Connotation,Development and Challenges of Reliability Technology of Intelligent UAV Swarm

doi:10.12382/bgxb.2024.0322

Abstract

Abstract:

With the rapid development of unmanned aerial vehicle (UAV)-related technologies,the intelligent UAV swarms have attracted more and more research attention.The organizational algorithm models of natural swarms are used for the intelligent UAV swarms,and combined with other intelligent technologies to form advanced group intelligence behaviors,which have the advantages that traditional single artificial intelligent agents cannot match.However,the issue of reliability is also an important factor that restricts its large-scale application.The concept,characteristics,and technological development path of intelligent UAV swarms are systematically analyzed.Based on the application scenarios,application modes,and behavioral processes of intelligent UAV swarms,the connotation of UAV swarm reliability is discussed.A technological framework for the reliability of UAV swarms is constructed,and the technical approaches and suggestions for the development of UAV swarm reliability technology are given based on the technological characteristics and application requirements of intelligent UAV swarms.Under the reliability connotation of “one core, two capabilities, and N foundations,” a reliability technology framework for intelligent UAVs has been proposed,which consists of reliability metrics,reliability development and verification,and reliability maintenance and support technologies.

Key words: unmanned aerial vehicle swarm, artificial intelligence, swarm intelligence, reliability

CLC Number:

V279

ZENG Zhaoyang, PENG Wensheng, LI Yunkai, XU Ming. Connotation,Development and Challenges of Reliability Technology of Intelligent UAV Swarm[J]. Acta Armamentarii, 2025, 46(3): 240322-.

Figures/Tables 13

Fig.1 UAV swarm

Fig.2 Military UAV swarm

Fig.3 Centralized control

Fig.4 Decentralized control

Table 1 Reliability issues faced by key technologies of intelligent UAV swarms

智能UAVS关键技术	可靠性问题
自主控制算法	算法复杂度、易受攻击、资源受限等
异构无人机协同	协同效率、可解释性、软硬件兼容问题
网络通信技术	不稳定的信号、易受攻击
分布式数据融合算法	数据丢失、算法扩展性
大数据技术	数据丢失、软硬件兼容问题等
机器学习技术	泛化能力、可解释性问题

Fig.5 Intelligent UAV swarm system composition

Fig.6 Typical mission profile of intelligent UAV swarm

Fig.7 Factors influencing the reliability of intelligent UAV swarm

Fig.8 The connotation of reliability of intelligent UAV swarm

Fig.9 Technical framework for the reliability of intelligent UAV swarm

Table 2 Reliability index system

体系层次	可靠性指标需求
任务完成	任务成功率
功能实现	协同可靠性、自组织适应性、智能决策准确性、自主恢复效率
结构组成	硬件可靠性、软件可靠性、算法鲁棒性、数据质量和安全性、通信可靠性等

Table 3 Reliability parameter model and function

模型类别	可靠性参数模型	模型功能	相关研究
集群行为模型类	协同度模型	用于评估通信效率、任务完成率等指标	文献[86]提出的UAVS的协同度模型,其中包括协同时间基本模型、协同信息量模型,以及基于多Agent的协同方法协同性能模型。该文提出的协同时间T为 T=T₁+T₂+T₃+T₄+T₅ 式中:T₁,…,T₅为一次协同过程的感知、决策与执行相关时间。
	自组织能力评估模型	用于考察集群对环境变化的响应速度	文献[87]提出的UAVS重新集结的指标体系。其中提出了UAVS集结并协调一致时间的计算指标T_c。第i个无人机的预计到达时间为 $T E T A i$ ,将整个编队的集结时间T_e与 $T E T A i$ 的差记为协调一致时间误差: E_i(t)=T_e- $T E T A i$ (t) 当n个无人机的协调一致时间误差的平方和等于δ时,t=T_c。
	智能决策质量模型	用于衡量指挥主体根据战场态势完成目标分配、战略决策和战术决策的能力	文献[88]提出的模型,其指挥决策系统能力E_DEC可以用态势感知能力、战术决策能力、指挥控制能力和系统生存能力表示: E_DEC=λ_gz·C_gz+λ₄·P_t+λ_s·C_s+λ_jc·C_jc 式中:C_gz表示态势感知能力;P_t为指挥控制时效性;C_s表示系统生存能力;C_jc为战术决策能力:λ_gz、λ_t、λ_s、λ_gc为权重系数。
集群恢复模型类	自主恢复效率模型	用于分析集群在故障时的重新配置速度	文献[87]提出的整个无人机编队的可靠度,以体现其自主恢复效率: $P = ∑ i = 1 n P i / n$ 式中:Pⁱ为第i个无人机的可靠度,需要通过第i个无人机飞行时间和平均故障间隔时间来计算;整个无人机编队共有n个无人机。

