Research
My research develops risk-aware methods for integrating drones safely into urban airspace at scale. I build agent-based models of UAV operational risk, optimisation methods for truck–drone delivery and UAV fleet scheduling, and reinforcement learning approaches to dynamic airspace sectorisation. My current work combines weather-adaptive risk modelling with constrained and federated reinforcement learning to schedule delivery drones safely across multiple service providers.
Publications
- A scalable reinforcement learning-based approach to dynamic airspace sectorization(2026, Transportation Research Part C: Emerging Technologies)
- Agent-based Modeling Approach for Operational Risk Assessment of Large-scale Low-altitude UAV Traffic(2025, 2025 Integrated Communications, Navigation and Surveillance Conference (ICNS))
- Risk-based truck-drone delivery optimization (2025, TRISTAN XII, Okinawa, Japan; Wang, Zhang, Beech, Majumdar, Ochieng and Escribano)