重庆铁马工业集团有限公司,重庆 400030
中兵智能创新研究院有限公司,北京 100072
群体协同与自主国家级重点实验室,北京 100072
陆军装备部驻重庆地区第六军事代表室,重庆 400030
*通信作者邮箱:liulingk@stu.cqut.edu.cn
收稿:2025-06-30,
网络首发:2026-02-11,
纸质出版:2026-01-31
移动端阅览
刘灵坤, 项燊, 阚磊, 等. 陆域无人平台“通信-感知-决策”一体化协同技术综述[J]. 兵工学报, 2026,47(1):250561.
LIU Lingkun, XIANG Shen, GAN Lei, et al. A Review on Integrated Communication, Sensing and Decision-making Technologies for Land-based Unmanned Platforms[J]. Acta Armamentarii, 2026, 47(1): 250561.
刘灵坤, 项燊, 阚磊, 等. 陆域无人平台“通信-感知-决策”一体化协同技术综述[J]. 兵工学报, 2026,47(1):250561. DOI: 10.12382/bgxb.2025.0561.
LIU Lingkun, XIANG Shen, GAN Lei, et al. A Review on Integrated Communication, Sensing and Decision-making Technologies for Land-based Unmanned Platforms[J]. Acta Armamentarii, 2026, 47(1): 250561. DOI: 10.12382/bgxb.2025.0561.
现有陆域无人平台普遍采用松耦合的通信、感知与决策系统,难以有效支撑高烈度战场对快速响应、自主协同与抗毁运行的综合作战需求。随着抗毁通信、多模态感知、边缘智能和分布式协同决策等关键技术的持续演进,陆域无人平台正加速由单平台自治向集群协同演进,逐步构建“通信-感知-决策”一体化协同架构。综述聚焦高对抗环境下陆域无人平台的一体化协同技术,系统梳理其核心技术演化路径,重点解析集群内的抗毁通信、自主感知与边缘智能等关键能力,深入探讨去中心化分布式决策机制及其基于联邦学习的优化策略。归纳总结了陆域无人平台在复杂战场环境中面临的系统性风险与核心挑战,并对多平台跨域协同发展与可解释性人工智能伦理治理体系进行了展望。
The current land-based unmanned platforms generally rely on loosely coupled communication
sensing and decision-making systems
which are unable to effectively meet the comprehensive combat requirements of high-intensity battlefields for rapid response
autonomous coordination and operational resilience. With ongoing advances in the key technologies such as resilient communication
multi-modal sensing
edge intelligence
and distributed collaborative decision-making
the land-based unmanned platforms are transitioning from single-unit autonomy toward cluster collaboration
gradually forming an integrated communication-sensing-decision-making architecture. This paper examines the integrated cooperative technology of land-based unmanned platform in high-intensity combat environments
systematically review the evolution path of its core technologies
emphatically analyzes the key capabilities of resilient intra-cluster communication
autonomous perception and edge intelligence
and deeply explore the decentralized decision-making mechanism and its optimization strategies based on federated learning. It also summarizes major system-level risks and main challenges faced by land-based unmanned platforms in complex battlefield environments
and provides insights into the development of cross-platform interoperability and the ethical governance framework for explainable artificial intelligence (AI) .
於志文,孙卓,程岳,等.智能无人机集群协同感知计算研究综述[J].航空学报,2024,45(20):630912.
YU Z W, SUN Z, CHENG Y, et al.A review of intelligent UAV swarm collaborative perception and computation [J].Acta Aeronautica et Astronautica Sinica, 2024, 45(20): 630912.(in Chinese)
BENDETT S, BLANK S, CHERAVITCH J, et al. Russian unmanned vehicle developments: Syria and beyond [R]. Washington, D. C, US:CSIS,2020:38-47.
HEWISH M, LOK J. SWORDS:the first armed robotic vehicle in combat[J]. Jane's International Defense Review, 2006, 39(5):24-29.
GULDEN T R, LAMB J, HAGEN J, et al. Modeling rapidly composable, heterogeneous, and fractionated forces: findings on mosaic warfare from an agent-based Model [M]. Santa Monica, CA, US:RAND Corporation,2021.
黄林江,于小红,王杰娟,等.解析美军马赛克战概念的内涵和战场变化的关系[J].信息工程大学学报,2023,24(5):627-633.
HUANG L J, YU X H, WANG J J, et al.Analyzing the relationship between connotation of the american mosaic war concept and the battlefield changes [J].Journal of Information Engineering University,2023,24(5):627-633.(in Chinese)
FACHEY K, MILLER M. Unmanned systems integrated roadmap 2017-2042[R]. Arlington County, TX, US:Office of the Secretary of Defense,2018.
党晨光,李超,封慧勇,等.陆空无人集群协同作战效能评估[J].兵器装备工程学报,2025,46(8):53-59.
