| 物联网数据收集中无人机路径智能规划 |
| |
| 引用本文： | 付澍,杨祥月,张海君,陈晨,喻鹏,简鑫,刘敏.物联网数据收集中无人机路径智能规划[J].通信学报,2021(2):124-133. |
| |
| 作者姓名： | 付澍 杨祥月 张海君 陈晨 喻鹏 简鑫 刘敏 |
| |
| 作者单位： | 重庆大学微电子与通信工程学院;重庆大学信息物理社会可信服务计算教育部重点实验室;北京科技大学计算机与通信工程学院;北京邮电大学网络与交换技术国家重点实验室 |
| |
| 基金项目： | 国家自然科学基金资助项目(No.61701054);中央高校基本科研业务费专项资金资助项目(No.2020CDJQY-A001,No.2020CDJGFWDZ014)。 |
| |
| 摘 要： | 为解决无人机在数据收集过程中的路径规划问题,将其分为全局路径规划和局部路径规划。针对全局路径规划,将其建模为一个定向问题,定向问题是背包问题和旅行商问题2种经典优化问题的组合。采用指针网络深度学习对该模型进行求解,并在无人机能量约束下得到其服务节点集合及服务顺序。针对局部路径规划,基于无人机接收到节点的参考信号强度,通过深度Q网络学习对无人机局部飞行路径进行规划,使无人机逼近节点位置并服务各节点。仿真结果表明,所提方案能够在无人机能量约束下有效提升其数据收集的收益。
|
| 关 键 词： | 无人机 数据收集 路径规划 指针网络 深度Q网络 |
UAV path intelligent planning in IoT data collection |
| |
| FU Shu,YANG Xiangyue,ZHANG Haijun,CHEN Chen,YU Peng,JIAN Xin,LIU Min.UAV path intelligent planning in IoT data collection[J].Journal on Communications,2021(2):124-133. |
| |
| Authors: | FU Shu YANG Xiangyue ZHANG Haijun CHEN Chen YU Peng JIAN Xin LIU Min |
| |
| Affiliation: | (School of Microelectronics and Communication Engineering,Chongqing University,Chongqing 400030,China;Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education,Chongqing University,Chongqing 400030,China;School of Computer&Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China;State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China) |
| |
| Abstract: | To solve the problem of path planning of UAV data collection,it was generally be divided into global path planning and local path planning.For global path planning,it was modeled as an orientation problem,which was a combination of two classical optimization problems,the knapsack problem and the traveling salesman problem.The pointer network of deep learning was used to solve the model to obtain the service node set and service order under the energy constraint of the UAV.In terms of the local path planning,the reference signal strength(RSS)of the sensor node received by UAV was employed to learn the local flight path of UAV by deep Q network,which enabled the UAV to approach and serve the nodes.Simulation results show that the proposed scheme can effectively improve the revenue of UAV data collection under the energy constraint of UAV. |
| |
| Keywords: | unmanned aerial vehicle data collection path planning pointer network deep Q network |
| 本文献已被 维普 等数据库收录！ |
|
