一种多分辨率仿真模型构建方法

doi:10.16182/j.issn1004731x.joss.21-0778

系统仿真学报 ›› 2023, Vol. 35 ›› Issue (1): 202-211.doi: 10.16182/j.issn1004731x.joss.21-0778

一种多分辨率仿真模型构建方法

刘兆鹏¹(), 徐新海¹, 袁博文¹, 张金璐²()

^1.军事科学院战争研究院，北京 100091
^2.军事科学院军队政治工作研究院，北京 100091

收稿日期:2021-07-30 修回日期:2021-11-03 出版日期:2023-01-30 发布日期:2023-01-18
通讯作者: 张金璐 E-mail:scs_pengpeng@126.com;jinlu0724@163.com
作者简介:刘兆鹏(1981-)，男，副研究员，博士，研究方向为运筹分析与军事智能决策。E-mail：scs_pengpeng@126.com

A Multi-resolution Simulation Modeling Method

Zhaopeng Liu¹(), Xinhai Xu¹, Bowen Yuan¹, Jinlu Zhang²()

^1.War Studies Institute, The Academy of Military Science, Beijing 100091, China
^2.Military Political Work Studies Institute, The Academy of Military Science, Beijing 100091, China

Received:2021-07-30 Revised:2021-11-03 Online:2023-01-30 Published:2023-01-18
Contact: Jinlu Zhang E-mail:scs_pengpeng@126.com;jinlu0724@163.com

摘要/Abstract

摘要：

针对人在回路仿真推演中高层级指挥员下达作战任务与仿真系统模型指令之间的分辨率鸿沟，提出了一种基于行为树的多分辨率模型构建方法。通过改进行为树语法，实现了低分辨率作战任务解聚为高分辨率仿真系统指令；通过设计嵌入行为树的决策模型，解决了模型指令执行过程中面临的资源不确定性、执行效果不确定性问题。设计了夺取制空权作战场景，验证该方法的有效性。

关键词: 多分辨率模型, 行为树, 资源不确定性, 效果不确定性

Abstract:

Aiming at the resolution gap between the operation task issued by the high-level commanders and the simulation system model instructions in the human-in-the-loop simulation deduction, a multi-resolution modeling method based on behavior tree is proposed. By improving the behavior tree syntax, the low-resolution combat missions are disaggregated into high-resolution simulation system instructions. By designing a decision model embedded in the behavior tree, the problem of resource uncertainty and execution effect uncertainty faced in the execution of model instructions is solved. A combat scenario for seizing air supremacy is designed to verify the effectiveness of the method.

Key words: multi-resolution model, behavior tree, resource uncertainty, effect uncertainty

中图分类号:

TP301

刘兆鹏, 徐新海, 袁博文, 张金璐. 一种多分辨率仿真模型构建方法[J]. 系统仿真学报, 2023, 35(1): 202-211.

Zhaopeng Liu, Xinhai Xu, Bowen Yuan, Jinlu Zhang. A Multi-resolution Simulation Modeling Method[J]. Journal of System Simulation, 2023, 35(1): 202-211.

