Short vs. Long-term Coordination of Drones: When Distributed Optimization Meets Deep Reinforcement Learning

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.