面向协同感知的无人蜂群智能资源调度方案

赵鹏程; 李天扬; 冷甦鹏; 熊凯

doi:10.11959/j.issn.1000-0801.2025050

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面向协同感知的无人蜂群智能资源调度方案

专题：低空智联网与低空安全 | 更新时间：2025-04-24

- 面向协同感知的无人蜂群智能资源调度方案
- Intelligent resource scheduling scheme for UAV swarm collaborative sensing
- 电信科学 2025年41卷第3期页码：17-26
- 作者机构：
  
  电子科技大学信息与通信工程学院，四川成都 611731
- 作者简介：
  
  [ "赵鹏程（1999- ），男，电子科技大学信息与通信工程学院博士生，主要研究方向为无人蜂群协同感知、资源分配、机器学习等。" ]
  [ "李天扬（1997- ），女，电子科技大学信息与通信工程学院博士生，主要研究方向为无线通信网络、无人机自组网、多智能体强化学习、数字孪生、网络演算、空中计算等。" ]
  [ "冷甦鹏（1973- ），男，电子科技大学信息与通信工程学院教授、博士生导师，主要研究方向为物联网、车联网、新一代宽带无线网络、无线自组织网络、智能交通信息系统的资源管理、介质访问控制、路由、组网与互联、智能算法理论及技术应用等。" ]
  [ "熊凯（1991- ），男，电子科技大学信息与通信工程学院副研究员，主要研究方向为无人机编队资源分配、移动边缘计算和机器学习。" ]
- 基金信息：
  
  国家自然科学基金青年项目(62201122);四川省科技计划项目(2024YFHZ0321)
- DOI：10.11959/j.issn.1000-0801.2025050
  中图分类号： TP393
- 收稿：2024-12-01，
  
  修回：2025-02-19，
  
  纸质出版：2025-03-20
- 稿件说明：
移动端阅览
赵鹏程,李天扬,冷甦鹏等.面向协同感知的无人蜂群智能资源调度方案[J].电信科学,2025,41(03):17-26.

ZHAO Pengcheng,LI Tianyang,LENG Supeng,et al.Intelligent resource scheduling scheme for UAV swarm collaborative sensing[J].Telecommunications Science,2025,41(03):17-26.
赵鹏程,李天扬,冷甦鹏等.面向协同感知的无人蜂群智能资源调度方案[J].电信科学,2025,41(03):17-26. DOI： 10.11959/j.issn.1000-0801.2025050.

ZHAO Pengcheng,LI Tianyang,LENG Supeng,et al.Intelligent resource scheduling scheme for UAV swarm collaborative sensing[J].Telecommunications Science,2025,41(03):17-26. DOI： 10.11959/j.issn.1000-0801.2025050.

摘要

随着低空经济的蓬勃发展，无人机在监测和感知领域得到广泛应用。然而，无人机有限的机载计算资源制约了感知数据的高效处理。此外，协同感知中产生的观测区域重叠进一步增加了冗余的计算负担。同时，无人机网络的高动态拓扑和节点资源的波动性大幅加剧了资源协同的难度。针对上述挑战，提出了一种面向协同感知的无人蜂群智能资源调度方案，通过自适应感知模式、分步卸载计算任务和竞价带宽策略，实现了通信、感知、计算（通感算）异构资源协同互补，提升协同感知效率。采用基于注意力机制的多智能体强化学习算法求解优化问题，以增强智能体提取环境关键特征的能力。仿真结果表明，与基准方案相比，该方案不仅有效降低了感知任务的执行时间，还提高了计算资源的利用率。

Abstract

With the rapid development of the low-altitude economy

unmanned aerial vehicles (UAV) have been widely applied in monitoring and sensing tasks. However

the limited onboard computing resources of UAV constrain the efficient processing of sensing data. Moreover

overlapping observation areas in collaborative sensing introduce additional computational redundancy. Meanwhile

the highly dynamic network topology and fluctuating node resources significantly increase the complexity of resource coordination. To address these challenges

an intelligent resource scheduling scheme for UAV swarm collaborative sensing was proposed. Adaptive sensing mode selection

stepwise computation offloading

and competitive bandwidth allocation were integrated to achieve heterogeneous resource coordination across communication

sensing

and computation (CSC)

thereby enhancing collaborative sensing efficiency. Furthermore

a multi-agent reinforcement learning (MARL) algorithm with an attention mechanism was employed to solve the optimization problem

enabling agents to extract critical environmental features more effectively. Simulation results demonstrate that

compared with benchmark schemes

the proposed scheme significantly reduces the execution time of sensing tasks while improving computational resource utilization.

关键词

Keywords

references

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