Abstract
This paper addresses the problem of distributed 3D area coverage with multiple UAVs under incomplete information. It proposes a leader-follower UAV framework that integrates pheromone-based coverage and reinforcement learning dispatching. The 3D space is segmented for dimensionality reduction, and pheromone matrices enable dynamic area coverage. To reduce redundant coverage and achieve multi-layered coordination, the leader UAV uses a virtual decision mechanism and a communication neural network to handle local observations and asynchronous scheduling challenges. Additionally, an event-triggered reinforcement learning is introduced to minimize communication costs and enhance system robustness. Pheromone-based implicit teamwork addresses sparse rewards and improves coverage efficiency. This modular framework enhances general applicability and significantly reduces overall coverage time.
Published Version
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