UAV path intelligent planning in IoT data collection

Shu FU; Xiangyue YANG; Haijun ZHANG; Chen CHEN; Peng YU; Xin JIAN; Min LIU

doi:10.11959/j.issn.1000-436x.2021036

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UAV path intelligent planning in IoT data collection

Papers | 更新时间：2024-06-05

- UAV path intelligent planning in IoT data collection
- Journal on Communications Vol. 42, Issue 2, Pages: 124-133(2021)
- 作者机构：
  
  1. 重庆大学微电子与通信工程学院，重庆 400030
  2. 重庆大学信息物理社会可信服务计算教育部重点实验室，重庆 400030
  3. 北京科技大学计算机与通信工程学院，北京 100083
  4. 北京邮电大学网络与交换技术国家重点实验室，北京 100876
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61701054);The Fundamental Research Funds for the Central Universities(2020CDJQY-A001);The Fundamental Research Funds for the Central Universities(2020CDJGFWDZ014)
- DOI：10.11959/j.issn.1000-436x.2021036
  CLC： TN92
- Online First：2021-02，
  
  Published：25 February 2021
- 稿件说明：
移动端阅览
Shu FU, Xiangyue YANG, Haijun ZHANG, et al. UAV path intelligent planning in IoT data collection[J]. Journal on Communications, 2021, 42(2): 124-133.
DOI：

Shu FU, Xiangyue YANG, Haijun ZHANG, et al. UAV path intelligent planning in IoT data collection[J]. Journal on Communications, 2021, 42(2): 124-133. DOI： 10.11959/j.issn.1000-436x.2021036.

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。

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.

关键词

Keywords

references

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