
The paper proposes a novel approach, DO-RL, that integrates distributed optimization and deep reinforcement learning to coordinate a swarm of autonomous drones for efficient traffic monitoring. The approach enables drones to strategically determine their flying directions and recharging locations while autonomously optimizing their navigation and sensing operations.


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coordinating-autonomous-drones-for-efficient-traffic-monitoring-integrating-distributed-optimization-and-deep-reinforcement-learning


Coordinating Autonomous Drones for Efficient Traffic Monitoring: Integrating Distributed Optimization and Deep Reinforcement Learning


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This work shares experiences and results obtained during the construction and active use of a testbed for comparing simulations and field tests of drone coordination algorithms in Flying Ad-Hoc Networks (FANETs).


experiences-results-and-reflections-from-real-world-experiments-with-fanets-and-uavs-extended-version


Experiences, Results, and Reflections from Real-World Experiments with FANETs and UAVs - Extended Version
