计算机工程与应用2024，Vol.60Issue(20)：328-338,11.DOI:10.3778/j.issn.1002-8331.2306-0311
RISE-D3QN驱动的多无人机数据采集路径规划
RISE-D3QN-Based Path Planning for Multi-UAV Data Collection
摘要
Abstract
Unmanned aerial vehicles(UAVs)assisted Internet of things(IoT)data collection is an efficient and promising approach.The optimization of resource allocation in path planning is addressed in this paper by refining the energy con-sumption model and considering three metrics:the amount of collected data,time efficiency,and energy efficiency.The problem is formulated as a distributed partially observable Markov decision process(POMDP)and a novel deep reinforce-ment learning algorithm called RISE(Rényi state entropy)-D3QN(dueling double deep Q network)is proposed.It com-bines intrinsic rewards,prioritized experience replay,and soft-max exploration strategy,enabling path planning for UAV swarms while adapting to changes in UAV battery capacity,IoT device locations,data volume,and quantity.Simulation results demonstrate that compared to traditional D3QN and DQN algorithms,the proposed approach significantly increases.the amount of collected data from IoT devices while reducing UAV flight time and energy consumption,all while ensuring UAV safety during flight.关键词
无人机/路径规划/深度强化学习/多智能体/物联网/数据采集Key words
unmanned aerial vehicles(UAVs)/path planning/deep reinforcement learning/multi-agent/Internet of things(IoT)/data collection分类
信息技术与安全科学引用本文复制引用
黄泽丰,李涛..RISE-D3QN驱动的多无人机数据采集路径规划[J].计算机工程与应用,2024,60(20):328-338,11.基金项目
国家自然科学基金(62373195) （62373195）
江苏省"333工程"项目(BRA2020067). （BRA2020067）
