计算机技术与发展2026，Vol.36Issue(1)：1-7,16,8.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0214
基于Q学习的无人机自组网智能过滤路由算法
An Intelligent Filtering Routing Algorithm for Flying Ad-hoc Network Based on Q-learning
吴质彬 1谢钧 1骆西建1
作者信息
- 1. 中国人民解放军陆军工程大学 指挥控制工程学院,江苏 南京 210007
- 折叠
摘要
Abstract
Due to its dynamic topology and high mobility,flying ad hoc networks face challenges such as high routing time delay,unstable links and routing holes.Conventional routing protocols exhibit limited adaptability in complex dynamic environments,whereas reinforcement learning methodologies present innovative solutions to these issues.To solve these problems,an intelligent filtering routing algorithm based on Q-learning is proposed.UAV nodes perceive neighbor states by broadcasting Hello messages regularly,and construct state space based on link stability and neighbor topology information,employ a three-dimensional convex hull filtering mechanism to reduce the state space scale and mitigate Q-learning computational overhead.The multi-objective reward function of the algorithm com-prehensively considers the single-hop delay and link stability design to achieve multi-objective joint optimization.At the same time,the learning rate and discount factor are adaptively adjusted by combining topological information to enhance the environmental adaptability of the routing strategy.In addition,a HELLO message pre-learning mechanism is introduced,leveraging the maximum Q-values carried by neighboring nodes to accelerate Q-table convergence.Simulation results demonstrate that the proposed algorithm outperforms existing protocols such as QRF,QGeo,and GPSR in terms of packet delivery ratio and end-to-end delay,effectively improving routing performance in dynamically changing topological environments of flying ad-hoc networks.关键词
无人机自组网/路由协议/Q学习/强化学习/三维凸包Key words
flying ad hoc network/routing protocol/Q-learning/reinforcement learning/3D convex hull分类
信息技术与安全科学引用本文复制引用
吴质彬,谢钧,骆西建..基于Q学习的无人机自组网智能过滤路由算法[J].计算机技术与发展,2026,36(1):1-7,16,8.