图/表 12

图1

图2

图3

图4

图5

图6

图7

图8

图9

表1

图10

图11

参考文献 25

1	Pace Dale K. Dimensions and Attributes of Simulation Fidelity [C]// Proceedings of 1998 Fall SIW.
2	David P K, Bigelow J. Experiments on Multiresolution Modeling(MRM) [R]. RAND Report-MR1004, 1998.
3	Davis P K, Bigelow J. Introduction to Multi-Resolution Model(MRM) with an Example Involving Precision Fires[C]// Enabling technology for Simulation Science (II). Proceeding of SPIE AeoroSense 1998.
4	刘宝宏, 黄柯棣. 多分辨率建模的研究现状与发展[J]. 系统仿真学报, 2004, 16(6): 1150-1154.
	Liu Baohong, Huang Kedi. Research Status and Development of Multi-resolution Modeling[J]. Journal of System Simulation, 2004, 16(6): 1150-1154.
5	李志飞, 吴静. 多粒度建模方法分析及实例研究[J]. 中国电子科学研究院学报, 2011, 6(1): 72-76.
	Li Zhifei, Wu Jing. Analysis of Multi-granularity Modeling Method and Case Study[J]. Journal of China Academy of Electronics, 2011, 6(1): 72-76.
6	李明忠, 毕长剑, 刘小荷, 等. 空军作战仿真模型聚合与解聚研究[J]. 系统仿真学报, 2008, 20(14): 3679-3684.
	Li Mingzhong, Bi Changjian, Liu Xiaohe, et al. Research on Aggregation and Disaggregation of Air Force Combat Simulation Models[J]. Journal of System Simulation, 2008, 20(14): 3679-3684.
7	刘宝宏, 黄柯棣. 多分辨率模型联合仿真的研究[J]. 计算机仿真, 2005, 22(2): 9-11
	Liu Baohong, Huang Kedi. Research on Co-simulation of Multi-resolution Models[J]. Computer Simulation, 2005, 22(2): 9-11.
8	Staff Joint. Universal Joint Task List[Z/OL].(2017-06-01) [2019-04-17]. .
9	王伟, 陈聪, 蒋鲁峰, 等. 兰德战略评估系统的软件设计、应用与发展[M]. 北京: 航空工业出版社, 2015.
	Wang wei, Chen Cong, Jiang Lufeng, et al. Software Design, Application and Development of RAND Strategic Evaluation System[M]. Beijing: Aviation Industry Press, 2015.
10	杨立功, 郭齐胜, 张伟. 聚合级实体仿真中任务的分解与解聚[J]. 系统仿真学报, 2006, 18(2): 186-188.
	Yang Ligong, Guo Qisheng, Zhang Wei. Decomposition and disaggregation of Tasks in Aggregation-level Entity Simulation[J]. Journal of System Simulation, 2006, 18(2):186-188.
11	荣明, 杨镜宇, 张昱. 多分辨率模型想定数据联合部署系统设计与实现[J]. 系统仿真学报, 2013, 25(增1): 318-322.
	Rong Ming, Yang Jingyu, Zhang Yu. Design and Implementation of Joint Deployment System for Multi-resolution Model Scenario Data[J]. Journal of System Simulation, 2013, 25(S1): 318-322.
12	刘剑, 薛昌友, 孙珠峰. 海战潜艇兵力作战任务聚合、解聚与重构实现[J].火力与指挥控制, 2015, 40(1): 131-136.
	Liu Jian, Xue Changyou, Sun Zhufeng. The Realization of Aggregation, Disaggregation and Reconstruction of Naval Submarine Forces Combat Missions[J]. Firepower and Command Control, 2015, 40(1): 131-136.
13	刘剑, 肖明彦, 章阳. 面向服务的舰艇编队反潜作战任务多分辨率建模研究[J]. 舰船科学技术, 2014, 36(8): 142-148.
	Liu Jian, Xiao Mingyan, Zhang Yang. Research on Service-Oriented Multi-resolution Modeling of Ship Formation Anti-submarine Combat Tasks[J]. Ship Science and Technology, 2014, 36(8): 142-148.
14	Puga G F, Gomez-Martin M A, Diaz-Agudob, et al. Dynamic Expansion of Behavior Trees[C] // Artificial Intelligence and Interactive Digital Entertainment Conference. 2008: 22-24.
15	刘瑞峰, 王家胜, 张灏龙, 等. 行为树技术的研究进展与应用[J]. 计算机与现代化工程, 2020(2): 76-82.
