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无人机集群联合拓扑控制的智能路由规划方法

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针对现有无人机集群路由协议拓扑适变能力弱,易产生包重传、能量空洞和高时延,严重恶化了数据路由性能的问题,针对无人机集群中集群拓扑与路由的耦合特性,提出了一种联合拓扑控制的智能路由规划(IRPJTC)方法.该方法由基于虚拟力的自适应拓扑控制(VFATC)和基于近端策略优化的地理路由规划(PPO-GRP)组成.其中,VFATC 使各无人机根据邻居运动状态信息自适应调整与邻居的距离,保证集群中链路的稳定连接;进一步,PPO-GRP引入VFATC中的链路稳定性指标,并结合端到端时延与能耗指标,设计多目标奖励函数,采用深度强化学习中的近端策略优化算法训练路由策略.仿真实验结果表明,IRPJTC 相比于现有路由方法,能在保证分组传输成功率的同时,使端到端时延降低12.11%,无人机集群能耗降低4.56%,且具备更强的能耗均衡能力.
Intelligent route planning method with jointing topology control of UAV swarm
Existing routing protocols without awareness of the topology causes excessive retransmissions,energy holes,and long delay,data routing performance was seriously deteriorated.Considering the relation of topology and routing,an intelligent route planning with jointing topology control(IRPJTC)method was proposed.IRPJTC consisted of two part,the virtual force-based adaptive topology control(VFATC),and the PPO-based geographic routing protocol(PPO-GRP).Based on neighbor's mobility information,the distance between UAVs was adaptively adjusted by VFATC to provide stable links between UAVs.Combined with link stability metric in VFATC,end-to-end delay and energy consumption,a multi-objective reward function was designed by PPO-GRP to train optimal routing strategy.According to the perfor-mance study,the proposed IRPJTC reduces existing routing protocols by 12.11%of end-to-end delay,and 4.56%of en-ergy consumption,and has a better energy balance ability.

UAV swarmrouting protocoltopology controlproximal policy optimizationdeep reinforcement learning

颜志、易正伦、欧阳博、王耀南

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湖南大学电气与信息工程学院,湖南 长沙 410082

无人机集群 路由协议 拓扑控制 近端策略优化 深度强化学习

国家自然科学基金资助项目湖南省科技重大专项基金资助项目网络与交换技术国家重点实验室(北京邮电大学)开放课题基金资助项目

622935112021GK1010SKLNST-2021-2-03

2024

通信学报
中国通信学会

通信学报

CSTPCD北大核心
影响因子:1.265
ISSN:1000-436X
年,卷(期):2024.45(2)
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