首页 | 本学科首页   官方微博 | 高级检索  
     

面向物联网的深度Q网络无人机路径规划
引用本文:张建行, 康凯, 钱骅, 杨淼. 面向物联网的深度Q网络无人机路径规划[J]. 电子与信息学报, 2022, 44(11): 3850-3857. doi: 10.11999/JEIT210962
作者姓名:张建行  康凯  钱骅  杨淼
作者单位:1.中国科学院上海高等研究院 上海 201210;2.中国科学院大学 北京 100049;3.上海科技大学信息科学与技术学院 上海 201210
基金项目:国家重点研发计划(2020YFB2205603),国家自然科学基金(61971286),上海市科技创新行动计划(19DZ1204300)
摘    要:随着无人机技术的广泛应用,基于无人机辅助数据收集的物联网架构扩展了物联网的应用范围,尤其适用于军事战场、灾害救援等极端场景。针对上述场景,该文提出一种基于深度Q网络(Deep Q-Network, DQN)框架的无人机飞行路径规划算法。该算法以无人机飞行周期内收集信息的平均信息年龄(Age of Information, AoI)为优化目标,来保证无人机收集数据的时效性。仿真结果表明,所提算法可以有效降低无人机单个飞行周期内收集数据的平均AoI。与随机算法、基于最大AoI的贪心算法、最短路径算法以及基于AoI的路径规划算法(AoI-based Trajectory Planning, ATP)相比,平均AoI分别降低了约81%, 67%, 56%和39%。该研究实现了无人机辅助物联网系统中,数据的高效、低时延采集。

关 键 词:无人机   物联网   信息年龄   路径规划   深度Q网络
收稿时间:2021-09-09
修稿时间:2021-11-05

UAV Trajectory Planning Based on Deep Q-Networkfor Internet of Things
ZHANG Jianhang, KANG Kai, QIAN Hua, YANG Miao. UAV Trajectory Planning Based on Deep Q-Networkfor Internet of Things[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3850-3857. doi: 10.11999/JEIT210962
Authors:ZHANG Jianhang  KANG Kai  QIAN Hua  YANG Miao
Affiliation:1. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
Abstract:With the wide application of Unmanned Aerial Vehicle (UAV), the UAV-assisted Internet of Things (IoT) data collection architecture has expanded IoT’s application scope, which is especially suitable for extreme scenarios like military battlefields or disaster rescue. This paper proposes a UAV trajectory planning algorithm based on Deep Q-Network (DQN) framework for the above scenarios. The proposed algorithm takes the Age of Information (AoI) of collected data in a UAV’s flight cycle as the optimization goal to maintain data freshness. The simulation results show that this algorithm can effectively reduce the average AoI of the collected data. Compared with the random algorithm, the greedy algorithm based on the maximum AoI, the shortest path algorithm and the AoI-based Trajectory Planning (ATP) algorithm, the proposed algorithm can reduce AoI by about 81%, 67%, 56% and 39%, respectively. This paper has realized the efficient and low-latency data collection in the UAV-assisted IoT system.
Keywords:Unmanned Aerial Vehicle (UAV)  Internet of Things (IoT)  Age of Information (AoI)  Trajectory planning  Deep Q-Network (DQN)
点击此处可从《电子与信息学报》浏览原始摘要信息
点击此处可从《电子与信息学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号