DANG C G, LI C, FENG H Y, et al.Evaluation of the effectiveness of air-ground unmanned swarm cooperative operations[J].Journal of Ordnance Equipment Engineering, 2025, 46(8): 53-59.(in Chinese)
薛建强,史彦军,李波.面向无人集群的边缘计算技术综述[J].兵工学报,2023,44(9):2546-2555.
XUE J Q, SHI Y J, LI B.A review of edge computing technology for unmanned swarms [J].Acta Armamentarii, 2023, 44(9):2546-2555.(in Chinese)
王侃,曹铁林,李旭杰,等.无人机辅助边缘计算网络轨迹规划与资源分配研究综述[J].电子与信息学报,2025,47(5):1266-1281.
WANG K, CAO T L, LI X J, et al.A survey on trajectory planning and resource allocation in unmanned aerial vehicle-assisted edge computing networks [J].Journal of Electronics & Information Technology,2025,47(5):1266-1281.(in Chinese)
王童豪,彭星光,胡浩,等.海上有人/无人协同系统及其关键技术综述[J].兵工学报,2024,45(10):3317-3340.
WANG T H, PENG X G, HU H, et al.Maritime manned/unmanned collaborative systems and key technologies: a survey [J].Acta Armamentarii,2024,45(10):3317 - 3340.(in Chinese)
孔国杰,冯时,于会龙,等.无人集群系统协同运动规划技术综述[J].兵工学报,2023,44(1):11-26.
KONG G J, FENG S, YU H L, et al.A review on cooperative motion planning of unmanned vehicles[J].Acta Armamentarii, 2023,44(1):11-26.(in Chinese)
CLARK B, PATT D, SCHRAMM H. mosaic warfare: exploiting artificial intelligence and autonomous systems to implement decision-centric operations[R]. Washington, D. C. ,US:CSBA, 2020.
PREDD J B, SCHMID J, BARTELS E M, et al. Acquiring a mosaic force:issues, options, and trade-offs[R]. Santa Monica, CA, US:RAND Corporation,2021.
HOEHN J R. Joint all domain command and control(JADC2)[R]. New York, NY, US:Congressional Research Service,2022.
杨国强,朱启超.美军以“复制者计划”推动军事智能化布局[J].世界知识,2024(10):44-46.
YANG G Q, ZHU Q C.The U.S military promotes military intelligence layout with the “ Replicator program ” [J].World Affairs,2024(10):44-46.(in Chinese)
付梦印,宋文杰,张婷,等.陆上无人平台自主决策与规划技术综述[J].导航与控制,2024,23(1):1-13.
FU M Y, SONG W J, ZHANG T, et al.A review of decisionmaking and motion planning technology for unmanned ground platforms[J].Navigation and Control,2024,23(1):1-13.(in Chinese)
潘琦,马志强.马赛克战研究发展综述[J].中国电子科学研究院学报,2021,16(7):728-736.
PAN Q, MA Z Q.Research and development of mosaic warfare [J].Journal of China Academy of Electronics and Information Technology,2021,16(7):728-736.(in Chinese)
李强,王飞跃.马赛克战概念分析和未来陆战场网信体系及其智能对抗研究[J].指挥与控制学报,2020,6(2):87-93.
LI Q, WANG F Y.Conceptual analysis of mosaic warfare and systems of network information systems for intelligent countermeasures and future land battles[J].Journal of Command and Control,2020,6(2):87-93.(in Chinese)
穆巍炜,何滨兵,齐尧,等.基于环形阵列的地面无人装备集群通信干扰抑制[J].兵工学报,2023,44(5):1414-1421.
MU W W, HE B B, QI Y, et al.Interference suppressionin ground unmanned equipment cluster communication based on circular array[J].Acta Armamentarii, 2023, 44(5): 1414-1421.(in Chinese)
JIANG P F, GENG X S, PAN G W, et al. GNSS anti-interference technologies for unmanned systems:a brief review [J]. Drones, 2025,9(5):349.
王野,司璐璐,陈慧岩,等.水陆无人两栖车环境感知技术综述[J].车辆与动力技术,2024(2):47-56.
WANG Y, SI L L, CHEN H Y, et al.An overview of environmental perception technology of unmanned amphibious vehicle[J].Vehicle & Power Technology,2024(2):47-56.(in Chinese)
杨洋,王烨,康大勇,等.基于强化学习的多智能体协同电子对抗方法[J].兵器装备工程学报,2024,45(7):1-10.
YANG Y, WANG Y, KANG D Y, et al.Multi agent cooperative electronic countermeasure method based on reinforcement learning [J].Journal of Ordnance Equipment Engineering,2024,45(7):1-10.(in Chinese)
杨光红,石重霄.多智能体系统的分布式协同定位方法研究综述[J].复杂系统与复杂性科学,2025,22(2):18-30.
YANG G H, SHI C X.A survey on distributed cooperative localization for multi-agent systems [J].Complex Systems and Complexity Science,2025,22(2):18-30.(in Chinese)
李佳键,史彦军,杨雨,等.无人集群作战任务的多智能体强化学习卸载决策[J].兵工学报,2023,44(11):3295-3309.