Table 3 Reliability parameter model and function

模型类别	可靠性参数模型	模型功能	相关研究
集群行为模型类	协同度模型	用于评估通信效率、任务完成率等指标	文献[86]提出的UAVS的协同度模型,其中包括协同时间基本模型、协同信息量模型,以及基于多Agent的协同方法协同性能模型。该文提出的协同时间T为 T=T₁+T₂+T₃+T₄+T₅ 式中:T₁,…,T₅为一次协同过程的感知、决策与执行相关时间。
	自组织能力评估模型	用于考察集群对环境变化的响应速度	文献[87]提出的UAVS重新集结的指标体系。其中提出了UAVS集结并协调一致时间的计算指标T_c。第i个无人机的预计到达时间为 $T E T A i$ ,将整个编队的集结时间T_e与 $T E T A i$ 的差记为协调一致时间误差: E_i(t)=T_e- $T E T A i$ (t) 当n个无人机的协调一致时间误差的平方和等于δ时,t=T_c。
	智能决策质量模型	用于衡量指挥主体根据战场态势完成目标分配、战略决策和战术决策的能力	文献[88]提出的模型,其指挥决策系统能力E_DEC可以用态势感知能力、战术决策能力、指挥控制能力和系统生存能力表示: E_DEC=λ_gz·C_gz+λ₄·P_t+λ_s·C_s+λ_jc·C_jc 式中:C_gz表示态势感知能力;P_t为指挥控制时效性;C_s表示系统生存能力;C_jc为战术决策能力:λ_gz、λ_t、λ_s、λ_gc为权重系数。
集群恢复模型类	自主恢复效率模型	用于分析集群在故障时的重新配置速度	文献[87]提出的整个无人机编队的可靠度,以体现其自主恢复效率: $P = ∑ i = 1 n P i / n$ 式中:Pⁱ为第i个无人机的可靠度,需要通过第i个无人机飞行时间和平均故障间隔时间来计算;整个无人机编队共有n个无人机。

Table 4 Reliability maintenance support technology and classification

技术分类	可靠性维持保障技术
故障自主隔离技术	分布式故障定位
	智能故障检测与诊断
	自适应性故障隔离
自适应自恢复技术	动态任务重新分配
	自组织网络拓扑重构
	预测性维护与自修复
	环境适应性飞行控制
	冗余系统设计与切换

