曾照洋 , 彭文胜 , 李云凯 , 等 . 智能无人机集群可靠性技术内涵、发展及挑战 [J ] . 兵工学报 , 2025 , 46 ( 3 ): 240322 . DOI: 10.12382/bgxb.2024.0322 http://doi.org/10.12382/bgxb.2024.0322 随着无人机相关技术的快速发展,智能无人机集群得到越来越多的研究。智能无人机集群应用自然界集群组织算法模型,并结合其他智能技术,形成高级群体智能行为,具有传统单人工智能体无法比拟的优势,但是可靠性问题也是制约其面向大规模应用的一个重要因素。系统分析智能无人机集群的概念、特征及技术发展路径,基于智能无人机集群的应用场景、应用模式及行为过程,探讨智能无人机集群的可靠性内涵,构建无人机集群可靠性技术框架,并结合智能无人机集群的技术特征和应用需求,给出智能无人机集群可靠性技术发展的技术途径与建议。最终在“1个核心,2种能力,N个基础”的可靠性内涵下,提出由可靠性指标、可靠性研制验证以及可靠性维持保障技术组成的智能UAVS可靠性技术框架。

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李洋军 , 黄琦龙 , 杨力 , 等 . 数据和知识双驱动的空中集群目标作战意图识别 [J ] . 兵工学报 , 2025 , 46 ( 2 ): 240113 . DOI: 10.12382/bgxb.2024.0113 http://doi.org/10.12382/bgxb.2024.0113 针对集群目标空间特性多元时变和传统数据驱动模型过分依赖经验样本等问题,提出一种针对集群目标的数据和知识双驱动作战意图识别方法。考虑集群目标空间形态等编队特点,构造基于目标编队外包络线和最小外接矩形的集群特征向量,增强敌情数据的特征表达效果;建立基于专家经验的知识模型和结合注意力机制的长短期记忆(Long short-term memory,LSTM)网络模型,基于专家经验的知识模型根据约束规则生成意图预识别向量,LSTM模型预测输出意图概率分布的残差;利用一种可学习的残差估计器结构,自适应调整双模型的融合比率,并设计多目标损失函数控制双模型的影响权重,最终通过双模型的融合有效克服传统数据模型高精度和数据样本不足的矛盾。实验表明,提出方法的精度相比LSTM和Attention-LSTM分别提升约5.34%和4.98%,且对样本量的依赖性显著低于传统数据驱动方法。

LI Y J , HUANG Q L , YANG L , et al. Combat intention recognition of air cluster targets driven by data and knowledge [J ] . Acta Armamentarii , 2025 , 46 ( 2 ): 240113 . (in Chinese) DOI: 10.12382/bgxb.2024.0113 http://doi.org/10.12382/bgxb.2024.0113 Aiming at the diverse spatiotemporal characteristics of cluster targets and the excessive reliance of traditional data driven models on empirical samples,this paper proposes an algorithm for combat intent recognition driven by both data and knowledge.A cluster feature vector based on the virtual envelope and minimum bounding rectangle of target formation are constructed to enhance the feature expression of enemy situation data,which takes the cluster characteristics,such as the spatial form of cluster targets,into account.A knowledge model based on military expert experience and a long short-term memory (LSTM) network model with attention mechanism are established then.The knowledge model generates the intent pre-recognition vectors based on constraint rule,while the LSTM network model predicts the residual of intent probability distribution.The fusion ratio of both models is adaptively adjusted by utilizing a learnable residual estimator structure.A multi-objective loss function is designed to control the influence weights of the dual models.Ultimately,the fusion of the dual models overcomes the contradiction between the high accuracy of traditional data models and the insufficient data samples.Experimental results indicate that the proposed method improves the recognition accuracy to about 5.34% and 4.98% compared to LSTM and Attention-LSTM,respectively,and has significantly lower dependence on sample size than traditional data-driven methods.

