Research on UAV Path Planning Algorithm for Fairness Data Collection and Energy Supplement

GAO Si-hua; LI Jun-hui; LI Jian-fu; LIU Bao-yu

doi:10.12263/DZXB.20230299

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Research on UAV Path Planning Algorithm for Fairness Data Collection and Energy Supplement

PAPERS | 更新时间：2025-12-11

- Research on UAV Path Planning Algorithm for Fairness Data Collection and Energy Supplement
- ACTA ELECTRONICA SINICA Vol. 52, Issue 11, Pages: 3699-3710(2024)
- 作者机构：
  
  中国民航大学计算机科学与技术学院，天津 300300
- 作者简介：
- 基金信息：
  
  General Program of National Natural Science Foundation of China(62173332);The Fundamental Research Funds for the Central Universities(3122019118)
- DOI：10.12263/DZXB.20230299
  CLC： TP393
- Received：04 April 2023，
  
  Revised：2024-05-12，
  
  Published：25 November 2024
- 稿件说明：
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高思华, 李军辉, 李建伏, 等. 面向公平性数据采集和能量补充的无人机路径规划算法研究[J]. 电子学报, 2024, 52(11): 3699-3710.

GAO Si-hua, LI Jun-hui, LI Jian-fu, et al. Research on UAV Path Planning Algorithm for Fairness Data Collection and Energy Supplement[J]. Acta Electronica Sinica, 2024, 52(11): 3699-3710.
高思华, 李军辉, 李建伏, 等. 面向公平性数据采集和能量补充的无人机路径规划算法研究[J]. 电子学报, 2024, 52(11): 3699-3710. DOI：10.12263/DZXB.20230299

GAO Si-hua, LI Jun-hui, LI Jian-fu, et al. Research on UAV Path Planning Algorithm for Fairness Data Collection and Energy Supplement[J]. Acta Electronica Sinica, 2024, 52(11): 3699-3710. DOI：10.12263/DZXB.20230299

摘要

针对无人机（Unmanned Aerial Vehicle，UAV）辅助WSN（Wireless Sensor Networks）数据采集和能量补充工作中存在的数据来源单一和能量补充不均衡现象，本文首先提出数据采集和能量补充公平性问题并进行数学建模.其次，本文设计一种DPDQN（Double Parametrized Deep Q-Networks）强化学习算法，规划无人机的飞行路线和悬停位置，优化数据采集和能量补充效果.DPDQN学习离散动作与多种连续动作相混合的动作选择策略，算法网络模型包括离散动作网络和连续动作网络两部分.前者规划无人机访问数据采集节点的顺序，后者优化无人机在数据采集节点周围的悬停位置.仿真实验结果显示，本文算法在数据采集公平性、能量补充公平性、飞行距离和四种影响公平性的因素比较中均优于三种现有对比算法，并具有良好的鲁棒性和稳定性.

Abstract

UAV (Unmanned Aerial Vehicle)-assisted WSN (Wireless Sensor Networks) suffers from single-source data collection and uneven energy supplement. In this article

we first investigate and develop a mathematical model for the problem of fairness for data collection and energy supplement. Then

a novel deep reinforcement learning algorithm

named DPDQN (Double Parametrized Deep Q-Networks)

is designed to resolve the proposed problem. The DPDQN algorithm incorporates a hybrid discrete-continuous action strategy

which consists of two components

namely

discrete action network and continuous action network. The former schedules the UAV's visiting order to sensors in WSN

and the latter optimizes the UAV’s hover position around each visited sensor. Numerical results demonstrate that the DPDQN algorithm outperforms three existing solutions in data collection fairness

energy replenishment fairness

flying distance

and four factors that influence fairness. Furthermore

the results validate our algorithm is robust and stable.

关键词

Keywords

references

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