LI J J, SHI Y J, YANG Y, et al.Multi-agent reinforcement learning-based offloading decision for UAV cluster combat tasks [J].Acta Armamentarii,2023,44(11):3295 - 3309.(in Chinese)
MUNASINGHE I, PERERA A, DEO R. A comprehensive review of UAV-UGV collaboration: advancements and challenges [J]. Journal of Sensor and Actuator Networks,2024,13(6):81.
SHI J C, REN Y, TANG H S, et al. Hydraulic directional valve fault diagnosis using a weighted adaptive fusion of multi-dimensional features of a multi-sensor [J]. Journal of Zhejiang University-Science A,2022,23(4):257-271.
董希旺,于江龙,化永朝,等.集群系统智能协同IOODA技术体系架构与关键技术[J].航空学报,2025,46(4):030911.
DONG X W, YU J L, HUA Y C, et al.Architecture and key technologies of intelligent cooperative IOODA technology system for swarm systems[J].Acta Aeronautica et Astronautica Sinica, 2025,46(4):030911.(in Chinese)
CHAI R Q, GUO Y L, ZUO Z Y, et al. Cooperative motion planning and control for aerial-ground autonomous systems:methods and applications[J]. Progress in Aerospace Sciences, 2024,146:101005.
ZANG Z, ZHANG X, SONG J R, et al. A coordinated behavior planning and trajectory planning framework for multi-ugvs in unstructured narrow interaction scenarios[J]. IEEE Transactions on Intelligent Vehicles,2025,10(4):2781-2794.
张涛,李清,张长水,等.智能无人自主系统的发展趋势[J].无人系统技术,2018,1(1):11-22.
ZHANG T, LI Q, ZHANG C S, et al.Current trends in the development of intelligent unmanned autonomous systems [J].Unmanned Systems Technology, 2018, 1(1): 11 - 22.(in Chinese)
李飞翔,李宁,刘明哲,等.基于边缘计算技术的智能无人系统[J].太赫兹科学与电子信息学报,2024,22(1):80-86.
LI F X, LI N, LIU M Z, et al.Intelligent unmanned system based on edge computing technology[J].Journal of Terahertz Science and Electronic Information Technology,2024,22(1):80-86.(in Chinese)
ZHANG Y, JIANG C, YUE B L, et al. Information fusion for edge intelligence:a survey [J]. Information Fusion, 2022, 81(12):171-186.
黄宝贵,禹继国,马春梅.基于SINR的动态无线网络分布式链路调度[J].软件学报,2023,34(9):4225-4238.
HUANG B G, YU J G, MA C M.Distributed link scheduling in dynamic wireless networks under sinr model [J].Journal of Software,2023,34(9):4225-4238.(in Chinese)
唐焕博,郑鸿强,沈启航,等.基于QoE的无人机网络部署和缓存策略优化方法[J].计算机应用研究,2023,40(5):1473-1479.
TANG H B, ZHENG H Q, SHEN Q H, et al.QoE-based optimization method for UAV network deployment and cache strategy[J].Application Research of Computers,2023,40(5):1473-1479.(in Chinese)
ALI A M, NGADI M A, SHAM R, et al. Enhanced QoS routing protocol for an unmanned ground vehicle, based on the ACO approach[J]. Sensors,2023,23(3):1431.
GARG S, IHLER A, BENTLEY E S, et al. A cross-layer, mobility, and congestion-aware routing protocol for UAV networks [J]. IEEE Transactions on Aerospace and Electronic Systems, 2022,59(4):3778-3796.
谢添,高士顺,赵海涛,等.基于强化学习的定向无线通信网络抗干扰资源调度算法[J].电波科学学报,2020,35(4):531-541.
XIE T, GAO S S, ZHAO H T, et al.An anti-jamming resource scheduling algorithm for directional wireless[J].Chinese Journal of Radio Science,2020,35(4):531-541.(in Chinese)
宋博文.基于QUIC协议的网络通信代理系统设计与实现[D].南京:东南大学,2022.
SONG B W.Design and implementation of a network communication proxy system based on QUIC protocol [D].Nanjing:Southeast University,2022.(in Chinese)
严汉池,苏春林.基于AES和Hash算法的生物信息数据库混合加密方法[J].吉林大学学报(工学版),2024,54(10):2994-2999.
YAN H C, SU C L.Hybrid encryption method for biological information database based on AES and hash algorithm [J].Journal of Jilin University(Engineering and Technology Edition),2024,54(10):2994-2999.(in Chinese)
RAO J T, CUI Z. Chosen plaintext combined attack against SM4 algorithm[J]. Applied Sciences,2022,12(18):9349.
王桂胜,董淑福,黄国策.无人系统认知联合抗干扰通信研究综述[J].计算机工程与应用,2022,58(8):1-11.
