ID 原文 译文
56978 因此,本文对基于群体智能的多无人机空战系统进行了研究. Therefore, this paper presentsa multi-UAV air combat system based on swarm intelligence.
56979 针对多无人机协同飞抵空战场并完成作战任务的问题,本文对飞机的空气动力学模型和飞机路径上的威胁区域进行了建模,并利用蚁群算法完成了无人机飞抵空战场的航迹规划. Considering the problem of multi-UAV cooperativearrival at the air battlefield and the accomplishment of combat tasks, the aerodynamic model of the aircrafts andthe threat area on the path to the battlefield are modeled; the path planning is completed through an ant colonyalgorithm.
56980 在单无人机有限状态机控制算法的基础上,结合多无人机协同,提出了一种多无人机自主控制算法以提高无人机集群在空战中的成功率. Based on the control algorithm of single-UAV finite-state machine and the cooperation of multipleUAVs, an autonomous control algorithm for multi-UAV systems is proposed to improve the success rate of UAVclusters in air combat.
56981 本文还搭建了一套仿真平台,对所设计算法的有效性进行了相关测试. The effectiveness of the proposed algorithm is tested with a simulation platform
56982 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. In recent years, swarm unmanned systems (SUSs) have become crucial in the military field, both athome and abroad. This has promoted the evolution of the unmanned combat mode from single-platform remote?control to intelligent-swarm combat. SUSs support the cooperative, autonomous, and flexible characteristics ofthe combat system under uncertain tasks and environments.
56983 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. The overall swarm performance depends on thesystem and structure among its members and also dynamically evolves with the time and the environment. Thus,new intelligence emerges from the interaction among systems.
56984 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. 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-dimensionalgraphical representation model by using a graph neural network; then, the dependency graph of the relationshipsof the systems and layers of the SUS is constructed.
56985 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. The integral network is classified according to a task-basedstandard. The recursive neural network algorithm is derived from the intra- and interlayer relationships. The SUSstructure is predicted via some examples of training data sets and the attribute labels of task-based nodes. Theimpact of the system or data layer damage can be evaluated according to the weight parameters of the structuredependence relationship.
56986 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. Finally, the autonomous decision of the SUS from the task to the swarm structure isrealized.
56987 互联网技术的发展对软件开发技术、运行形态和服务模式都产生了前所未有的影响, 以开源和众包为代表的大规模群体协作实践所蕴含的群体智能机理为网络时代的软件开发带来重大启示. The evolution of Internet has produced profound impacts on software development technologies, operation forms, and service models. In particular, the underlying crowd intelligence mechanisms in large-scale crowdcollaboration practices, such as open sourcing and crowdsourcing, have greatly inspired the software development.