References 94

[1]	李军, 陈士超. 无人机蜂群关键技术发展综述[J]. 兵工学报, 2023, 44(9):2533-2545. doi: 10.12382/bgxb.2023.0514
	LI J, CHEN S C. Overview of key technology and its development of drone swarm[J]. Acta Armamentarii, 2023, 44(9):2533-2545. (in Chinese) doi: 10.12382/bgxb.2023.0514
[2]	贾永楠, 田似营, 李擎. 无人机集群研究进展综述[J]. 航空学报, 2020, 41(增刊1):723738.
	JIA Y N, TIAN S Y, LI Q. Recent development of unmanned aerial vehicle swarms[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(S1):723738. (in Chinese) doi: 10.7527/S1000-6893.2019.23738
[3]	邹立岩, 张明智, 荣明. 智能无人机集群概念及主要发展趋势分析[J]. 战术导弹技术, 2019(5):1-11,43.
	ZOU L Y, ZHANG M Z, RONG M. Analysis of intelligent unmanned aircraft systems swarm concept and main development trend[J]. Tactical Missile Technology, 2019(5):1-11,43. (in Chinese)
[4]	焦士俊, 王冰切, 刘剑豪, 等. 国内外无人机集群研究现状综述[J]. 航天电子对抗, 2019, 35(1):61-64.
	JIAO S J, WANG B Q, LIU J H, et al. Review of drone swarm research at home and abroad[J]. Aerospace Electronic Warfare, 2019, 35(1):61-64. (in Chinese)
[5]	牛轶峰, 肖湘江, 柯冠岩. 无人机集群作战概念及关键技术分析[J]. 国防科技, 2013, 34(5):37-43.
	NIU Y F, XIAO X J, KE G Y. Operation concept and key techniques of unmanned aerial vehicle swarms[J]. Defense Science and Technology, 2013, 34(5):37-43. (in Chinese)
[6]	许彪, 张宇, 王超. 美军无人系统蜂群技术发展现状与趋势分析[J]. 飞航导弹, 2018(3):36-39.
	XU B, ZHANG Y, WANG C. Development status and trend analysis of swarm technology in US military unmanned systems[J]. Flight Dynamics, 2018(3):36-39. (in Chinese)
[7]	申超, 武坤琳, 宋怡然. 无人机蜂群作战发展重点动态[J]. 飞航导弹, 2016(11):28-33.
	SHEN C, WU K L, SONG Y R. Dynamic analysis of key development focuses in UAV swarm operations[J]. Flight Dynamics, 2016(11):28-33. (in Chinese)
[8]	Department of Defense. Unmanned systems integrated roadmap FY2011-2036[R]. Washington,D.C.,US:DoD, 2011.
[9]	Department of Defense. Unmanned systems integrated roadmap FY2017-2042[R]. Washington,D.C.,US:DoD, 2017.
[10]	蒋罗婷. 当代反无人机系统技术综述[J]. 电子质量, 2023(2):96-100.
	JIANG L T. Review of modern counter drone systems technology[J]. Electronic Quality, 2023(2):96-100. (in Chinese)
[11]	BENI G, WANG J. Swarm intelligence in cellular robotic systems[J]. Robots and Biological Systems:Towards a New Bionics, 1993(102):703-712.
[12]	KENNEDY J. Particle swarm optimization[C]//Proceedings of ICNN’95-International Conference on Neural Networks.Perth,WA, Australia:IEEE, 1995, 4(8):1942-1948.
[13]	KENNEDY J, EBERHART R C, SHI Y. Swarm intelligence[M]. San Francisco,CA, US: Morgan Kaufmann Publishers, 2001.
[14]	KENNEDY J, EBERHART R C. Swarm intelligence[M]. San Diego,CA, US: Academic Press, 2001.
[15]	宫远强, 张业鹏, 马万鹏, 等. 无人机蜂群中的群体智能涌现机理[J]. 兵工学报, 2023, 44(9):2661-2671. doi: 10.12382/bgxb.2022.1181
	GONG Y Q, ZHANG Y P, MA W P, et al. Mechanisms of group intelligence emergence in UAV swarms[J]. Acta Armamentarii, 2023, 44(9):2661-2671. (in Chinese) doi: 10.12382/bgxb.2022.1181
[16]	王玫, 朱云龙, 何小贤. 群体智能研究综述[J]. 计算机工程, 2005, 31(22):194-196.
	WANG M, ZHU Y L, HE X X. A survey of swarm intelligence[J]. Computer Engineering, 2005, 31(22):194-196. (in Chinese)
[17]	高岳林, 杨钦文, 王晓峰, 等. 新型群体智能优化算法综述[J]. 郑州大学学报(工学版), 2022, 43(3):21-30.
	GAO Y L, YANG Q W, WANG X F, et al. Overview of new swarm intelligent optimization algorithms[J]. Journal of Zhengzhou University (Engineering Edition), 2022, 43(3):21-30. (in Chinese)
[18]	赵健, 张鑫褆, 李佳明, 等. 群体智能2.0研究综述[J]. 计算机工程, 2019, 45(12):1-7. doi: 10.19678/j.issn.1000-3428.0056410
	ZHAO J, ZHANG X T, LI J M, et al. Research review of crowd intelligence 2.0[J]. Computer Engineering, 2019, 45(12):1-7. (in Chinese) doi: 10.19678/j.issn.1000-3428.0056410