WANG G S, DONG S F, HUANG G C.Survey on cognitive and joint anti-jamming communication for unmanned systems [J].Computer Engineering and Applications,2022,58(8):1-11.(in Chinese)
石锐,李勇,牛英滔.无线通信智能抗干扰研究进展和发展方向[J].电子信息对抗技术,2024,39(4):98-108.
SHI R, LI Y, NIU Y T.Research progress and development trends of intelligent anti-jamming in wireless communication [J].Electronic Information Warfare Technology, 2024, 39(4): 98-108.(in Chinese)
施育鑫,李玉生,安康.基于干扰认知的索引调制跳频抗干扰方法[J].电波科学学报,2023,38(5):757-763,852.
SHI Y X, LI Y S, AN K.Anti-jamming method of index modulation frequency hopping based on interference cognition [J].Chinese Journal of Radio Science,2023,38(5):757-763, 852.(in Chinese)
SUN Y F, LIN Z, AN K, et al. Multi-functional RIS-assisted semantic anti-jamming communication and computing in integrated aerial-ground networks[J]. IEEE Journal on Selected Areas in Communications,2024,42(12):3597-3617.
王桂胜,黄国策,王叶群,等.基于认知驱动的变换域通信智能抗干扰方法[J].系统工程与电子技术,2021,43(1):223-231.
WANG G S, HUANG G C, WANG Y Q, et al.Anti-interference method with intelligence for transform domain communication based on cognitive-engine [J].Systems Engineering and Electronics,2021,43(1):223-231.(in Chinese)
马金辰.基于蝴蝶优化算法的多径电子通信环境抗干扰系统设计[J].现代电子技术,2025,48(12):1-5.
MA J C.Design of multipath electronic communication environment anti- interference system based on butterfly optimization algorithm [J].Modern Electronics Technique, 2025, 48(12): 1-5.(in Chinese)
梁应敞,谭俊杰,DUSIT N.智能无线通信技术研究概况[J].通信学报,2020,41(7):1-17.
LIANG Y C, TAN J J, DUSIT N.Overview on intelligent wireless communication technology [J].Journal on Communications, 2020,41(7):1-17.(in Chinese)
张征明.智能无线通信关键技术研究[D].南京:东南大学,2023.
ZHANG Z M.Research on key technologies of intelligent wireless communication[D].Nanjing: Southeast University, 2023.(in Chinese)
刘淼,龚玉萍,任国春,等.基于退火Q学习的动态扩频通信抗干扰策略[J].无线电通信技术,2025,51(2):274-282.
LIU M, GONG Y P, REN G C, et al.Dynamic spread spectrum communication anti-interference strategy based on annealed Qlearning[J].Radio Communications Technology,2025,51(2):274-282.(in Chinese)
宋佰霖,许华,蒋磊,等.一种基于深度强化学习的通信抗干扰智能决策方法[J].西北工业大学学报,2021,39(3):641-649.
SONG B L, XU H, JIANG L, et al.An intelligent decision-making method for anti-jamming communication based on deep reinforcement learning [J].Northwestern Polytechnical University,2021,39(3):641-649.(in Chinese)
黄杰.面向无人驾驶汽车协同感知的边缘计算关键技术研究[D].南宁:广西大学,2024.
HUANG J.Research on key technologies of edge computing for cooperative perception in connected and autonomous vehicles [D].Nanning:Guangxi University,2024.(in Chinese)
赵丹露,张永安,何光辉,等.透烟雾红外数字全息像的亮度增强算法[J].中国激光,2023,50(18):290-301.
ZHAO D L, ZHANG Y A, HE G H, et al.Brightness enhancement algorithm for infrared digital holographic image through smoke [J].Chinese Journal of Lasers,2023,50(18):290-301.(in Chinese)
TIAN C W, ZHENG M H, ZUO W M, et al. Multi-stage image denoising with the wavelet transform [J]. Pattern Recognition, 2023,134:109050.
WU W C, LIU S J, XIA Y, et al. Dual residual attention network for image denoising[J]. Pattern Recognition,2024,149:110291.
GHIASI G, LIN T Y, PANG R M, et al. Nas-fpn:learning scalable feature pyramid architecture for object detection: arXiv:1904. 07392 [R]. Ithaca, NY, US: Cornell University, 2019:1904. 07392.
VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need: arXiv: 1706. 03762 [R]. Ithaca, NY, US: Cornell University,2017:1706. 03762.
李治林,杜玉军,王牌.基于YOLOv4改进算法的坦克装甲车辆目标检测[J].电光与控制,2024,31(6):105-111.
LI Z L, DU Y J, WANG P.Tank and armored vehicle target detection based on improved YOLOv4 algorithm[J].Electronics Optics & Control,2024,31(6):105-111.(in Chinese)
褚文杰.基于YOLOv5的坦克装甲车辆目标检测关键技术的研究[D].北京:北京交通大学,2021.
CHU W J.Research on the key technology of tank and armored vehicle target detection based on YOLOv5[D].Beijing:Beijing Jiaotong University,2021.(in Chinese)
吉浩宇,孟卫华,张新朝,等.复杂背景下的红外运动目标语义分割算法研究[J].航空兵器,2025,32(2):80-86.
