计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (9): 311-317.doi: 10.11896/jsjkx.220800032

• 计算机网络 • 上一篇    下一篇

基于边缘智能感知的无人机空间航迹规划方法

刘兴光, 周力, 张晓瀛, 陈海涛, 赵海涛, 魏急波   

  1. 国防科技大学电子科学学院 长沙 410073
  • 收稿日期:2022-08-02 修回日期:2022-10-10 出版日期:2023-09-15 发布日期:2023-09-01
  • 通讯作者: 周力(zhouli2035@nudt.edu.cn)
  • 作者简介:(liuxingguang20@nudt.edu.cn)
  • 基金资助:
    国家自然科学基金(62171449)

Edge Intelligent Sensing Based UAV Space Trajectory Planning Method

LIU Xingguang, ZHOU Li, ZHANG Xiaoying, CHEN Haitao, ZHAO Haitao, WEI Jibo   

  1. College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,China
  • Received:2022-08-02 Revised:2022-10-10 Online:2023-09-15 Published:2023-09-01
  • About author:LIU Xingguang,born in 1998,postgra-duate. His main research interests include radio environment map and mobile edge computing.
    ZHOU Li,born in 1988,Ph.D,postgra-duate supervisor.His main research interests include intelligent communication network,wireless resource mana-gement and edge computing.
  • Supported by:
    National Natural Science Foundation of China(62171449).

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摘要： 随着海量用频设备的涌现,无人机执行任务的电磁环境愈加复杂,对无人机认知环境和自主避障能力提出了更高的要求。鉴于此,提出了一种基于边缘智能感知的无人机空间航迹规划方法。首先,提出了一个基于边缘智能感知的无人机航迹规划框架,通过边缘服务器、传感器终端和无人机的协同通信与计算,提高无人机的环境感知和自主避障能力;其次,提出了一种基于深度确定性策略梯度(Deep Deterministic Policy Gradient,DDPG)算法优化的人工势场方法,避免无人机航迹规划陷入局部最小值点,同时行能耗;最后,在静态和动态干扰环境中对所提算法进行仿真实验,结果表明,与现有航迹规划方法相比,所提方法可以优化无人机的飞行航迹和传输数据速率,在静态和动态干扰环境中,无人机飞行能耗分别降低5.59%和11.99%,传输速率分别提高7.64%和16.52%,显著提高了无人机的通信稳定性和对复杂电磁环境的适应性。

关键词: 频谱地图, 移动边缘计算, 航迹规划, 人工势场, 深度强化学习

Abstract: With the emergence of a large number of frequency-using equipment,the radio environment for UAVs to perform tasks has become more and more complex,which puts forward higher requirements for UAVs to recognize the situation and autonomous obstacle avoidance.In view of this,this paper proposes a 3D trajectory planning method for UAVs based on side-end colla-boration.First,a UAV trajectory planning framework with side-end collaboration is proposed,which can synergistically improve the environment perception and autonomous obstacle avoidance capabilities of UAVs under communication connectivity constraints.Second,it proposes an artificial potential field method based on the deep deterministic policy gradient(DDPG) algorithm to avoid UAVs from falling into local minimum points and optimize UAV flight energy consumption.Finally,by performing simulation experiments in static and dynamic interference environments,compared with other trajectory planning methods,the proposed method can optimize the UAV flight trajectory and transmission data rate,which reduces the flight energy consumption of UAVs 5.59% and 11.99% respectively,and improve the transmission data rate 7.64% and 16.52% in static and dynamic interference environments.The proposed method also significantly improves the communication stability and the adaptability of UAVs to complex electromagnetic environments.

Key words: Radio environment map, Mobile edge computing, Trajectory planning, Artificial potential field, Deep reinforcement learning

中图分类号: 

  • TN919.4
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