Short vs. Long-term Coordination of Drones: When Distributed Optimization Meets Deep Reinforcement Learning.
CoRR(2023)
摘要
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.
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