Graph Learning Based Decision Support for Multi-Aircraft Take-Off and Landing at Urban Air Mobility Vertiports
Graph Learning Based Decision Support for Multi-Aircraft Take-Off and Landing at Urban Air Mobility Vertiports
复制标题
DOI：
10.2514/6.2023-1848
复制
发表时间：
2023-01
期刊：
影响因子：
--
通讯作者：
Prajit K. Kumar;Jhoel Witter;Steve Paul;Karthik Dantu;Souma Chowdhury
中科院分区：
文献类型：
--
作者：
Prajit K. Kumar;Jhoel Witter;Steve Paul;Karthik Dantu;Souma Chowdhury
城市空中交通（UAM）概念下的大多数飞机预计将是电动垂直起降（eVTOL）车辆类型，将在垂直机场外运行。虽然这类似于通用航空飞机和机场之间的关系，但在密集的城市环境中设想的垂直机场位置对管理垂直机场服务的空中交通提出了独特的挑战。随着定期着陆和起飞频率的增加，这一挑战变得更加明显。本文假设采用集中式空中交通管制员 (ATC) 来探索新的人工智能驱动的 ATC 方法的性能，以管理垂直起降场服务的 eVTOL。在这种情况下，最小分离驱动的安全性和延误是两个重要的考虑因素。 ATC 问题被建模为任务分配问题，并且由于通信中断（例如，链路质量差）和恶劣天气（例如，强阵风效应）导致的不确定性被添加为行动失败的小概率。为了学习垂直起降机场 ATC 策略，开发了一种新颖的基于图的强化学习 (RL) 解决方案，称为“城市空中交通-垂直起降时刻表管理 (UAM-VSM)”。该方法使用图卷积网络 (GCN) 将垂直起降场空间和 eVTOL 空间抽象为图，并为集中式 ATC 代理聚合信息以帮助概括环境。虚幻引擎与 Airsim 结合用作进行训练和测试的模拟环境。由于在这种实际模拟中进行 Mc 采样的成本很高，因此仅在测试期间考虑不确定性。与可行的随机决策基线和先到先服务 (FCFS) 基线相比，所提出的图 RL 方法在测试场景中表现出明显更好的性能，包括泛化到未见过的场景和不确定性的能力。
Majority of aircraft under the Urban Air Mobility (UAM) concept are expected to be of the electric vertical takeoff and landing (eVTOL) vehicle type, which will operate out of vertiports. While this is akin to the relationship between general aviation aircraft and airports, the conceived location of vertiports within dense urban environments presents unique challenges in managing the air traffic served by a vertiport. This challenge becomes pronounced within increasing frequency of scheduled landings and take-offs. This paper assumes a centralized air traffic controller (ATC) to explore the performance of a new AI driven ATC approach to manage the eVTOLs served by the vertiport. Minimum separation-driven safety and delays are the two important considerations in this case. The ATC problem is modeled as a task allocation problem, and uncertainties due to communication disruptions (e.g., poor link quality) and inclement weather (e.g., high gust effects) are added as a small probability of action failures. To learn the vertiport ATC policy, a novel graph-based reinforcement learning (RL) solution called"Urban Air Mobility- Vertiport Schedule Management (UAM-VSM)"is developed. This approach uses graph convolutional networks (GCNs) to abstract the vertiport space and eVTOL space as graphs, and aggregate information for a centralized ATC agent to help generalize the environment. Unreal Engine combined with Airsim is used as the simulation environment over which training and testing occurs. Uncertainties are considered only during testing, due to the high cost of Mc sampling over such realistic simulations. The proposed graph RL method demonstrates significantly better performance on the test scenarios when compared against a feasible random decision-making baseline and a first come first serve (FCFS) baseline, including the ability to generalize to unseen scenarios and with uncertainties.