Tackling Airspace Congestion: A Scalable and Robust Framework for End-to-End UAS Traffic Management

We present an end-to-end air traffic management framework for Unmanned Aircraft Systems (UAS) operations that is both scalable and robust. Our approach involves defining congestion in the airspace and developing a congestion-based cost map that can mitigate potential congestion while adhering to regulations and guidelines. Each UAS operation leverages a recursively updated cost map in solving the path planning problem, providing scalability of the framework. Additionally, we introduce a time-critical controller to enhance the robustness of mission execution. Empirical evidence confirms the feasibility and effectiveness of our method, achieving significant reductions in both cumulative and maximum levels of airspace congestion.