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薛建儒 , 房建武 , 吴俊 , 等 . 多机协同智能发展战略研究 [J ] . 中国工程科学 , 2024 , 26 ( 1 ): 101 - 116 . DOI: 10.15302/J-SSCAE-2024.01.013 http://doi.org/10.15302/J-SSCAE-2024.01.013 多个自主智能系统通过信息、行为交互构成的多机协同智能，代表着未来智能系统的必然发展趋势，是我国新一代人工智能规划部署的主攻方向，也是支撑国防、社会安全的核心技术和推动制造业由大到强的必由之路。开展突破多机协同智能技术发展研究，对于推动我国军事智能、智能产业高质量发展、加快工业转型升级具有重要意义。本文基于多机协同智能系统当前面临的难以适应复杂任务这一挑战，从基础理论和核心关键技术两个层面出发，系统地梳理了多机协同智能的研究现状，分析了制约基础理论与关键技术发展的主要瓶颈性问题，并以多机协同智能制造为典型应用，剖析理论与技术发展中存在的问题。研究认为，多机协同智能将朝着人机群组智能的方向发展，为抢占发展先机，需及早布局人机群组智能的基础理论探索，加速核心技术突破，并加快应用示范。

XUE J R , FANG J W , WU J , et al. Collaborative multiple autonomous systems [J ] . Strategic Study of CAE , 2024 , 26 ( 1 ): 101 - 116 . (in Chinese) DOI: 10.15302/J-SSCAE-2024.01.013 http://doi.org/10.15302/J-SSCAE-2024.01.013 Collaborative intelligence formed via information and behavioral interactions of multiple autonomous systems is an inevitable trend of future intelligent systems. It is a focus of planning of the next-generation artificial intelligence in China and is crucial for supporting national security and strengthening the manufacturing industry. Research aimed at overcoming bottlenecks regarding collaborative multiple autonomous systems will significantly aid the advancement of intelligent industries and accelerate industrial transformation and upgrading in China. Focusing on the challenge that collaborative multiple autonomous systems cannot adapt to complex tasks, this study thoroughly analyzes the research status and major bottlenecks of collaborative multiple autonomous systems from the aspects of fundamental research and engineering. Using multi-robot collaborative intelligent manufacturing as an example, we provide an in-depth analysis of relevant theoretic and technical problems. Our research indicates that collaborative multiple autonomous systems will inevitably evolve toward human ‒ machine teaming. To master this opportunity, it is critical to proactively lay the groundwork for the theoretical exploration and technological breakthroughs of human‒machine teaming and to conduct exemplary applications.

段海滨 , 梅宇 , 赵彦杰 , 等 . 2023年无人机热点回眸 [J ] . 科技导报 , 2024 , 42 ( 1 ): 217 - 231 . DOI: 10.3981/j.issn.1000-7857.2024.01.014 http://doi.org/10.3981/j.issn.1000-7857.2024.01.014 2023年,无人机技术方面实现了革新和突破,展现出了引人瞩目的技术创新进展和广泛的应用态势。从无人机管控政策、无人机技术革新、无人机关键技术、无人机集群验证、反无人机技术等多个角度全方位分析了2023年无人机的科技热点。随着视觉应急避障技术、智能自主飞行能力的提升,无人机性能和功能均得到了显著提升。随着无人机的不断普及和应用,无人机集群的关键技术、作战样式和平台建设也不断完善,行业定制化、自主智能化、跨域实战化依然是无人机未来发展的重要方向。

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苏波 , 江磊 , 刘宇飞 , 等 . 移动机器人跨域跃质关键技术综述 [J ] . 兵工学报 , 2023 , 44 ( 9 ): 2556 - 2567 . DOI: 10.12382/bgxb.2023.0414 http://doi.org/10.12382/bgxb.2023.0414 人类探索陆域、天域、空域、海域等空间的脚步从未停止,该过程蕴含了多域-多介质环境的复杂科学问题。移动机器人是实现跨域跃质环境下运动能力的典型代表,未来将走向更广的陆面、更高的蓝天、更远的深空、更深的海底,对移动机器人能够在多域-多介质环境中始终保持高性能提出了挑战。介绍了移动机器人在能量域-时空域-信息域的科学融合,及其在探索新域新质道路上所发挥的越来越重要的作用。归纳了移动机器人在支撑与牵引技术、速度与稳定技术、交互与协同技术等方面的基础理论研究应用及国内外发展现状。对移动机器人学科重点解决的多域-多介质机器人既有机动性和作业智能性问题提出建议,以期能够完成人类所不可及的任务,真正实现机器人装备体系在多域-多介质环境下的广泛应用。