[19]	王健, 杨渡佳, 黄科举, 等. 认知电子战发展趋势:从单体智能到群体智能[J]. 信息对抗技术, 2023, 2(4):151-170.
	WANG J, YANG D J, HUANG K J, et al. Developing trend of cognitive electronic warfare: from single-agent intelligence to multi-agent intelligence[J]. Information Countermeasure Technology, 2023, 2(4):151-170. (in Chinese)
[20]	梁晓龙, 孙强, 尹忠海, 等. 大规模无人系统集群智能控制方法综述[J]. 计算机应用研究, 2015, 32(1):11-16.
	LIANG X L, SUN Q, YIN Z H, et al. Review on large-scale unmanned system swarm intelligence control method[J]. Journal of Computer Applications Research, 2015, 32(1):11-16. (in Chinese)
[21]	付巍巍. 集群智能算法的改进与应用研究[D]. 大连: 大连理工大学, 2021.
	FU W W. Research on improvement and Applications of Swarm intelligentAlgorithms[D]. Dalian: Dalian University of Technology, 2021. (in Chinese)
[22]	蒲汛. 群集智能及其在分布式系统中的应用研究[D]. 成都: 电子科技大学, 2012.
	PU X. Swarm intelligence and its applications in distributed systems[D]. Chengdu: University of Electronic Science and Technology of China, 2012. (in Chinese)
[23]	王伟嘉, 郑雅婷, 林国政, 等. 集群机器人研究综述[J]. 机器人, 2020, 42(2):232-256. doi: 10.13973/j.cnki.robot.190009
	WANG W J, ZHENG Y T, LIN G Z, et al. Swarm robotics:a review[J]. Robotics, 2020, 42(2):232-256. (in Chinese)
[24]	李欣, 李若琼, 董海鹰. 基于仿生群体协同的集群智能控制研究[J]. 电气自动化, 2006, 28(4):3-5.
	LI X, LI R Q, DONG H Y. Study on the collective intelligence control based on model of swarm cooperative[J]. Electrical Automation, 2006, 28(4):3-5. (in Chinese)
[25]	李志刚. 智能化在集群作战中的应用探索[J]. 飞航导弹, 2019(11):1-3,17.
	LI Z G. Exploration of the application of intelligence in cluster warfare[J]. Air-to-Air Missiles, 2019(11):1-3,17. (in Chinese)
[26]	李晗, 苏京昭, 闫咏. 智能无人机集群技术概述[J]. 科技视界, 2017(26):5-7.
	LI H, SU J Z, YAN Y. Summary of the technology about swarm intelligence of UAV[J]. Science and Technology Horizon, 2017(26):5-7. (in Chinese)
[27]	王祥科, 刘志宏, 丛一睿, 等. 小型固定翼无人机集群综述和未来发展[J]. 航空学报, 2020, 41(4):023732.
	WANG X K, LIU Z H, CONG Y R, et al. Miniature fixed-wing UAV swarms:Review and outlook[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(4):023732. (in Chinese)
[28]	宋怡然, 申超, 李东兵. 美国分布式低成本无人机集群研究进展[J]. 飞航导弹, 2016(8):17-22.
	SONG Y R, SHEN C, LI D B. Research progress on distributed low-cost UAV clusters in the United States[J]. Flight Dynamics, 2016(8):17-22. (in Chinese)
[29]	陈晶. 解析美海军低成本无人机蜂群技术[J]. 无人机, 2016(1):24-26.
	CHEN J. Analysis of low-cost UAV swarm technology of the US Navy[J]. UAV, 2016(1):24-26. (in Chinese)
[30]	顾凌枫, 何明, 陈国友, 等. 无人机集群系统弹性研究[J]. 系统工程与电子技术, 2021, 43(1):156-162. doi: 10.3969/j.issn.1001-506X.2021.01.19
	GU L F, HE M, CHEN G Y, et al. Research on unmanned aerial vehicle swarm system resilience[J]. Systems Engineering and Electronics Technology, 2021, 43(1):156-162. (in Chinese)
[31]	齐小刚, 吴相远, 刘立芳. 无人机集群编队自组网可靠性评估[J]. 控制与决策, 2024, 39(2):689-696.
	QI X G, WU X Y, LIU L F. Reliability evaluation of ad hoc network for UAV swarm formation[J]. Control and Decision, 2024, 39(2):689-696. (in Chinese)
[32]	钱殷鼎, 王瑛, 孙贇. 集群无人机任务可靠性的贝叶斯网络建模[J]. 计算机科学与应用, 2020, 10(8):1450-1456.
	QIAN Y D, WANG Y, SUN Y. Bayesian network modeling for mission reliability of swarm UAVs[J]. Computer Science and Application, 2020, 10(8):1450-1456. (in Chinese)
[33]	张驰. 基于任务可靠性的无人机集群结构优化及空地协同韧性研究[D]. 郑州: 郑州大学, 2021.
	ZHANG C. Research on structure optimization and air-ground collaborative resilience of UAV swarms based on mission reliability[D]. Zhengzhou: Zhengzhou University, 2021. (in Chinese)
[34]	白光晗, 张驰, 兑红炎, 等. 无人机集群任务可靠性建模及重要度分析[J]. 机械工程学报, 2022, 58(10):361-373. doi: 10.3901/JME.2022.10.361
