摘要
集群无人系统是近年来国内外军事领域的研究重点,正在推动无人作战样式由"单平台遥控作战"向"智能集群作战"发展,支撑作战系统在不确定任务和环境下具备协同、自主、灵活的特性.集群的整体性能取决于其成员系统及成员之间的相互关系,且随时间、环境变化而动态演化,系统间交互涌现出新智能.本文从集群无人系统结构演化机理入手,构建集群无人系统从底层链路到集群系统再到任务需求的三级结构与关系模型,并用图神经网络将多维空间关系模型转化为二维的图表示模型,构建出集群无人系统中系统之间以及层级之间的关系依赖图.整个图网络以任务为标准分类,提出了用递归神经网络描述层内关系和层间关系的方法,并给出了实现算法,利用训练数据集基于任务的节点属性标签,对集群无人系统的结构进行预测.以此为基础,可以进一步实现对结构依赖关系的权重参数学习,得到系统或链路损坏对任务层的影响,实现集群无人系统从作战任务到集群结构的自主决策.
In recent years, swarm unmanned systems(SUSs) have become crucial in the military field, both at home and abroad. This has promoted the evolution of the unmanned combat mode from single-platform remotecontrol to intelligent-swarm combat. SUSs support the cooperative, autonomous, and flexible characteristics of the combat system under uncertain tasks and environments. The overall swarm performance depends on the system and structure among its members and also dynamically evolves with the time and the environment. Thus,new intelligence emerges from the interaction among systems. Starting from the evolution of the SUS structure,this paper proposes the model of a three-layer structure and a relationship involving the data-link layer, the SUS,and the task requirements. The multidimensional spatial relationship model is transformed into a two-dimensional graphical representation model by using a graph neural network;then, the dependency graph of the relationships of the systems and layers of the SUS is constructed. The integral network is classified according to a task-based standard. The recursive neural network algorithm is derived from the intra-and interlayer relationships. The SUS structure is predicted via some examples of training data sets and the attribute labels of task-based nodes. The impact of the system or data layer damage can be evaluated according to the weight parameters of the structure dependence relationship. Finally, the autonomous decision of the SUS from the task to the swarm structure is realized.
作者
张婷婷
宋爱国
蓝羽石
Tingting ZHANG;Aiguo SONG;Yushi LAN(College of Command and Control Engineering,Army Engineering University of PLA,Nanjing 210017,China;College of Instrumental Science and Engineering,Southeast University,Nanjing 210017,China;The 28th Research Institute of China Electronics Technology Group Corporation,Nanjing 210017,China)
出处
《中国科学：信息科学》
CSCD
北大核心
2020年第3期347-362,共16页
Scientia Sinica(Informationis)
基金
国家自然科学基金(批准号:61802428,61906086)
中国博士后科学基金(批准号:2019M651991)
军委科技委国防科技项目基金(批准号:3602026)
陆军装备科研项目(批准号:KYZYJWJK1702)资助。
关键词
集群无人系统
自适应结构
图神经网络
演化
智能涌现
结构预测
swarm unmanned system
adaptive structure
graph neural network
evolution
intelligent emergence
structure prediction
