グラフ強化学習による都市航空モビリティ・バーティポートのスケジュール管理意思決定支援
Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning
都市航空モビリティのバーティポートにおける離着陸スケジュール管理を、物理スポットと機体を別々のグラフで表現しグラフ畳み込みネットワークと強化学習で最適化する手法を提案。AirSimシミュレーションで遅延・衝突・バッテリー消費の改善を実証した。
著者: Prajit KrisshnaKumar, Jhoel Witter, Steve Paul, Hanvit Cho, Karthik Dantu, Souma Chowdhury
分類: cs.MA, cs.AI, cs.LG, cs.RO
原文アブストラクト
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.
