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无人机集群仿生行为控制研究进展及关键技术OA

Research Progress and Key Technologies of Bionic Behavior Control for UAV Swarms

中文摘要英文摘要

随着无人机集群面临的任务与环境日益复杂,传统控制方法在适应性、鲁棒性等方面的短板愈发凸显.将生物群体行为机制映射到无人机集群系统已成为重要研究方向,但现有研究缺乏对不同群体行为机制的提炼及相关技术的系统梳理.为此,系统综述无人机集群仿生行为控制的研究现状,重点围绕蜂群、狼群、鸟群三类典型对象展开.对三种典型仿生群体的内部结构及交互方式、行为机制进行阐述;分析无人机集群仿生行为控制的关键技术并总结各方法的优缺点及适用场景;针对无人机集群仿生行为控制适应性和灵活性的挑战,提出基于行为克隆的仿生行为控制策略.最后,总结并展望无人机集群仿生行为控制方法研究的未来发展趋势.

As the tasks and environments faced by unmanned aerial vehicle(UAV)swarms become increasingly complex,the shortcomings of traditional control methods in terms of adaptability and robustness have become more prominent.Mapping the behavioral mechanisms of biological groups to unmanned aerial vehicle(UAV)swarm systems has become an important research direction.However,existing research lacks the refinement of the behavioral mechanisms of different groups and the systematic review of related technologies.For this reason,this paper systematically reviews the current research status of bionic behavior control for UAVs,with a focus on three typical objects:bee colonies,wolf packs,and bird flocks.Firstly,the paper expounds the internal structure,interaction methods and behavioral mechanisms of three typical bionic groups.Secondly,it analyzes the key technologies of bionic behavior control for UAVs and summarizes the advantages and disadvantages of each method as well as their applicable scenarios.To address the challenges of adapt-ability and flexibility in bionic behavior control for unmanned aerial vehicle swarms,a bionic behavior control strategy based on behavior cloning is proposed.Finally,the paper summarizes and looks forward to the future development trends of the research on bionic behavior control methods for unmanned aerial vehicle swarms.

夏恒煜;何明;韩伟;吴晶晶;赵孝军

中国人民解放军陆军工程大学 指挥控制工程学院,南京 210007中国人民解放军陆军工程大学 指挥控制工程学院,南京 210007||近地面探测全国重点实验室,北京 100072中国人民解放军陆军工程大学 指挥控制工程学院,南京 210007中国人民解放军陆军工程大学 指挥控制工程学院,南京 210007中国人民解放军陆军工程大学 指挥控制工程学院,南京 210007

信息技术与安全科学

无人机集群群体行为仿生行为控制行为克隆

unmanned aerial vehicle(UAV)swarmgroup behaviorbionic behavior controlbehavioral cloning

《计算机工程与应用》 2026 (17)

1-17,17

国家自然科学基金(62273356)国家级人才项目(2022-JCJQ-ZQ-001)国家重点研发计划(2024YFF140140)启元国家实验室创新基金(2025-JCJQ-LA-001-101)江苏省重点研发计划(BE2023809).

10.3778/j.issn.1002-8331.2512-0142

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