Application of DRL-Based Algorithm for the Resolution of Strategic Conflicts in U-Space Airspaces

The rapid expansion of Unmanned Aircraft Systems (UAS) operations has created an urgent need for scalable strategic conflict resolution methods within the U-space framework. When requested 4D flight plans overlap with previously authorised ones, the Flight Authorisation Service (FAS) denies the request and can provide the UAS operator with an alternative, conflict-free route. While traditional pathfinding algorithms ensure optimal routes, their computational cost creates a critical bottleneck during the flight activation phase or emergency missions, which demand near-instantaneous responses. To address this, we propose a three-stage framework. First, an Octree spatial partitioning discretises the airspace to identify occupied cells. Second, the A* algorithm is implemented to establish an optimal reference route. Finally, a standard Deep Reinforcement Learning (DRL) model, trained on realistic PX4 Simulator trajectories and using a well-adjusted reward function, generates alternative paths that optimise distance and energy. Our results demonstrate that this DRL architecture achieves near-optimal routing behaviour. Crucially, it reduces computation time by several orders of magnitude compared to A*, solving complex conflicts in milliseconds rather than seconds. We conclude that a simple, well-tuned DRL architecture overcomes latency limitations of classical pathfinding while achieving optimal results, ensuring rapid, safe, and efficient conflict resolution for high-density U-space.