Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning

• URL: http://arxiv.org/abs/2308.09075v1
• Date: Thu, 17 Aug 2023 16:05:44 GMT
• Title: Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning
• Authors: Prajit KrisshnaKumar, Jhoel Witter, Steve Paul, Hanvit Cho, Karthik Dantu, and Souma Chowdhury
• Abstract summary: Urban Air Mobility (UAM) aircraft are conceived to operate from small airports called vertiports. 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.
• Score: 7.2547164017692625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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