Self-organized arrival system for urban air mobility
Martin Waltz Ostap Okhrin Michael Schultz
Urban air mobility is an innovative mode of transportation in which electric
vertical takeoff and landing (eVTOL) vehicles operate between nodes called
vertiports. We outline a self-organized vertiport arrival system based on deep
reinforcement learning. The airspace around the vertiport is assumed to be
circular, and the vehicles can freely operate inside. Each aircraft is
considered an individual agent and follows a shared policy, resulting in
decentralized actions that are based on local information. We investigate the
development of the reinforcement learning policy during training and illustrate
how the algorithm moves from suboptimal local holding patterns to a safe and
efficient final policy. The latter is validated in simulation-based scenarios
and also deployed on small-scale unmanned aerial vehicles to showcase its
real-world usability.