JI H Y, MENG W H, ZHANG X C, et al.Research on semantic segmentation algorithm for infrared moving targets in complex backgrounds[J].Aero Weaponry,2025,32(2):80-86.(in Chinese)
XIONG C R, LIU G Q, WU Q, et al. Ton-vio:online time offset modeling networks for robust temporal alignment in high dynamic motion vio: arXiv: 2403. 12504 [R]. Ithaca, NY, US: Cornell University,2024:2403. 12504.
BURNETT K, SCHOELLIG A P, BARFOOT T D. Continuous-time radar-inertial and lidar-inertial odometry using a gaussian process motion prior: arXiv:2402. 06174[R]. Ithaca, NY, US:Cornell University,2024:2402. 06174.
LIANG T T, XIE H W, YU K C, et al. Bevfusion:a simple and robust lidar-camera fusion framework [J]. Advances in Neural Information Processing Systems,2022,35:10421-10434.
ZHAO Y, GONG Z, ZHENG P R, et al. Simplebev: improved lidar-camera fusion architecture for 3D object detection: arXiv:2411. 05292[R]. Ithaca, NY, US: Cornell University, 2024:2411. 05292.
AHMAD R, ALKHAMMASH E H. Online adaptive Kalman filtering for real-time anomaly detection in wireless sensor networks[J]. Sensors,2024,24(15):5046.
王金科,左星星,赵祥瑞,等.多源融合SLAM的现状与挑战[J].中国图象图形学报,2022,27(2):368-389.
WANG J K, ZUO X X, ZHAO X R, et al.Review of multi-source fusion SLAM:current status and challenges[J].Journal of Image and Graphics,2022,27(2):368-389.(in Chinese)
SHAN T, RAJVANSHI A, MITHUN N, et al. Graph2nav: 3D object-relation graph generation to robot navigation: arXiv:2504. 16782 [R]. Ithaca, NY, US: Cornell University, 2025:2504. 16782.
SHAN T X, ENGLOT B, MEYERS D, et al. Lio-sam: tightly-coupled lidar inertial odometry via smoothing and mapping[C]∥Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems. Washington, D. C. , US: IEEE, 2020:5135-5142.
KIRILLOV A, MINTUN E, RAVI N, et al. Segment anything[C]∥Proceedings of the IEEE/CVF International Conference on Computer Vision. Washington, D. C. ,US:IEEE,2023:4015-4026.
OQUAB M, DARCET T, MOUTAKANNI T, et al. Dinov2:learning robust visual features without supervision: arXiv:2304. 07193[R]. Ithaca, NY, US: Cornell University, 2023:2304. 07193.
XIAO B, WU H P, XU W J, et al. Florence-2:advancing a unified representation for a variety of vision tasks[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, D. C. ,US:IEEE,2024:4818-4829.
LI S Q, LIU G Q, LI L, et al. A review on air-ground coordination in mobile edge computing: key technologies, applications and future directions [J]. Tsinghua Science and Technology,2024, 30(3):1359-1386.
ZHU Z P, DU Q W, WANG Z P, et al. A survey of multi-agent cross domain cooperative perception [J]. Electronics, 2022, 11(7):1091.
HOWARD A G, ZHU M L, CHEN B, et al. Mobilenets:efficient convolutional neural networks for mobile vision applications:arXiv:1704. 04861 [R]. Ithaca, NY, US: Cornell University, 2017:1704. 04861.
HAN K, WANG Y, TIAN Q, et al. Ghostnet:more features from cheap operations[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, D. C. , US:IEEE,2020:1580-1589.
GAO W, RAO D, YANG Y, et al. Edge devices friendly self-supervised monocular depth estimation via knowledge distillation [J]. IEEE Robotics and Automation Letters,2023,8(12):8470-8477.
陈志旺,王航,刘旺,等.抗遮挡与尺度自适应的改进KCF跟踪算法[J].控制与决策,2021,36(2):457-462.
CHEN Z W, WANG H, LIU W, et al.Improved KCF tracking algorithm based on anti-occlusion and scale-transformation[J].Control and Decision,2021,36(2):457-462.(in Chinese)
HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,37(3):583-596.
YAN P M, YAO S B, ZHU Q Y, et al. Real-time detection and tracking of infrared small targets based on grid fast density peaks searching and improved KCF[J]. Infrared Physics & Technology, 2022,123:104181.
洪培钦,罗灵鲲,刘冰,等.引入轻量注意力的孪生神经网络目标跟踪算法[J].计算机工程与应用,2022,58(12):112-121.
HONG P Q, LUO L K, LIU B, et al.Siamese neural network target tracking algorithm with lightweight attention [J].Computer Engineering and Applications, 2022, 58(12): 112-121.(in Chinese)
GUTIÉRREZ C S V, JUAN L U S, UGARTE I Z, et al. Towards a distributed and real-time framework for robots:Evaluation of ROS 2. 0 communications for real-time robotic applications: arXiv:1809. 02595 [R]. Ithaca, NY, US: Cornell University, 2018:1809. 02595.
