城市低空空域无人机集中-分散混合交通控制框架

IF 14.5 Q1 TRANSPORTATION
Xiangdong Chen , Shen Li , Meng Li
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引用次数: 0

摘要

城市空中交通(UAM)代表了一种通过从二维(2D)到三维(3D)交通系统过渡来缓解地面拥堵的变革性方法。UAM被设想为一种安全、可持续和高效的城市交通模式,它利用空中空间来减少对传统道路基础设施的依赖,同时解决城市交通中的交通拥堵挑战。然而,航空运输需求的快速增长,加上在三维空域管理大规模无人机(UAV)操作的复杂性,对传统交通管理系统的有效性提出了挑战。为了应对这些挑战,本研究提出了一种集成集中式和分散式方法的无人机空中交通管制混合框架。在低空空域建模三维空中交通网络,捕获详细的二维和三维冲突关系。引入了“虚拟飞行容器”(VFC)的概念来调节无人机的时空轨迹,确保无冲突、低延迟的操作,同时最小化实时计算需求,特别是在高需求时。该问题采用双层优化方法解决:上层着重解决交通分配问题,考虑航路容量约束;下层设计时空轨迹,保证无冲突运行,提高交通效率,与交通管制方案形成互补。数值实验验证了该框架的有效性,证明了该框架在提高网络传输效率和吞吐量方面的有效性。提供了关于网络结构的作用、起飞和降落点的位置以及优化UAM操作的控制参数的关键见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid centralized-decentralized traffic control framework for unmanned aerial vehicles in urban low-altitude airspace
Urban air mobility (UAM) represents a transformative approach to alleviating ground-level congestion by transitioning from two-dimension (2D) to three-dimension (3D) transportation systems. Envisioned as a safe, sustainable, and efficient mode of urban transit, UAM leverages aerial space to reduce dependence on traditional road infrastructure while addressing traffic congestion challenges in urban mobility. However, the rapid growth in aerospace transportation demand, coupled with the complexity of managing large-scale unmanned aerial vehicle (UAV) operations in 3D airspace, challenges the effectiveness of traditional traffic management systems. To address these challenges, this study proposes a hybrid framework for UAV air traffic control that integrates centralized and decentralized approaches. A 3D air traffic network is modeled in low-altitude airspace, capturing detailed 2D and 3D conflict relationships. The concept of a “virtual flight container” (VFC) is introduced to regulate UAV space–time trajectories, ensuring conflict-free, low-delay operations while minimizing real-time computational requirements, especially in high demands. The problem is addressed using a bi-level optimization approach: The upper level focuses on solving the traffic assignment problem, considering airway capacity constraints, while the lower level designs space–time trajectories to ensure conflict-free operations and enhance traffic efficiency, thereby complementing the traffic control scheme. Numerical experiments validate the proposed framework, highlighting its effectiveness in improving traffic efficiency and network throughput. Key insights are provided regarding the role of network structure, the placement of take-off and landing points, and control parameters in optimizing UAM operations.
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CiteScore
15.20
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