	BAI G H, ZHANG C, DUI H Y, et al. Mission reliability modeling and importance analysis of UAV swarm[J]. Journal of Mechanical Engineering, 2022, 58(10):361-373. (in Chinese) doi: 10.3901/JME.2022.10.361
[35]	孙浩, 孙天宇, 解龙, 等. 线性编队无人机集群多阶段任务可靠性建模方法研究[J]. 弹箭与制导学报, 2023, 43(4):74-79. doi: 10.15892/j.cnki.djzdxb.2023.04.011
	SUN H, SUN T Y, XIE L, et al. Research on phased-mission reliability modeling method of linear formation UAV swarm[J]. Journal of Ballistics and Missile Guidance, 2023, 43(4):74-79. (in Chinese)
[36]	李博. 面向任务的无人机集群可靠性建模与评估[D]. 长沙: 国防科学技术大学, 2018.
	LI B. Mission-oriented reliability modeling and prediction for unmanned aerial vehicle swarm[D]. Changsha: National University of Defense Technology, 2018. (in Chinese)
[37]	费陈, 赵亮, 孙许可, 等. 无人机蜂群技术发展研究[J]. 火炮发射与控制学报, 2024, 45(2):50-60.
	FEI C, ZHAO L, SUN X K, et al. Research on the development of UAV swarm technologies[J]. Journal of Artillery Launch and Control, 2024, 45(2):50-60. (in Chinese)
[38]	孙彧, 潘宣宏, 戴定成, 等. 无人机蜂群作战任务规划研究现状与展望[J]. 火力与指挥控制, 2024, 49(1):1-15.
	SUN Y, PAN X H, DAI D C, et al. Research status and prospect of UAV swarm combat mission planning[J]. Firepower and Command Control, 2024, 49(1):1-15. (in Chinese)
[39]	汪超, 杨健, 罗金亮, 等. 无人机集群军事运用模式研探[C]// 第十一届中国指挥控制大会. 北京,中国: 兵器工业出版社, 2023.
	WANG C, YANG J, LUO J L, et al. Research on the military application mode of UAV swarm[C]// Proceedings of the 11st China Command and Control Conference. Beijing,China: Publication House of Ordnance Industry, 2023. (in Chinese)
[40]	姜俊新. 无人机蜂群对防空作战的威胁与对策[J]. 国防科技, 2019, 40(6):108-113.
	JIANG J X. Threats and countermeatures of unmanned aerial vehicle swarm to aerial defense[J]. Defense Science and Technology, 2019, 40(6):108-113. (in Chinese)
[41]	张传良, 陈晓芳. 无人机蜂群或将改变未来战争形态[J]. 军事文摘, 2021(9):26-31.
	ZHANG C L, CHEN X F. UAV swarm may change the future form of warfare[J]. Military Digest, 2021(9):26-31. (in Chinese)
[42]	杨丽娜, 曹泽阳, 李勇祥. 无人机蜂群作战构成及作战概念研究[J]. 现代防御技术, 2020, 48(4):44-51. doi: 10.3969/j.issn.1009-086x.2020.04.07
	YANG L N, CAO Z Y, LI Y X. Research on the operational structure and concept of unmanned aerial swarm[J]. Modern Defense Technology, 2020, 48(4):44-51. (in Chinese)
[43]	陈镜. 无人机蜂群作战特点和对抗体系设想[J]. 无线电工程, 2020, 50(7):586-591.
	CHEN J. The operational characteristics of drone swarm and the conception of countermeasure system[J]. Radio Engineering, 2020, 50(7):586-591. (in Chinese)
[44]	杨中英, 王毓龙, 赖传龙. 无人机蜂群作战发展现状及趋势研究[J]. 飞航导弹, 2019(5):34-38.
	YANG Z Y, WANG Y L, LAI C L. Research on the development status and trend of UAV swarm combat[J]. Flight Dynamics, 2019(5):34-38. (in Chinese)
[45]	SKOROBOGATOV G, BARRADO C, SALAMÍ E. Multiple UAV systems:a survey[J]. Unmanned Systems, 2020, 8(2):149-169.
[46]	解决城市级的无人机管控问题,警察还需要这样的装备![EB/OL].(2020-09-15)[2024-04-25]. https://k.sina.com.cn/article_3358412904_vc82d506801900sppb.html.
	Solving the problem of city-level drone management,what equipment do the police still need? [EB/OL].(2020-09-15) [2024-04-25]. https://k.sina.com.cn/article_3358412904_vc82d506801900sppb.html. (in Chinese)
[47]	详解:为什么集群无人机是趋势,以及,它的关键技术是什么?[EB/OL].(2016-11-09)[2024-04-25]. https://www.leiphone.com/category/robot/91aRY5MtzkpH74cB.html.
	Detailed explanation:why swarm drones are a trend and what are their key technologies? [EB/OL].(2016-11-09) [2024-04-25]. https://www.leiphone.com/category/robot/9.1aRY5MtzkpH74cB.html. (in Chinese)
[48]	杨彦祥, 张翔引, 李波, 等. 基于群体智能算法的无人机蜂群拓扑构型方法[J]. 电子科技大学学报, 2023, 52(2):203-208.
