Master Thesis Internship - Hybrid Vertiport Arrival Manager: Reinforcement Learning Optimization with Rule-Based Safety Supervision

Job description

Background Urban Air Mobility (UAM) and large-scale drone operations will require automated traffic management systems capable of safely sequencing large numbers of aircraft at vertiports. Similar to arrival managers (AMAN) used in conventional Air Traffic Management (ATM), such systems must determine conflict-free arrival sequences while respecting operational constraints such as separation minima, route availability, pad capacity, and vehicle energy limitations. Existing concepts often rely on deterministic rule-based algorithms that guarantee safety and predictability. While these approaches are robust and certifiable, they may become suboptimal in complex traffic situations where many aircraft compete for limited vertiport capacity. Recent advances in Reinforcement Learning (RL) suggest that learning-based methods may improve traffic flow efficiency by discovering optimized sequencing and control strategies. However, purely learning-based systems raise concerns regarding safety, explainability, and certification. A promising approach is therefore a hybrid architecture in which a centralized reinforcement learning-based optimization layer proposes actions while a deterministic r...