基于复杂网络的城市低空飞行计划优化调度研究

  • 钟罡 ,
  • 华骏鸣 ,
  • 杜森 ,
  • 刘玉璞 ,
  • 刘皞 ,
  • 张洪海
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  • 1. 南京航空航天大学
    2. 南京航空航天大学,民航学院
    3. 南京航空航天大学 民航学院

收稿日期: 2024-11-01

  修回日期: 2025-01-08

  网络出版日期: 2025-01-10

基金资助

国家自然科学基金民航联合基金重点项目“面向融合运行的无人机驾驶员在环感知与避撞(DAA) 控制机理与效能研究”;中国博士后科学基金资助项目;中央高校基本科研业务费“复杂城市低空无人机飞行安全态势智能感知方法”;南京航空航天大学研究生创新计划项目

Research on Urban Low-Altitude Flight Plan Optimization and Scheduling Based on Complex Network Theory

  • ZHONG Gang ,
  • HUA Jun-Ming ,
  • DU Sen ,
  • LIU Yu-Pu ,
  • LIU Hao ,
  • ZHANG Hong-Hai
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Received date: 2024-11-01

  Revised date: 2025-01-08

  Online published: 2025-01-10

Supported by

National Natural Science Foundation of China;Project funded by China Postdoctoral Science Foundation;Fundamental Research Funds for the Central Universities

摘要

城市空中交通(Urban Air Mobility, UAM)发展面临着密集飞行活动带来的风险管理与效率提升问题,本文聚焦城市低空无人机集群的飞行前阶段,提出了基于复杂网络的飞行计划两阶段优化调度方法。首先,基于数字空域栅格环境下无人机飞行潜在的第三方风险,预先规划单机的四维航迹并生成无人机集群初始飞行计划;其次,提出飞行不确定性影响下的冲突探测模型,构建以飞行计划为节点、以飞行冲突为连边的冲突复杂网络,通过提取计算冲突复杂网络拓扑指标来识别关键飞行计划;最后,建立了融合地面等待、速度调整和局部改航等多种策略的飞行计划优化调度模型,设计了先整体优化关键飞行计划,再局部调整剩余冲突点的两阶段优化求解算法框架,并基于改进的FATA算法实现模型求解。实验结果表明,该方法能够在考虑飞行不确定性的情况下显著减少甚至完全消除飞行冲突,同时可以保证在飞行计划调度过程中造成延误的飞行计划数量不超过总数量的3%,并保持增加的总运行风险比例不超过1%。研究成果能够为城市低空飞行计划调度管理提供技术支撑,对于推动城市空中交通安全有序发展具有指导意义。

本文引用格式

钟罡 , 华骏鸣 , 杜森 , 刘玉璞 , 刘皞 , 张洪海 . 基于复杂网络的城市低空飞行计划优化调度研究[J]. 航空学报, 0 : 1 -0 . DOI: 10.7527/S1000-6893.2025.31479

Abstract

With the development of the low-altitude economy, Urban Air Mobility (UAM) confronts challenges in risk manage-ment and efficiency due to dense flight operations. This study addresses the pre-flight phase of urban low-altitude Unmanned Aerial Vehicle (UAV) swarms and introduces a two-stage optimal scheduling approach for flight plans utilizing complex network. Initially, the four-dimensional trajectory for individual UAVs is pre-planned within a digital airspace grid environment, accounting for potential third-party risks, to generate an initial four-dimensional flight plan for the UAV swarm. Subsequently, a conflict detection model is proposed to address flight uncertainty, con-structing a complex network where flight plans are nodes and conflicts are edges. Important flight plans are identi-fied by analyzing topological metrics of the complex network. Finally, an integrated flight plan optimization model is established, incorporating strategies such as ground holding, speed adjustments, and re-route. A two-stage optimi-zation algorithm frame is developed to globally optimize key flight plans and then locally adjust remaining conflicts, and is solved based on the improved FATA algorithm. Experimental results demonstrate that this method can signifi-cantly reduce or eliminate flight conflicts considering flight uncertainty, ensuring that delayed flight plans constitute no more than 3% of the total and increasing operational risk by no more than 1%. These findings offer technical support for urban low-altitude flight plan scheduling management and are instrumental in fostering the safe and orderly progression of urban air traffic
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