transition mode. A well-designed simulated annealing-based hybrid algorithm (SA-HA) is developed for system response optimization, of which the simulated annealing process is adopted to coordinate two heuristic operators. To avoid redundant actions, an asynchronous cooperation mechanism (ACM) is proposed to strengthen the collaboration among agents in staggered system time intervals. Computational results indicate that the involvement of ACM can extract more problem-specific knowledge, which makes SA-HA easier to get high-quality system state-action sequences. Through the system redundancy analysis, we found that properly configured drones and hives can achieve high-efficiency global dynamic multi-source information awareness. The proposed system can provide pivotal support for regional situation awareness and analysis.

中文翻译：

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一种先进的协作式多蜂巢无人机群系统，用于全球动态多源信息感知

随着无人机技术的进步，无人机动态监控被广泛采用，增强多源信息感知能力。无人机之间的合作策略仍然构成重大挑战。无人机群系统内的冗余动作可能会导致感知性能显着下降。在这项工作中，开发了一种先进的协作式多蜂巢无人机群系统，该系统集成了用于信息感知的多个无人机和用于电池更换的多个蜂巢。系统响应由一系列离散系统状态动作序列建模，遵循并行系统状态转换模式。开发了一种精心设计的基于模拟退火的混合算法（SA-HA）用于系统响应优化，其中采用模拟退火过程来协调两个启发式算子。为了避免冗余动作，提出了一种异步协作机制（ACM），以在交错的系统时间间隔内加强代理之间的协作。计算结果表明，ACM的参与可以提取更多针对问题的知识，这使得SA-HA更容易获得高质量的系统状态-动作序列。通过系统冗余分析，我们发现适当配置的无人机和蜂巢可以实现高效的全局动态多源信息感知。所提出的系统可以为区域态势感知和分析提供关键支持。