	YANG Y X, ZHANG X Y, LI B, et al. UAV swarm topology shaping method based on swarm intelligence algorithm[J]. Journal of University of Electronic Science and Technology of China, 2023, 52(2):203-208. (in Chinese)
[49]	蒋毓晨. 无人机分级编队控制与自适应通信算法研究[D]. 成都: 电子科技大学, 2019.
	JIANG Y C. Research on hierarchical formation control and adaptive communication algorithm for UAVs[D]. Chengdu: University of Electronic Science and Technology, 2019. (in Chinese)
[50]	邱华鑫, 段海滨, 范彦铭. 基于鸽群行为机制的多无人机自主编队[J]. 控制理论与应用, 2015, 32(10):1298-1304.
	QIU H X, DUAN H B, FAN Y M. Multiple unmanned aerial vehicle autonomous formation based on the behavior mechanism in pigeon flocks[J]. Control Theory & Applications, 2015, 32(10):1298-1304. (in Chinese)
[51]	熊伟, 陈宗基, 周锐. 运用混合遗传算法的多机编队重构优化方法[J]. 航空学报. 2008, 29(增刊1):S209-S214.
	XIONG W, CHEN Z J, ZHOU R. Optimization of multiple flght vehicle formation reconstruction USINS hybrid genetic algorithm[J]. Journal of Aeronautics, 2008, 29(S1):S209-S214. (in Chinese)
[52]	周兴社, 武文亮. 无人系统群体智能及其研究进展[J]. 微电子学与计算机, 2021, 38(12):1-7.
	ZHOU X S, WU W L. Collective intelligence of unmanned systems and its research progress[J]. Microelectronics & Computer, 2021, 38(12):1-7. (in Chinese)
[53]	贺井然, 何广军, 于学生. 基于改进蜂群算法的无人机路径规划[J]. 火力与指挥控制, 2021, 46(10):103-106.
	HE J R, HE G J, YU X S. The UAV path planning based on improved artificial bee colony algorithm[J]. Firepower & Command Control, 2021, 46(10):103-106. (in Chinese)
[54]	齐小刚, 李博, 范英盛, 等. 多约束下多无人机的任务规划研究综述[J]. 智能系统学报, 2020, 15(2):204-217.
	QI X G, LI B, FAN Y S, et al. A survey of mission planning on UAVs systems based on multiple constraints[J]. Journal of Intelligent Systems, 2020, 15(2):204-217. (in Chinese)
[55]	吴皓, 高秋雅. 无人机蜂群电磁频谱自主协同半实物仿真研究[J]. 中国电子科学研究院学报, 2023, 18(1):1-6.
	WU H, GAO Q Y. Study on autonomous coordinated electromagnetic spectrum of UAV swarm[J]. Journal of China Academy of Electronics and Information Technology, 2023, 18(1):1-6. (in Chinese)
[56]	王昭. 无人机蜂群协同任务分配与规划研究[D]. 西安: 西安工业大学, 2021.
	WANG Z. Research on cooperative task assignment and planning of UAV swarm[D]. Xi’an: Xi’an Technological University, 2021. (in Chinese)
[57]	王书磊. 基于强化学习的无人机蜂群任务规划与决策方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2023.
	WANG S L. Research on mission planning and decision-making method of unmanned aerial vehicle swarm based on reinforcement learning[D]. Harbin: Harbin Institute of Technology, 2023. (in Chinese)
[58]	徐平, 赵浦媛, 马天乙, 等. 无人蜂群分布式相干协作通信系统[J]. 现代导航, 2023, 14(6):442-445.
	XU P, ZHAO P Y, MA T Y, et al. Distributed coordinated coherent communication system for UAV swarm application[J]. Modern Navigation, 2023, 14(6):442-445. (in Chinese)
[59]	贾维敏, 杨龑, 赵建伟, 等. 无人机蜂群通信感知一体化关键技术[J]. 国防科技, 2023, 44(3):88-95.
	JIA W M, YANG Y, ZHAO J W, et al. Integrated sensing and communications in UAV swarm operations[J]. Defense Science and Technology, 2023, 44(3):88-95. (in Chinese)
[60]	肖玉婷, 方勇纯, 梁潇, 等. 基于分块优化思想的多无人机覆盖路径规划[J]. 中国科学(技术科学), 2020, 50(4):439-452.
	XIAO Y T, FANG Y C, LIANG X, et al. Planning of the coverage route for multi-UAVs based on section optimization[J]. Science in China (Technological Sciences), 2020, 50(4):439-452. (in Chinese)
[61]	中国科学技术大学. 基于区块链和人工智能驱动的无人机蜂群协同工作平台:CN202010634515.2[P].(2020-10-09.)[2024-02-29].
	University of Science and Technology of China. UAV swarm collaborative work platform driven by blockchain and artificial intelligence:CN202010634515.2[P].(2020-10-09)[2024-02-29]. (in Chinese)
[62]	海丰通航科技有限公司. 一种复杂地域多地面站蜂群无人机数据融合方法和系统:CN202111537837.6[P].(2022-03-08) [2024-02-29].