SU B , JIANG L , LIU Y F , et al. A review of key technologies for cross-domain and trans-medium of mobile robotics [J ] . Acta Armamentarii , 2023 , 44 ( 9 ): 2556 - 2567 . (in Chinese) DOI: 10.12382/bgxb.2023.0414 http://doi.org/10.12382/bgxb.2023.0414 Human exploration of terrestrial, aerial, spatial, and aquatic environments has never ceased, and this process involves complex scientific problems related to multi-domain and multi-medium environments.As typical examples of machines capable of crossing different environments, mobile robots are expected to explore broader land, higher sky, farther space, and deeper seabed in the future but this presents a significant challenge for mobile robots to maintain high performance in multi-domain and multi-medium environments.This paper first introduces the scientific fusion of mobile robots in energy, space-time, and information domains, highlighting their increasingly important role in exploring new domains and media.Then, it summarizes the basic theoretical research, applications, and development status of mobile robots in areas such as supporting and traction technology, speed and stability technology, and interaction and collaborative technology.Finally, suggestions are proposed to address the key challenges related to the maneuverability and operational intelligence of multi-domain and multi-medium mobile robots.The aim is to enable these machines to perform tasks that are beyond human reach, and to facilitate the widespread application of robot equipment systems in multi-domain and multi-medium environments.

陈军 , 徐嘉 , 高晓光 . 基于ABFCM模型框架的UCAV自主攻击决策 [J ] . 系统工程与电子技术 , 2017 , 39 ( 3 ): 549 - 556 .

CHEN J , XU J , GAO X G . Autonomous attack decision-making of UCAV based on ABFCM model framework [J ] . Systems Engineering and Electronics , 2017 , 39 ( 3 ): 549 - 556 . (in Chinese) DOI: 10.3969/j.issn.1001-506X.2017.03.14 http://doi.org/10.3969/j.issn.1001-506X.2017.03.14 <p>In order to exert the autonomy of unmanned combat aerial vehicle&rsquo;s(UCAV) decision-making and relieve the operator&rsquo;s task burden, based on the agentbased fuzzy cognitive map(ABFCM) model framework, the three-level UCAV&rsquo;s autonomous attack decision-making model is designed, which contains information processing, decision reasoning and human-vehicle interaction. The contents of UCAV&rsquo;s autonomous attack decision-making are decomposed into agents of different levels and effects. On the basis of respectively constructing the causal inference model of every agent, the whole decision inference behavior is organized legitimately and orderly. Simulation analysis proves the model&rsquo;s rationality and feasibility, simultaneously it demonstrates that the autonomous attack decisionmaking model will help UCAV adapt to the increasingly complex battle-field environment, and enhance the independent combat capability under the supervisory control mode.</p>

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CAVALIERE D , SENATORE S , LOIA V . Proactive UAVs for cognitive contextual awareness [J ] . IEEE Systems Journal , 2019 , 13 ( 3 ): 3568 - 3579 . DOI: 10.1109/JSYST.2018.2817191 http://doi.org/10.1109/JSYST.2018.2817191 Unmanned vehicle systems are often teleoperated, semiautonomous, and strictly dependent on human operators. In complex and dynamic environments, unmanned vehicles should be autonomous in a stricter sense, which means they should exhibit a human-like behavior to he capable of accurately perceiving the environment; understanding the situation, locating and interacting with environmental elements; and reporting solutions to humans. In order to address these desiderata, a modeling of a proactive, context-aware unmanned system is presented. Precisely, the system framework is designed for an unmanned aerial vehicle (UAV) that flies over an area, and collects data in the form of video frames, sensor values, etc. It recognizes situations, senses scene object and environment data, acquires the awareness about the evolving scenes, and, finally, takes a decision based on the perception of the overall scenario. The system design is based on two primary building blocks: 1) the semantic web technologies that provide the high-level object description in the tracked scenario, and 2) the fuzzy cognitive map model that provides the cognitive accumulation of spatial knowledge in order to discern specific situations that need a decision. Although the paper presents a UAV-based surveillance system model, its applicability is shown based on a realistic case study (viz., broken car on the highway); moreover, several possible scenario configurations have been simulated to assess the criticality level perceived by the system (UAV) in a given situation and to validate the effective response/decision in the case of critical situations.

