Swarms of smart drones, with the support of charging technology, can provide completing 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 recharging. However, they have distinct challenges: short-term optimization struggles to provide sustained benefits, while long-term DRL lacks scalability, resilience, and flexibility. To bridge this gap, this paper introduces a new progressive approach that encompasses the planning and selection based on distributed optimization, as well as DRL-based flying direction scheduling. Extensive experiment with datasets generated from realisitic urban mobility demonstrate the outstanding performance of the proposed solution in traffic monitoring compared to three baseline methods.

翻译：智能无人机群在充电技术的支持下，可为智慧城市（如交通监测和灾难响应）提供完备的感知能力。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经济高效的高质量导航、感知和充电。然而，它们面临独特的挑战：短期优化难以提供持续收益，而长期深度强化学习缺乏可扩展性、鲁棒性和灵活性。为弥合这一差距，本文提出一种渐进式新方法，涵盖基于分布式优化的规划与选择，以及基于深度强化学习的飞行方向调度。基于真实城市交通生成数据集的广泛实验表明，与三种基线方法相比，所提方案在交通监测任务中表现出卓越性能。