	Haifeng Aviation Technology Co.,Ltd. A method and system for complex area multi-ground station swarm UAV data fusion:CN202111537837.6[P].(2022-03-08)[2024-02-29]. (in Chinese)
[63]	闵欢, 卢虎, 史浩东. 采用深度神经网络的无人机蜂群视觉协同控制算法[J]. 西安交通大学学报, 2020, 54(9):173-179,196.
	MIN H, LU H, SHI H D. Visual collaborative control for unmanned aerial vehicle swarm based on deep neural network[J]. Journal of Xi’an Jiaotong University, 2020, 54(9):173-179,196. (in Chinese)
[64]	李功源, 刘博涵, 杨雨豪, 等. 可信人工智能系统的质量属性与实现:三级研究[J]. 软件学报, 2023, 34(9):3941-3965.
	LI G Y, LIU B H, YANG Y H, et al. Quality attributes and practices of trustworthy artificial intelligence systems:a tertiary study[J]. Journal of Software, 2023, 34(9):3941-3965. (in Chinese)
[65]	KAUR D, USLU S, RITTICHIER K J, et al. Trustworthy artificial intelligence:a review[J]. ACM Computing Surveys, 2023, 55(2):39.
[66]	GEZICI B, TARHAN A K. Systematic literature review on software quality for AI-based software[J]. Empirical Software Engineering, 2022, 27(3):66.
[67]	XIONG P L, BUFFETT S, IQBAL S, et al. Towards a robust and trustworthy machine learning system development:an engineering perspective[J]. Journal of Information Security and Applications, 2022, 65:103121.
[68]	GUIDOTTI R, MONREALE A, RUGGIERI S, et al. A survey of methods for explaining black box models[J]. Computing Surveys, 2019, 51(5):93.
[69]	张斌, 林斌, 杨彦彰, 等. 消费级多旋翼无人机的飞行可靠性和安全性评测[J]. 中国测试, 2020, 46(1):129-139.
	ZHANG B, LIN B, YANG Y Z, et al. Evaluation of flight reliability and safety of consumer-grade multi-rotor UAVs[J]. China Measurement & Test, 2020, 46(1):129-139. (in Chinese)
[70]	齐小刚, 周雨桐, 刘立芳. 无人机集群对地作战任务可靠性评估[J]. 系统工程与电子技术, 2023, 45(9):2971-2978. doi: 10.12305/j.issn.1001-506X.2023.09.38
	QI X G, ZHOU Y T, LIU L F. Evaluation of the reliability of UAV swarm for ground combat missions[J]. Systems Engineering and Electronics, 2023, 45(9):2971-2978. (in Chinese) doi: 10.12305/j.issn.1001-506X.2023.09.38
[71]	ANDREWS J D, POOLE J, CHEN W H. Fast mission reliability prediction for unmanned aerial vehicles[J]. Reliability Engineering & System Safety, 2013, 120(12): 3-9.
[72]	PRESCOTT D, REMENYTE-PRESCOTT R, REED S, et al. A reliability analysis method using binary decision diagrams in phased mission planning[J]. Proceedings of the Institution of Mechanical Engineers,Part 0:Journal of Risk and Reliability, 2009, 223(2):133-143.
[73]	李润泽, 姜斌, 余自权, 等. 基于数据驱动的无人机集群故障检测与诊断[J]. 北京航空航天大学学报, 2024, 50(5):1586-1592.
	LI R Z, JIANG B, YU Z Q, et al. Fault detection and diagnosis of UAV swarm based on data-driven method[J]. Journal of Beihang University, 2024, 50(5):1586-1592. (in Chinese)
[74]	徐鑫. 基于状态切换的杂草测绘无人机群可靠性研究[D]. 南京: 东南大学, 2022.
	XU X. Research on reliability of weed mapping UAV swarm based on state switching[D]. Nanjing: Southeast University, 2022. (in Chinese)
[75]	谷旭平, 唐大全, 唐管政. 无人机集群关键技术研究综述[J]. 自动化与仪器仪表, 2021(4):21-26,30.
	GU X P, TANG D Q, TANG G Z. Summary of research on key technologies of UAV Swarms[J]. Automation and Instrumentation, 2021(4):21-26,30. (in Chinese)
[76]	王汉宁, 黄文翰, 廖燕邦, 等. 去中心化无人机集群控制系统设计与实现[J]. 信息技术与信息化, 2022(6):16-19.
	WANG H N, HUANG W H, LIAO Y B, et al. Design and implementation of decentralized unmanned aerial vehicle swarm control system[J]. Information Technology and Informatization, 2022(6):16-19. (in Chinese)
[77]	袁利平, 陈宗基. 一种新型多无人机系统体系结构的设计[J]. 系统仿真学报, 2008, 20(22):6137-6141.
	YUAN L P, CHEN Z J. Design of new architecture for Multi-UAV systems[J]. Journal of System Simulation, 2008, 20(22):6137-6141. (in Chinese)
