Fast Decision Support for Air Traffic Management at Urban Air Mobility
  Vertiports using Graph Learning
Prajit KrisshnaKumar Jhoel Witter Steve Paul Hanvit Cho Karthik Dantu Souma Chowdhury
Urban Air Mobility (UAM) promises a new dimension to decongested, safe, and
fast travel in urban and suburban hubs. These UAM aircraft are conceived to
operate from small airports called vertiports each comprising multiple
take-off/landing and battery-recharging spots. Since they might be situated in
dense urban areas and need to handle many aircraft landings and take-offs each
hour, managing this schedule in real-time becomes challenging for a traditional
air-traffic controller but instead calls for an automated solution. This paper
provides a novel approach to this problem of Urban Air Mobility - Vertiport
Schedule Management (UAM-VSM), which leverages graph reinforcement learning to
generate decision-support policies. Here the designated physical spots within
the vertiport's airspace and the vehicles being managed are represented as two
separate graphs, with feature extraction performed through a graph
convolutional network (GCN). Extracted features are passed onto perceptron
layers to decide actions such as continue to hover or cruise, continue idling
or take-off, or land on an allocated vertiport spot. Performance is measured
based on delays, safety (no. of collisions) and battery consumption. Through
realistic simulations in AirSim applied to scaled down multi-rotor vehicles,
our results demonstrate the suitability of using graph reinforcement learning
to solve the UAM-VSM problem and its superiority to basic reinforcement
learning (with graph embeddings) or random choice baselines.