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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 http://doi.org/10.12382/bgxb.2023.0514 现代战争模式催生了无人机蜂群新型作战样式。系统分析了无人机蜂群利用功能简单的个体,应用自然界蜂群组织算法模型形成高级群体智能行为的概念,介绍无人蜂群的分类和特点,凝练总结无人机蜂群协同组网、协同感知、协同决策、协同控制等关键领域技术及发展现状,针对复杂对抗环境下的实际需求提出集光电探测、通信组网、协同控制、任务规划等多学科于一体的无人机蜂群技术发展的重点和难点,给出无人机蜂群技术发展的途径和举措建议,期望达到牵引无人机蜂群技术发展方向、推动无人机蜂群从技术走向实用的目的。

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 http://doi.org/10.12382/bgxb.2023.0514 Modern warfare modes have given birth to the drone swarms as a new type of operational pattern. The concept of forming the advanced group intelligent behavior obtained by using drones with simple functions, which applies the natural bee swarm organization algorithm,is systematically analyzed. The classification and characteristics of drone swarm are introduced. The key technologies and development status of collaborative networking, sensing, decision-making and control are summarized. Focusing on the practical requirements under complex countermeasure environments, the key points and difficulties in the development of unmanned drone swarm technology which integrates electro-optic detection, communication networking, collaborative control and task planning into one are presented. The development approaches and measures of the drone swarm technologies are also put forward, hoping to achieve the goal of leading the development direction of unmanned drone swarm technology and promoting the corresponding technologies to practical applications.

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 http://doi.org/10.12382/bgxb.2022.1181 针对无人机蜂群中自主协同行为涌现机理难以解释的问题,提出一种多Agent系统中的自主协同行为涌现的分析方法,对系统的微观个体层、中观结构层和宏观网络层三个层面展开分析,自底向上地量化分析系统动态演化过程,揭示系统从微观到宏观的内在逻辑以及系统演变中的一些问题。通过计算实验方法构建无人机蜂群的计算实验模型,根据蜂群作战的关键特征设计无人机蜂群社团信息网络,引入公共物品博弈机制构建蜂群合作演化模型,并给出社团网络上蜂群的演化动力学过程。通过数值模拟,从无人机蜂群系统的不同层面量化分析蜂群协同行为的涌现现象,从而认识蜂群自主协同行为的涌现机理,并为无人机蜂群协同机制的优化提供决策支持。

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 http://doi.org/10.12382/bgxb.2022.1181 Understanding the emergence of autonomous cooperative behavior in UAV swarms is a challenging problem. In this study, an analysis method for studying the autonomous cooperative behavior emergence in multi-agent systems is proposed. The method analyzes three levels of the system: the micro-individual layer, the meso-structure layer, and the macro-network layer. It also quantifies the dynamic evolution process of the system from the bottom up, revealing the internal logic of the system from micro to macro and identifying problems in system evolution. The computational experimental model of UAV swarms is also introduced, and the information network of UAV swarm association is designed according to the key features of swarm operations. The game mechanism for public goods is introduced, and the cooperative evolution model of swarms is constructed. The evolution dynamics of swarms on the association network is revealed. Through numerical simulation, the emergence of swarm cooperative behaviors is quantitatively analyzed from different levels of the UAV swarm system, so as to understand the emergence mechanisms of autonomous and cooperative swarm behaviors and provide decision support for the optimization of UAV swarm cooperative mechanisms.

