考虑通信拓扑控制的FANET实时任务调度算法

  • 王沛曌 ,
  • 何明 ,
  • 陈海华 ,
  • 王鸿鹏
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  • 南开大学

收稿日期: 2025-07-31

  修回日期: 2025-10-28

  网络出版日期: 2025-10-30

基金资助

国家自然科学基金项目

Real-time task scheduling algorithm for FANET considering communication topology control

  • WANG Pei-Zhao ,
  • HE Ming ,
  • CHEN Hai-Hua ,
  • WANG Hong-Peng
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Received date: 2025-07-31

  Revised date: 2025-10-28

  Online published: 2025-10-30

摘要

多无人机飞行自组网(Flying Ad hoc Network, FANET)凭借强大的态势感知和环境适应能力,能够在各类具有实时监测需求的应用场景中有效保障实时地面任务搜索、分配和执行。基于上述应用场景和任务需求,重点解决FANET任务自主分配阶段的实时任务调度与动态拓扑控制的协同优化难题。实时任务调度保障监测任务的时效性,动态拓扑控制确保任务和定位数据的稳定回传。首先,构建基于动态价值评估的拍卖机制与拓扑控制联合优化框架。其次,通过引入动态任务价值评估模型,实现动态的任务优先级排序与分配,同时设计基于最小维护代价的拓扑控制策略,通过选择性链路维护机制降低拓扑调整成本。最后,复杂度分析表明所提算法能够满足动态场景中的高效求解和实时优化需求,仿真实验表明所提算法在任务响应速度和拓扑稳定性方面具有显著优势,为具有即时响应和精准监测等工程应用需求的场景提供了理论支撑和技术参考。

本文引用格式

王沛曌 , 何明 , 陈海华 , 王鸿鹏 . 考虑通信拓扑控制的FANET实时任务调度算法[J]. 航空学报, 0 : 1 -0 . DOI: 10.7527/S1000-6893.2025.32636

Abstract

The multi-UAV flying ad hoc network (FANET) with its enhanced situational awareness and environmental adaptability can effectively ensure real-time ground task search, allocation and execution in various scenarios with real-time monitoring requirements. Given these application scenarios and task requirements, the key issue of collaborative optimization of real-time task scheduling and dynamic topology control during autonomous allocation in FANET is addressed: the real-time task scheduling ensures the timeliness requirements of monitoring tasks, and the dynamic topology control ensures the stable transmission of tasks and location data. Firstly, a joint optimization framework integrating auction mechanism and topology control based on dynamic value assessment is constructed. Secondly, through the introduction of a dynamic task value assessment model, dynamic task priority ranking and allocation are realized, and a topology control strategy based on minimum maintenance cost is designed. The cost of topology adjustment is reduced via a selective link maintenance mechanism. Finally, the complexity analysis demonstrates that the proposed algorithm can meet the requirements for efficient solution and real-time optimization in dynamic scenarios. The significant advantages of the proposed algorithm in terms of task response speed and topology stability are demonstrated through simulation experiments. This work provides both theoretical support and technical references for engineering scenarios requiring immediate response and precise monitoring.

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