AI-based Dynamic Re-routing for Dense Low-Altitude Air Traffic Management

Thanks to their rapid uptake in various industries, an increasing number of Uninhabited Aircraft Systems (UAS) and other emerging aerospace platforms is expected to operate in the shared airspace. Viable conflict detection and resolution as well as demand-capacity balancing (DCB) services will be required to ensure the desired level of safety, particularly with the proliferation of Beyond Line-of-Sight (BLOS) operations. This paper proposes a novel UAS Traffic Management (UTM) system DCB functionality adopting multiple Artificial Intelligence (AI) algorithms to manage both regular and emergency situations. The system is based on a four-dimensional trajectory (4DT) planning algorithm with a flexible DCB process and solution framework. The method is not limited to fixed routing, but can also adjust dynamically to evolving conditions. The selected AI techniques are based on the most suitable machine learning and metaheuristic algorithms. Simulation case studies demonstrate that the proposed method allows to achieve a safe and efficient management of dense traffic in low-altitude airspace around cities and suburbs.