Swarms of autonomous interactive drones, with the support of recharging technology, can provide compelling sensing capabilities in Smart Cities, such as traffic monitoring and disaster response. Existing approaches, including distributed optimization and deep reinforcement learning (DRL), aim to coordinate drones to achieve cost-effective, high-quality navigation, sensing, and charging. However, they face grand challenges: short-term optimization is not effective in dynamic environments with unanticipated changes, while long-term learning lacks scalability, resilience, and flexibility. To bridge this gap, this paper introduces a new progressive approach that combines short-term plan generation and selection based on distributed optimization with a DRL-based long-term strategic scheduling of flying direction. Extensive experimentation with datasets generated from realistic urban mobility underscores an outstanding performance of the proposed solution compared to state-of-the-art. We also provide compelling new insights about the role of drones density in different sensing missions, the energy safety of drone operations and how to prioritize investments for key locations of charging infrastructure.

翻译：在充电技术的支持下，自主交互无人机集群可为智慧城市提供极具吸引力的感知能力，例如交通监控和灾害响应。现有方法（包括分布式优化和深度强化学习）旨在协调无人机以实现高性价比、高质量的导航、感知与充电。然而，这些方法面临重大挑战：短期优化在动态环境中难以应对未预期变化，而长期学习则缺乏可扩展性、鲁棒性和灵活性。为弥合这一差距，本文提出了一种渐进式新方法，该方法将基于分布式优化的短期规划生成与选择，与基于深度强化学习的长期飞行方向策略调度相结合。在真实城市交通数据集上的广泛实验表明，所提出的解决方案相较于现有最优方法具有卓越性能。我们还提供了关于无人机密度在不同感知任务中的作用、无人机运行能量安全性以及如何优先投资关键充电基础设施位置的令人信服的新见解。