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陈军 , 梁晶 , 程龙 , 等 . 基于FCM的多无人机协同攻击决策建模方法 [J ] . 航空学报 , 2022 , 43 ( 7 ): 325526 . DOI: 10.7527/S1000-6893.2021.25526 http://doi.org/10.7527/S1000-6893.2021.25526 针对复杂不确定战场环境下多无人机协同任务的需求,提出了一种基于模糊认知图(FCM)及其扩展模型的多无人机协同攻击决策建模方法。基于人的决策心智模式,采用智能体模糊认知图(ABFCM)建立了包含感性和理性2种决策模式的多无人机协同攻击决策系统模型框架。采用模糊灰色认知图(FGCM)对多无人机的态势感知和协同攻击决策开展了建模工作。借鉴人脑杏仁核机理建立了态势-决策模板快速匹配的感性攻击决策模型。为降低建模工作对专家知识的依赖,采用直觉模糊集的决策阈值算法提高理性攻击决策模型的客观性,并采用动量梯度下降(MGD)学习算法进一步提高了决策模型的学习进化能力。通过仿真验证分析表明,基于FCM的多无人机协同攻击决策建模方法能够较好地应对复杂不确定战场环境,发挥知识和数据在建模中的综合作用,可为提升多无人机执行任务的决策优势提供理论指导和建模方法参考。

CHEN J , LIANG J , CHENG L , et al. Cooperative attack decision modeling method of multiple UAVs based on FCM [J ] . Acta Aeronautica et Astronautica Sinica , 2022 , 43 ( 7 ): 325526 . (in Chinese) DOI: 10.7527/S1000-6893.2021.25526 http://doi.org/10.7527/S1000-6893.2021.25526 According to the requirements for multi-UAV cooperative mission in the complex and uncertain battlefield environment, a decision-making modeling method of multi-UAV cooperative attack based on the Fuzzy Cognitive Map (FCM) and its extended model are proposed. Based on the human decision-making mental model, the model framework of the multi-UAV cooperative attack decision-making system including the perceptual and rational decision-making modes is established by using the Agent-Based Fuzzy Cognitive Map (ABFCM). The Fuzzy Grey Cognitive Map (FGCM) is used to model the situation awareness and cooperative attack decision of multi-UAVs. Based on the amygdala mechanism of human brain, a perceptual attack decision model for quick matching of situation and decision template is established. To reduce the dependence of modeling work on expert knowledge, a rational attack decision model is established based on the decision threshold algorithm of intuitionistic fuzzy sets, and the learning and evolution ability of the decision model is further improved by using the Momentum Gradient Descent (MGD) learning algorithm. The simulation results show that the method proposed can better cope with the complex and uncertain battlefield environment, give full play to the comprehensive role of knowledge and data in modeling, and provide theoretical and modeling guidance for improving the decision-making advantages in mission execution by multi-UAVs.

陈军 , 张岳 , 陈晓威 , 等 . 基于模糊灰色认知图的复杂战场智能态势感知建模方法 [J ] . 兵工学报 , 2022 , 43 ( 5 ): 1093 - 1106 . DOI: 10.12382/bgxb.2021.0259 http://doi.org/10.12382/bgxb.2021.0259 针对复杂战场环境的动态、不确定性特征，提出一种基于模糊灰色认知图（FGCM）的智能态势感知（SA）建模方法。基于SA理论，采用自上而下任务驱动的态势觉察方式实现态势元素的提取；以目标威胁评估为态势理解的建模对象，利用FGCM在不确定数据表达和推理的模型特性，同时引入外部环境控制节点建立威胁评估动态FGCM模型。以目标意图预测为态势预测的建模对象，在基于专家知识建立的FGCM模型结构基础上，采用粒子群优化算法提高了意图预测模型对历史数据样本的参数学习能力。仿真验证分析结果表明，基于FGCM的智能SA建模方法能够较好地应对动态、不确定的战场环境，发挥知识和数据在建模中的综合作用。