PARDO-CASTELLOTE G. OMG data-distribution service:Architectural overview[C]∥Proceedings of the 23rd International Conference on Distributed Computing Systems Workshops. Washington, D. C. ,US:IEEE,2003:200-206.
GAMBO M L, DANASABE A, ALMADANI B, et al. A systematic literature review of dds middleware in robotic systems [J]. Robotics,2025,14(5):63.
SPERLING N, BENDRICK A, STÖHRMANN D, et al. Caching in automated data centric vehicles for edge computing scenarios[C]∥Proceedings of the 2023 60th ACM/IEEE Design Automation Conference. Washington, D. C. ,US:IEEE,2023:1-4.
TANG J H, ZENG Y. UAV data acquisition and processing assisted by UGV-enabled mobile edge computing [J]. IEEE Transactions on Industrial Informatics,2025,21(5):3695-3704.
SUN M D, TANG J H, ZHAO J. Efficient updating of UGV-assisted reality digital twin:an AoDT-oriented approach[J]. IEEE Internet of Things Journal,2025,12(15):29109-29120.
ZAKI A M, ELSAYED S A, ELGAZZAR K, et al. Quality-aware task offloading for cooperative perception in vehicular edge computing [J]. IEEE Transactions on Vehicular Technology, 2024,73(12):18320-18332.
JI T X, LUO C Q, YU L X, et al. Energy efficient computation offloading in mobile edge computing systems with uncertainties [J]. IEEE Transactions on Wireless Communications, 2022, 21(8):5717-5729.
YANG J M, SHAH A A, PEZAROS D. A survey of energy optimization approaches for computational task offloading and resource allocation in MEC networks [J]. Electronics, 2023,12(17):3548.
ZHAN C Q, ZHENG S J, CHEN J Y, et al. Integrated quality of service for offline and online services in edge networks via task offloading and service caching[J]. Sensors,2024,24(14):4677.
PARK J, CHUNG K. Distributed DRL-based computation offloading scheme for improving QoE in edge computing environments[J]. Sensors,2023,23(8):4166.
张凯歌,卢志刚,聂天常,等.面向无人装备的智能边缘计算软技术分析[J].兵工学报,2023,44(9):2611-2621.
ZHANG K G, LU Z G, NIE T C, et al.Analysis of soft intelligent edge computing technologies for unmanned systems [J].Acta Armamentarii,2023,44(9):2611-2621.(in Chinese)
高志发,周宇,杨航,等.多域集群分布式智能协同自主控制技术研究现状与展望[J].兵工学报,2024, 45(增刊2):9-16.
GAO Z F, ZHOU Y, YANG H, et al.Research status and prospect of multi-domain cluster distributed intelligent cooperative autonomous control technology [J].Acta Armamentarii, 2024, 45(S2):9-16.(in Chinese)
SCHNEIDER F B. Implementing fault-tolerant services using the state machine approach:a tutorial[J]. ACM Computing Surveys, 1990,22(4):299-319.
COLLEDANCHISE M,ÖGREN P. Behavior trees in robotics and AI:an introduction[M]. Boca Raton, FL, US:CRC Press,2018.
PATHAK V M, VERMA V K, RAWAT B S, et al. Current status of pesticide effects on environment, human health and it's ecofriendly management as bioremediation:a comprehensive review [J]. Frontiers in Microbiology,2022,13:962619.
FAN J M, CHEN X, LIANG X. UAV trajectory planning based on bi-directional APF-RRT * algorith m with goal-biased[J ] . Expert Systems with Applications,2023,213:119137.
SCHWENZER M, AY M, BERGS T, et al. Review on model predictive control: an engineering perspective [J]. The International Journal of Advanced Manufacturing Technology, 2021,117(5):1327-1349.
吕晔,周锐,李兴,等基于多轮次分布式拍卖的异构多任务分配算法[J].北京航空航天大学学报,2025,51(3):1018-1027.
LÜ Y, ZHOU R, LI X, et al.A heterogeneous multi-task assignment algorithm based on multi-round distributed auction [J].Journal of Beijing University of Aeronautics and Astronautics,2025,51(3):1018-1027.(in Chinese)
董昭荣,赵民,姜利,等.异构无人系统集群自主协同关键技术综述[J].遥测遥控,2024,45(4):1-11.
DONG Z R, ZHAO M, JIANG L, et al.Review on key technologies of autonomous collaboration in heterogeneous unmanned system cluster[J].Journal of Telemetry Tracking and Command,2024, 45(4):1-11.(in Chinese)
毕文豪,王炤晰,吴伟,等.基于公共品博弈的无人机集群自主协同机制[J].兵工学报,2023,44(11):3407-3421.
