基于ABMS的全光化无人机集群体系贡献率评估方法

doi:10.12382/bgxb.2023.0830

摘要/Abstract

摘要：

为研究具有复杂地形的未知战场环境中的全光化无人机集群作战概念,提出基于智能体建模与仿真的全光化无人机集群效能和体系贡献率评估方法。根据全光化无人机集群作战特点建立评估指标体系,给出各作战单元的模块化Agent模型。对超低空巡逻打击任务开展仿真,分析全光化无人机感知性能、通信性能、集群规模和集群配置对集群生存率、任务完成率、战损比和体系贡献率的影响。研究结果表明:全光化无人机能为作战体系带来新能力,有效提升体系作战效能;集群规模越大、全光化无人机占比越高,则体系作战效能越强,但全光化无人机性能提升带来的贡献并不明显。

关键词: 无人机集群, 作战概念, Agent, 作战建模与仿真, 效能评估, 体系贡献率

Abstract:

To study the operational concept of all-optical UAV swarm in unknown complex-terrain battlefield environment, an evaluation method is proposed. The method is used to evaluate the effectiveness and the system-of-systems (SoS) contribution rate of all-optical UAV swarm and is based on Agent-based modeling and simulation (ABMS). Firstly, the evaluation index system is established according to the combat characteristics of all-optical UAV swarm. Then, the modular agent model of each combat unit is given. Finally, the simulation of patrol and attack mission in ultra-low altitude is carried out. Analyzing the influences of all-optical UAV perception performance, all-optical UAV communication performance, swarm size and swarm configuration on swarm survival rate, tasks completion rate, battle damage rate and SoS contribution rate. The research results show that the all-optical UAV can bring new capabilities to the combat system and effectively improve the combat effectiveness of SoS. Results also show that a larger swarm size and a higher proportion of all-optical UAV can increase the combat effectiveness of SoS. And the contribution brought by the improvement of the performance of all-optical UAV is not significant.

Key words: UAV swarm, operational concept, Agent, combat modeling and simulation, effectiveness evaluation, SoS contribution rate

中图分类号:

V279

张琦, 葛玉雪, 李攀, 康淇钧, 裴扬. 基于ABMS的全光化无人机集群体系贡献率评估方法[J]. 兵工学报, 2023, 44(11): 3422-3435.

ZHANG Qi, GE Yuxue, LI Pan, KANG Qijun, PEI Yang. Evaluation Method for SoS Contribution Rate of All-optical UAV Swarm Based on ABMS[J]. Acta Armamentarii, 2023, 44(11): 3422-3435.

图/表 20

图1 评估流程

Fig.1 Evaluating process

图2 全光化无人机Agent的模型框架

Fig.2 Framework model of all-optical UAV agent

图3 全光化无人机集群的评估指标体系

Fig.3 Evaluation index system of all-optical UAV

图4 作战单元及其关系示意图

Fig.4 Demonstration of combat units and their relationship

图5 全光化无人机Agent的行为模型

Fig.5 Behaviour model of all-optical UAV agent

图6 激光导引头工作原理示意图

Fig.6 Schematic diagram of laser seeker

图7 三维路径规划算法流程图

Fig.7 Flow chart of three-dimensional path planning algorithm

图8 三维地形建模

Fig.8 Three-dimensional terrain modelling

图9 可行区域示意图

Fig.9 Demonstration of feasible region

图10 导弹Agent的行为模型

Fig.10 Behaviour model of missile agent

图11 指挥中心Agent的行为模型

Fig.11 Behaviour model of command center agent

图12 敌方目标Agent的行为模型

Fig.12 Behaviour model of enemy targets agent

图13 仿真界面

Fig.13 Interface of the simulation

表1 仿真参数

Table 1 Parameters in the simulation

作战单元	参数	数值
全光化无人机集群	无人机总数量	50
	全光化无人机比例/%	50, 100
	感知距离/km	20
全光化无人机	识别确认概率	0.9
	通信距离/km	20
	相对高度限制/m	50~200
常规无人机	感知距离/km	10
	相对高度限制/m	50~200
	数量	7
敌方目标	干扰距离/km	20
	干扰强度	0.5

图14 指标值随实验次数变化分析

Fig.14 Analysis on the variation of index value with the number of experiments

表2 实验设计

Table 2 Design of the experiment

实验组号	全光化无人机感知距离/km	全光化无人机通信距离/km	集群无人机总数量	全光化无人机所占比例/%
1	10~30	20	50	0, 50, 100
2	20	10~30	50	0, 50, 100
3	20	20	10~60	0, 50, 100
4	20	20	50	0~100

图15 全光化无人机感知距离影响分析

Fig.15 Analysis on the influence of perception distance of all-optical UAV

图16 全光化无人机通信距离影响分析

Fig.16 Analysis on the influence of communication distance of all-optical UAV

图17 集群无人机总数量影响分析

Fig.17 Analysis on the influence of the number of UAV in swarm

图18 全光化无人机所占比例影响分析

Fig.18 Analysis on the influence of the proportion of all-optical UAV

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