基于NetLogo的多智能体空战模拟  

Multi-agent air combat simulation based on NetLogo

作　　者：贾宏刚 王伟[1] 承楠 JIA Honggang;WANG Wei;CHENG Nan(School of Communication Engineering,Xidian University,Xi'an 710071,China)

机构地区：[1]西安电子科技大学通信工程学院,陕西西安710071

出　　处：《指挥控制与仿真》2025年第2期132-140,共9页Command Control & Simulation

摘　　要：计算机模拟是未来智能空战研究的关键途径,但现有的空战模拟系统往往存在不开源、开发难度大、可视化效果差以及难以融合先进人工智能技术等问题,限制了智能空战的深入研究。提出了一种基于NetLogo 3D平台和HubNet模块的3D空战模拟系统。首先在NetLogo 3D环境中构建包含地形、飞机和导弹的静态模型,并通过封装函数实现飞机机动和导弹攻击等动态行为。系统不仅支持专家算法,还通过Python扩展引入了DDQN强化学习算法,实现智能体的机动及攻击决策。利用HubNet模块构建了具有C-S架构的空战环境,支持人人对抗、人机对抗和机机对抗多种形式的模拟。实验结果验证了系统的有效性和稳定性,同时实现了实时可视化功能并展现出快速集成智能算法的技术优势。Computer simulation is a crucial approach for advancing research in intelligent aerial combat.However,existing aerial combat simulators are often non-open-source,challenging to develop,poorly visualized,and difficult to integrate with advanced AI technologies.This paper introduces a 3D aerial combat simulation system based on NetLogo 3D and HubNet.The system constructs static models of terrain,aircraft,and missiles in NetLogo 3D,and encapsulates functions to implement dynamic behaviors such as aircraft maneuvers and missile attacks.The system not only supports expert algorithms but also integrates DDQN reinforcement learning algorithm via Python extensions,enabling intelligent agents to make maneuver and attack decisions.A C-S architecture is employed via HubNet to support various simulation scenarios,including human-human,human-machine,and machine-machine engagements.Experimental results validate the system's effectiveness and stability,highlighting its real-time visualization capabilities and rapid integration of AI algorithms.

关 键 词：空战模拟 HubNet C-S架构 DDQN算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.9[自动化与计算机技术—控制科学与工程]