北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2024, Vol. 47 ›› Issue (5): 115-121.

• 研究报告 • 上一篇    下一篇

面向林火监控的无人机位置部署策略研究

鲜永菊1,2,左维昊2,汪洲2,谭文光2   

  1. 1.
    2. 重庆邮电大学
  • 收稿日期:2023-09-04 修回日期:2023-12-08 出版日期:2024-10-28 发布日期:2024-11-10
  • 通讯作者: 左维昊 E-mail:zuowh1998@163.com
  • 基金资助:
    国家自然科学基金

Research on the Deployment Strategy of UAV Location for Forest Fire Monitoring

  • Received:2023-09-04 Revised:2023-12-08 Online:2024-10-28 Published:2024-11-10
  • Contact: Wei-Hao ZUO E-mail:zuowh1998@163.com

摘要: 将无人机应用于林火监控,可提高灭火和救援效率。然而,在森林火灾的复杂环境中,无人机的部署面临着能耗高、卸载效率低以及环境的动态变化等问题。因此研究了一种空地辅助的边缘计算框架,其中,无人机在火灾现场收集火灾现场数据并提供边缘计算服务,指挥中心提供计算能力较强的边缘计算服务。为了提供高效的计算服务,设计了一种基于多智能体强化学习的无人机位置部署方案,首先基于火灾蔓延速度和距离确定需要无人机提供计算服务的区域,然后设计一种基于多智能体强化学习的系统成本最小化的自主部署策略,以获得无人机在指定任务区域的最佳位置。最后仿真结果证明,所提方案可以有效降低无人机部署的总成本。

关键词: 无人机, 边缘计算, 位置部署, 多智能体强化学习

Abstract: The application of Unmanned Aerial Vehicles (UAVs) to forest fire monitoring can improve the efficiency of firefighting and rescue. However, in the complex environment of forest fires, the deployment of UAVs faces problems such as high energy consumption, low offloading efficiency, and dynamic changes in the environment. Therefore, an air-ground-assisted edge computing framework is investigated, in which the UAV collects fire scene data at the fire scene and provides edge computing services, and the command center provides edge computing services with high computational power. In order to provide efficient computing services, a UAV location deployment scheme based on multi-agent reinforcement learning is designed, which first determines the area that needs UAV to provide computing services based on the fire spreading speed and distance, and then designs an autonomous deployment strategy based on multi-agent reinforcement learning that minimizes the system cost to obtain the optimal location of the UAV in the designated task area. The final simulation results demonstrate that the proposed scheme can effectively reduce the total cost of UAV deployment.

Key words: unmanned aerial vehicle, edge computing, location deployment, multi-agent reinforcement learning

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