BI W H, WANG Z X, WU W, et al.Autonomous collaboration mechanism of UAV cluster based on public goods game[J].Acta Armamentarii,2023,44(11):3407-3421.(in Chinese)
LIU F C, LI M, LIU X X, et al. A review of federated metalearning and its application in cyberspace security [J]. Electronics,2023,12(15):3295.
HINTON G, VINYALS O, DEAN J. Distilling the knowledge in a neural network:arXiv:1503. 02531[R]. Ithaca, NY, US:Cornell University,2015:1503. 02531.
BAI X S, FIELBAUM A, KRONMÜLLER M, et al. Group-based distributed auction algorithms for multi-robot task assignment [J]. IEEE Transactions on Automation Science and Engineering, 2022,20(2):1292-1303.
WANG Y, LI H P, YAO Y. An adaptive distributed auction algorithm and its application to multi-AUV task assignment[J]. Science China Technological Sciences, 2023, 66(5): 1235-1244.
GAO Y, CHEN J F, CHEN X, et al. Asymmetric self-play-enabled intelligent heterogeneous multirobot catching system using deep multiagent reinforcement learning [J]. IEEE Transactions on Robotics,2023,39(4):2603-2622.
JIA Y J, SONG Y, CHENG J Y, et al. A deep reinforcement learning approach using asymmetric self-play for robust multirobot flocking [J]. IEEE Transactions on Industrial Informatics,2025,21(4):3266-3275.
倪悦.基于Paxos的低延迟分布式共识算法研究[D].天津:天津理工大学,2024.
NI Y.Research of low latency distributed consensus algorithm based on Paxos[D].Tianjin:Tianjin University of Technology, 2024.(in Chinese)
袁昊天,李飞.基于改进Raft共识算法和PBFT共识算法的双层共识算法[J].计算机应用研究,2024,41(5):1314-1320.
YUAN H T, LI F.Double layer consensus algorithm based on improved Raft consensus algorithm and PBFT[J].Application Research of Computers, 2024, 41(5): 1314 - 1320.(in Chinese)
李诗颖,丁应和,孙海文,等.规模化无人集群共识模型与协同控制方法[J].兵工学报,2024,45(增刊2):113-122.
LI S Y, DING Y H, SUN H W, et al.Consensus model and collaborative control method of large-scale unmanned cluster[J].Acta Armamentarii,2024,45(S2):113-122.(in Chinese)
MCMAHAN B, MOORE E, RAMAGE D, et al. Communication efficient learning of deep networks from decentralized data[M]∥Artificial Intelligence and Statistics. New York, NY, US:PMLR, 2017:1273-1282.
WEN J, ZHANG Z X, LAN Y, et al. A survey on federated learning:challenges and applications[J]. International Journal of Machine Learning and Cybernetics,2023,14(2):513-535.
周弈志,王军晓,谢鑫,等.联邦长尾学习研究综述[J].计算机学报,2025,48(4):779-807.
ZHOU Y Z, WANG J X, XIE X, et al.A survey on federated long-tailed learning[J].Chinese Journal of Computer,2025, 48(4):779-807.(in Chinese)
吴焯斌.基于联邦强化学习的车联网分布式任务卸载与资源分配研究[D].广州:华南理工大学,2023.
WU Z B.Research on distributed task off loading and resource allocation for internet of vehicles based on federated reinforcement learning[D].Guangzhou:South China University of Technology,2023.(in Chinese)
COLLINS L, HASSANI H, MOKHTARI A, et al. FedAvg with fine tuning:local updates lead to representation learning [J]. Advances in Neural Information Processing Systems,2022,35:10572-10586.
YUAN X T, LI P. On convergence of FedProx:local dissimilarity invariant bounds, non-smoothness and beyond: arXiv: 2206. 05187 [R]. Ithaca, NY, US: Cornell University, 2022:2206. 05187.
JOTHIMURUGESAN E, HSIEH K, WANG J Y, et al. Federated learning under distributed concept drift [C]∥Proceedings of International Conference on Artificial Intelligence and Statistics. New York, NY, US:PMLR,2023:5834-5853.
KANG M, K KIM S P, JIN K H, et al. FedNN:federated learning on concept drift data using weight and adaptive group normalizations[J]. Pattern Recognition,2024,149:110230.
XIANG Q Y, ZI L L, CONG X, et al. Concept drift adaptation methods under the deep learning framework:a literature review [J]. Applied Sciences,2023,13(11):6515.
CHEN S J, ZHANG Y, YANG Q. Multi-task learning in natural language processing: an overview: arXiv: 2109. 09138 [R]. Ithaca, NY, US:Cornell University,2021:2109. 09138.
HAO J F, CHEN P, CHEN J, et al. Multi task federated learning-based system anomaly detection and multi-classification for microservices architecture[J]. Future Generation Computer Systems,2024,159:77-90.
ZHUANG F Z, QI Z Y, DUAN K Y, et al. A comprehensive survey on transfer learning[J]. Proceedings of the IEEE,2020, 109(1):43-76.