[78]	WU C, LIU J J, GUO H Z, et al. Toward robust and intelligent drone swarm:challenges and future directions[J]. IEEE Network, 2020, 34:278-283.
[79]	杨彦祥. 无人机集群拓扑构型关键技术研究[D]. 成都: 电子科技大学, 2022.
	YANG Y X. Research on key techniques of UAV swarm topology shaping[D]. Chengdu: University of Electronic Science and Technology of China, 2022. (in Chinese)
[80]	HU J Y, LANZON A. An innovative tri-rotor drone and associated distributed aerial drone swarm control[J]. Robotics and Autonomous Systems, 2018, 103:162-174.
[81]	李相民, 颜骥, 刘波. 多无人机编队自主协同控制架构[J]. 电光与控制, 2015, 22(3):1-6.
	LI X M, YAN J, LIU B. Framework of autonomous cooperative control for Multi-UAV team[J]. Optoelectronics and Control, 2015, 22(3):1-6. (in Chinese)
[82]	韩月明, 方丹, 张红艳, 等. 无人机集群典型作战运用样式及关键技术分析[J]. 飞航导弹, 2020(9):43-47.
	HAN Y M, FANG D, ZHANG H Y, et al. Analysis of typical combat application patterns and key technologies of UAV swarm[J]. Missiles and Guidance, 2020(9):43-47. (in Chinese)
[83]	CHUNG S J, PARANJAPE A, et al. A survey on aerial swarm robotics[J]. IEEE Transactions on Robotics, 2018, 34(4):837-855.
[84]	贾高伟, 王建峰. 无人机集群任务规划方法研究综述[J]. 系统工程与电子技术, 2021, 43(1):99-111. doi: 10.3969/j.issn.1001-506X.2021.01.13
	JIA G W, WANG J F. Research review of UAV swarm mission planning method[J]. Systems Engineering and Electronics, 2021, 43(1):99-111. (in Chinese) doi: 10.3969/j.issn.1001-506X.2021.01.13
[85]	陈少飞. 无人机集群系统侦察监视任务规划方法[D]. 长沙: 国防科学技术大学, 2016.
	CHEN S F. Planning for reconnaissance and monitoringusing UAV swarms[D]. Changsha: National University of Defense Technology, 2016. (in Chinese)
[86]	曹文静, 杨林. 多无人机自主协同方法协同性能研究[J]. 飞航导弹, 2017(5):43-49.
	CAO W J, YANG L. Research on collaborative performance of multi-UAV autonomous cooperation methods[J]. Flight Dynamics, 2017(5):43-49. (in Chinese)
[87]	王元鑫, 温家鑫, 袁涛, 等. 无人机集结问题主要性能指标[J]. 兵工自动化, 2020, 39(10):71-75.
	WANG Y X, WEN J X, YUAN T, et al. Main performance index of UAV aggregation problem[J]. Ordnance Automation, 2020, 39(10):71-75. (in Chinese)
[88]	吴静, 王琪. 编队协同对地攻击总体作战效能评估[J]. 计算机系统应用, 2012, 21(3):83-86.
	WU J, WANG Q. Operational effectiveness evaluation for formation attacking ground targets[J]. Computer Systems & Applications, 2012, 21(3):83-86. (in Chinese)
[89]	陈新颖, 盛敏, 李博, 等. 面向6G的无人机通信综述[J]. 电子与信息学报, 2022, 44(3):781-789.
	CHEN X Y, SHENG M, LI B, et al. Survey on unmanned aerial vehicle communications for 6G[J]. Journal of Electronics and Information Technology, 2022, 44(3):781-789. (in Chinese)
[90]	赵林, 张宇飞, 姚明旿, 等. 无人机集群协同技术发展与展望[J]. 无线电工程, 2021, 51(8):823-828.
	ZHAO L, ZHANG Y F, MING Y X, et al. Development and trend of UAV swarm cooperative techniques[J]. Radio Engineering, 2021, 51(8):823-828 (in Chinese).
[91]	王兆轩, 李扬, 吕洋, 等. 无人机系统信息安全前沿技术发展趋势[J]. 软件导刊, 2021, 20(10):7-12.
	WANG Z X, LI Y, LÜ Y, et al. Frontier technology development trend of UAS cyber security[J]. Software Guide, 2021, 20(10):7-12. (in Chinese)
[92]	侯兆阳. 集群无人机空地链路通信容量提升关键技术研究[D]. 西安: 西安电子科技大学, 2021.
	HOU Z Y. Research on key technologies for improving air to ground link communication capacity of cluster UAV[D]. Xi’an: Xidian University, 2021. (in Chinese)
[93]	苗建国, 王剑宇, 张恒, 等. 无人机故障诊断技术研究进展概述[J]. 仪器仪表学报, 2020, 41(9):56-69.
	MIAO J G, WANG J Y, ZHANG H, et al. Review of the development of fault diagnosis technology for unmanned aerial vehicle[J]. Acta Instrumentation, 2020, 41(9):56-69. (in Chinese)
[94]	董艳. 基于观测器的无人机编队系统的故障诊断技术研究[D]. 南京: 南京航空航天大学, 2018.
	DONG Y. Research on fault diagnosis for AVs formation systems based on observer[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2018. (in Chinese)