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陈军 , 张新伟 , 徐嘉 , 等 . 有人/无人机混合编队有限干预式协同决策 [J ] . 航空学报 , 2015 , 36 ( 11 ): 3652 - 3665 . DOI: 10.7527/S1000-6893.2015.0085 http://doi.org/10.7527/S1000-6893.2015.0085 针对Leader-Follower异构型有人/无人机混合编队协同决策系统具有递阶分布式决策结构、决策信息分散以及通信约束等特点,在智能体模糊认知图(ABFCM)和动态模糊认知图(DFCM)的理论基础上,提出了一种有限干预式协同决策机制。通过构建层次化的Follower平台自主决策模型,实现了该平台与外部系统良好的交互能力,体现了自主决策的"动态性"。通过设计Leader平台的3种干预策略,满足了不同层次的决策需求,体现了干预过程的"有限性"。仿真结果表明:有限干预协同决策模型能够适应外部环境的动态变化,充分发挥Follower平台的自主决策能力;而不同层次的有限干预介入既减轻了Leader平台的控制负荷,又保证了决策的有效性和可行性,可为解决其他同类复杂系统的协同决策问题提供理论依据和方法参考。

CHEN J , ZHANG X W , XU J , et al. Human/unmanned-aerial-vehicle team collaborative decision-making with limited intervention [J ] . Acta Aeronautica et Astronautica Sinica , 2015 , 36 ( 11 ): 3652 - 3665 . (in Chinese) DOI: 10.7527/S1000-6893.2015.0085 http://doi.org/10.7527/S1000-6893.2015.0085 In view of the characteristics of Leader-Follower heterogeneous multi-platform system for the human/unmanned aerial vehicle team which are hierarchical and distributed structure, decision-making information decentralization and communication constraints, the collaborative decision-making mechanism with limited intervention based on the combination of agent-based fuzzy cognitive map (ABFCM) and dynamic fuzzy cognitive map (DFCM) is presented. In the proposed approach, hierarchical autonomous decision-making model for Follower platform is developed in order to make the Follower able to interact with external environment well and reflect the autonomous decision-making dynamics. Three intervention strategies under different conditions are designed for Leader platform to meet the requirements of different decision levels. Meanwhile these strategies reflect the limitation of intervention processes. Simulation results show that intervention-limited collaborative decision-making mechanism can adapt to dynamic changes in the external environment and make full use of Follower platform's autonomous decision-making ability. Hierarchical limited interventions can reduce the control workload of Leader and ensure the effectiveness and feasibility of decision-making as well. This work also provides a theoretical basis and methodology support to solve other similar collaborative decision-making problem in complex systems.

CHEN J , QIU X J , RONG J , et al. Design method of organizational structure for MAVs and UAVs heterogeneous team with adjustable autonomy [J ] . Journal of Systems Engineering and Electronics , 2018 , 29 ( 2 ): 286 - 295 . DOI: 10.21629/JSEE.2018.02.09 http://doi.org/10.21629/JSEE.2018.02.09 The increasingly complex battlefield environment requests much closer connection in a team having both manned and unmanned aerial vehicles (MAVs and UAVs). This special heterogeneous team structure causes demands for effective organizational structure design solutions. Implementing adjustable autonomy in the organizational structure, the expected evaluation function is established based on the physical resource, intelligent resource, network efficiency, network vulnerability and task execution reliability. According to the above constraints, together with interaction latency, decision-making information processing capacity, and decision-making latency, we aim to find a preferential organizational structure. The proposed organizational structure includes cooperative relationships, supervisory control relationships, and decision-making authorization relationships. In addition, by considering the influence on the intelligent support capabilities and the task execution reliability created by adjustable autonomy, it helps to build the proposed organizational structure designed with certain degree of flexibility to deal with the potential changes in the unpredictable battlefield environment. Simulation is conducted to confirm our design to be valid. And the method is still valid under different battlefield environments and interventions.

