An advanced cooperative multi-hive drone swarm system for global dynamic multi-source information awareness

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.