YU S Z, MUÑOZ J P, JANNESARI A. Federated foundation models:Privacy-preserving and collaborative learning for large models: arXiv: 2305. 11414 [R]. Ithaca, NY, US: Cornell University,2023:2305. 11414.
ZHUANG W M, CHEN C, LI J T. When foundation model meets federated learning: motivations, challenges, and future directions: arXiv: 2306. 15546 [R]. Ithaca, NY, US: Cornell University,2023:2023:2306. 15546.
DRIESS D, XIA F, SAJJADI M S M, et al. PaLM-E: an embodied multimodal language model:arXiv 2303. 03378[R]. Ithaca, NY, US:Cornell University,2023:2303. 03378.
ZITKOVICH B, YU T, XU S, et al. RT-2:vision-language-action models transfer web knowledge to robotic control [C]∥Proceedings of Conference on Robot Learning. New York, NY, US:PMLR,2023:2165-2183.
KUFAKUNESU R, MYBURGH H, DE FREITAS A. The internet of battle things: a survey on communication challenges and recent solutions[J]. Discover Internet of Things,2025,5(1):3.
TANG H, CHEN Y, ALI I. Secure distributed model predictive control for heterogeneous UAV-UGV formation under dos attacks [J]. IEEE Transactions on Intelligent Vehicles,2025,10(5):3504-3516.
SU N C, LIU F, MASOUROS C. Sensing-assisted eavesdropper estimation:an ISAC breakthrough in physical layer security[J]. IEEE Transactions on Wireless Communications,2023,23(4):3162-3174.
YU W H, ZHAO J. Quantum multi-agent reinforcement learning as an emerging AI technology:a survey and future directions[C]∥Proceedings of 2023 International Conference on Computer and Applications. Cairo, Egypt:IEEE,2023:1-7.
WANG H, CHEN B B, ZHANG T Y, et al. Learning to communicate through implicit communication channels: arXiv:2411. 01553[R]. Ithaca, NY, US: Cornell University, 2024:2411. 01553.
HUANG K L, SHI B T, LI X, et al. Multi-modal sensor fusion for auto driving perception: a survey: arXiv: 2202. 02703 [R]. Ithaca, NY, US:Cornell University,2022:2202. 02703.
XIA Q, YE W S, TAO Z Y, et al. A survey of federated learning for edge computing:research problems and solutions[J]. High-Confidence Computing,2021,1(1):100008.
LI S T, TANG H. Multimodal alignment and fusion:a survey:arXiv:2411.17040 [R]. Ithaca, NY, US:Cornell University, 2024:2411. 17040.
FILHO C P, MARQUES JR E, CHANG V, et al. A systematic literature review on distributed machine learning in edge computing[J]. Sensors,2022,22(7):2665.
HAN Z Y, LIU X Q, HAO J. LLaVA-GM: lightweight LLaVA multimodal architecture [J]. Frontiers in Computer Science, 2025,7:1626346.
QI J J, ZHOU Q H, LEI L, et al. Federated reinforcement learning: techniques, applications, and open challenges: arXiv:2108. 11887 [R]. Ithaca, NY, US: Cornell University, 2021:2108. 11887.
PINTO NETO E C, SADEGHI S, ZHANG X C, et al. Federated reinforcement learning in IoT: applications, opportunities and open challenges[J].Applied Sciences,2023,13(11):6497.
张浩然,李君,邢立宁,等.大模型与智能优化算法集成研究综述[J].控制与决策,https:∥doi.
org/10.13195/kzy-jc.2025.0121.ZHANG H R, LI J, XING L N, et al.A research review on the integration of large models and intelligent optimization algorithms [J].Control and Decision, https: ∥doi.org/10.-13195/kzyjc.2025.0121.(in Chinese)
韦炎炎,毛天一,李柏昂,等.视觉模型及多模态大模型推进图像复原增强研究进展[J].中国图象图形学报,2025, 30(5):1197-1219.
WEI Y Y, MAO T Y, LI B A, et al.Visual and large multimodal models promote image restoration and enhancement: research progress[J].Journal of Image and Graphics,2025,30(5):1197-1219.(in Chinese)
European Commission. Proposal for a regulation laying down harmonised rules on Artificial Intelligence(Artificial Intelligence Act)[R]. Brussels, Belgium:European Commission,2021.
U. S. Department of Defense. Directive 3000. 09: autonomy in weapon systems[R]. Washington, D. C. ,US:DoD,2023.
NATO. Summary of the responsible use of artificial intelligence in defence principles[R]. Brussels, Belgium:NATO,2021.
BODE I, HUELSS H, NADIBAIDZE A, et al. Prospects for the global governance of autonomous weapons:comparing Chinese, Russian, and US practices [J]. Ethics and Information Technology,2023,25:5.
LONGPRE S, STORM M, SHAH R. Lethal autonomous weapons systems & artificial intelligence:trends, challenges, and policies [J]. MIT Science Policy Review,2022,3:1-15.
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