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钟赟 , 姚佩阳 , 张杰勇 , 等 . 基于HFCM的有人-无人机作战系统交互式协同决策 [J ] . 系统工程理论与实践 , 2021 , 41 ( 10 ): 2748 - 2760 . DOI: 10.12011/SETP2020-0283 http://doi.org/10.12011/SETP2020-0283 针对有人-无人机作战系统中的交互式协同决策问题，本文基于模糊认知图（fuzzy cognitive map，FCM）理论，提出一种基于混合模糊认知图（hybrid fuzzy cognitive map，HFCM）的决策方法.首先，分析现有有人-无人机交互式协同决策模式研究的不足，在相关研究理论基础上，建立交互式协同决策框架，包括无人机自主决策和有人机干预决策（环境干预、知识干预和推理干预）.然后，结合基于规则的模糊认知图（rule-based fuzzy cognitive map，RBFCM）和直觉模糊认知图（intuitionistic fuzzy cognitive map，IFCM），建立交互式协同决策HFCM模型：在武器系统检测阶段，基于RBFCM推理得到武器发射条件；在攻击态势评估阶段，基于IFCM进行动态推理得到环境干预结果，并设计相应的知识和推理干预策略.最后，通过一组典型案例，验证了本文方法的有效性.

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向锦武 , 董希旺 , 丁文锐 , 等 . 复杂环境下无人集群系统自主协同关键技术 [J ] . 航空学报 , 2022 , 43 ( 10 ): 527570 . DOI: 10.7527/S1000-6893.2022.27570 http://doi.org/10.7527/S1000-6893.2022.27570 在高动态、不确定、资源受限等复杂环境下，无人集群系统执行协同区域搜索、集群优化调度等任务将会面临"感知—判断—决策—行动（OODA）"回路各个领域的挑战。为了提升无人集群系统的任务场景适应能力，必须突破复杂环境下无人集群系统自主协同关键技术。以复杂环境下大规模异构无人集群鲁棒自主协同理论为基础，探讨了复杂环境下无人集群系统自适应异构体系架构设计与建模方法，梳理了复杂环境下高维态势分布式感知与认知、可引导、可信任、可进化的智能决策、复杂环境下无人集群系统自主协同控制3个科学问题。首先综述了复杂环境下无人集群系统自主协同的研究进展；其次，分析了无人集群系统OODA任务回路面临的挑战；然后，初步梳理了复杂环境下无人集群系统自主协同涉及的各项关键技术及其进展；最后，给出了无人集群系统自主协同领域未来发展的思考。

XIANG J W , DONG X W , DING W R , et al. Key technologies for autonomous cooperation of unmanned swarm systems in complex environments [J ] . Acta Aeronautica et Astronautica Sinica , 2022 , 43 ( 10 ): 527570 . (in Chinese) DOI: 10.7527/S1000-6893.2022.27570 http://doi.org/10.7527/S1000-6893.2022.27570 In complex environments with high dynamics, uncertainty and resource constraints, the unmanned swarm system will face challenges in all fields of the "Observation-Orientation-Decision-Action (OODA)" loop when performing complicated tasks such as collaborative area search and swarm optimal scheduling. To improve the adaptability of unmanned swarm systems to different scenarios, it is necessary to break through the key technologies for autonomous cooperation of unmanned swarm systems in complex environments. Based on the theory of robust autonomous cooperation of large-scale heterogeneous unmanned swarm systems in complex environments, this paper gives a review of the design and modeling methods of adaptive heterogeneous architecture for unmanned swarm systems, and discusses three problems:high-dimensional situation distributed perception and cognition, intelligent decision-making with guiding, trusting and evolving ability, and autonomous cooperative control of the unmanned swarm system in complex environments. Firstly, the research progress of autonomous cooperation of unmanned swarm system in complex environment is summarized. Secondly, the challenges faced by OODA task loop of unmanned swarm system are analyzed. Then, the key technologies involved in autonomous cooperation of unmanned swarm system in complex environment and their progress are reviewed. Finally, the future development of autonomous cooperation of unmanned swarm system is given.

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