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.
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).