	Liu Ruifeng, Wang Jiasheng, Zhang Haolong, et al. Research Progress and Application of Behavior Tree Technology[J].Computer and Modern Engineering, 2020(2): 76-82.
16	董倩, 纪梦琪, 朱一凡. 空中作战决策行为树建模与仿真[J]. 指挥控制与仿真, 2019, 41(1): 18-25.
	Dong Qian, Ji Mengqi, Zhu Yifan. Air Combat Decision Behavior Tree Modeling and Simulation[J].Command Control and Simulation, 2019, 41(1): 18-25.
17	方君, 闫文君, 邓向阳, 等. 基于Q-学习和行为树的CGF空战行为决策[J]. 计算机与现代化, 2017(5): 37-39, 44.
	Fang Jun, Yan Wenjun, Deng Xiangyang, et al. CGF Air Combat Behavior Decision Based on Q Learning and Behavior Tree[J].Computer and Modernization, 2017(5): 37-39, 44.
18	涂浩, 刘洪星. 一种改进型Q 学习算法及其在行为树中的应用[J]. 计算机应用与软件, 2017, 34(12): 235-239.
	Tu Hao, Liu Hongxing. An Improved Q Learning Algorithm and Its Application in Behavior Tree[J].Computer Applications and Software,2017, 34(12): 235-239.
19	胡勇文, 陈国华. (m, n, k)指派问题的最小费用流模型及其算法[J]. 数学的实践与认知, 2017, 47(18): 162-170.
	Hu Yongwen, Chen Guohua. (m, n, k) Minimum Cost Flow Model and Algorithm for Assignment Problem[J].Practice and Cognition of Mathematics, 2017, 47(18):162-170.
20	Chen Y, Yang D, Yu J. Multi-UAV Task Assignment with Parameter and Time-sensitive Uncertainties using Modified Two-part Wolf Pack Search Algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems(S1557-9603), 2018, 54(6): 2853-2872.
21	GERKEY B P. On Multi-robot Task Allocation [D]. Los Angeles: University of Southern California, 2003.
22	Jian F W, Gao W J, Jun-can L, et al. Cooperative Task Allocation for Heterogeneous Multi-UAV Using Multi-objective Optimization Algorithm[J]. Journal of Central South University (S2095-2899), 2020, 27(2): 432-448.
23	Xu G, Long T, Wang Z, et al. Target-Bundled Genetic Algorithm for Multi-unmanned Aerial Vehicle Cooperative Task Assignment Considering Precedence Constraints[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering (S0954-4100), 2020, 234(3): 760-773.
24	费爱国, 张陆游, 丁前军. 基于拍卖算法的多级协同火力分配[J]. 系统工程与电子技术, 2012, 34(9): 1829-1833.
	Fei Aiguo, Zhang Luyou, Ding Qianjun. Multi-level Cooperative Fire Distribution Based on Auction Algorithm[J].System Engineering and Electronic Technology, 2012, 34(9): 1829-1833.
25	吴俊成, 周锐, 燃华明, 等. 遗传算法和拍卖算法在任务分配中的性能比较[J]. 电光与控制, 2016, 23(2): 11-15.
	Wu Juncheng, Zhou Rui, Ran Huaming, et al. Performance Comparison between Genetic Algorithm and Auction Algorithm in Task Allocation[J]. Electro-optical and control, 2016, 23(2): 11-15.

仿真结果	调用决策模型	未调用决策模型	总数
样本总数	6	3	9
完成仿真过程样本数	6	3	9
蓝方成功夺取制空权样本数	2	3	5
蓝方未成功夺取制空权样本数	4	0	4

[1]	段现银, 张灿, 朱泽润, 孙朝阳, 蒋国璋, 向峰. 逆解多目标优化的六自由度机械手轨迹规划[J]. 系统仿真学报, 2021, 33(9): 2128-2137.
[2]	杨杰, 张琪, 曾俊杰, 尹全军. 进化行为树方法研究综述[J]. 系统仿真学报, 2021, 33(10): 2315-2322.
[3]	徐文胜, 武博, 蒋坚鸿. 武器装备虚拟维修训练系统行为树设计与实现[J]. 系统仿真学报, 2018, 30(7): 2722-2728.

一种多分辨率仿真模型构建方法

A Multi-resolution Simulation Modeling Method

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 25

相关文章 3

编辑推荐

Metrics

本文评价