Centralized Landing Flow Merging for Drones Using Deep Reinforcement Learning

Abstract

Drones are expected to support applications such as emergency response, parcel delivery, and infrastructure monitoring in dense urban airspaces, creating traffic levels that are unmanageable for human operators. Autonomous separation management is therefore essential, combining strategic and tactical control to prevent conflicts. This paper addresses the tactical landing phase by introducing a centralized landing flow manager—a reinforcement learning (RL) agent that adjusts drone speed and heading to merge landing flows safely and efficiently prior to a final approach fix. The objective of the work was to demonstrate the potential of reinforcement learning in this novel context, by implementing and evaluating it in simulation and testing its capabilities with 10 concurrent landing drones. The RL agent learns to successfully separate traffic, thereby lowering intrusion counts compared to the baseline autopilot, but is outperformed in safety by the decentralized Modified Voltage Potential (MVP) method due to outlier scenarios. Nevertheless, the RL-based system achieves faster scenario completion and thus a higher overall throughput, by speeding up the vehicles towards the final approach fix. Future work will explore improved network architectures, transfer learning across varied scenarios, and algorithmic fine-tuning to further enhance safety performance.