无人机分布式集群反制动态多目标运动控制技术

doi:10.12305/j.issn.1001-506X.2025.11.24

系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (11): 3765-3778.doi: 10.12305/j.issn.1001-506X.2025.11.24

• 制导、导航与控制 • 上一篇

无人机分布式集群反制动态多目标运动控制技术

王琛¹^,², 朱承¹^,²^,*, 王祥科³, 丁兆云¹^,², 张千桢¹^,², 张胜¹^,², 朱先强¹^,²

1. 国防科技大学系统工程学院，湖南长沙 410073
2. 国防科技大学信息系统工程全国重点实验室，湖南长沙 410073
3. 国防科技大学智能科学学院，湖南长沙 410073

收稿日期:2024-12-05 出版日期:2025-11-25 发布日期:2025-12-08
通讯作者: 朱承
作者简介:王　琛（1998—），男，博士研究生，主要研究方向为智能指挥控制、分布式无人集群
王祥科（1981—），男，研究员，博士，主要研究方向为无人机自主集群控制
丁兆云（1982—），男，研究员，博士，主要研究方向为知识图谱
张千桢（1992—），男，研究员，博士，主要研究方向为图计算
张　胜（1993—），男，研究员，博士，主要研究方向为大模型指挥控制
朱先强（1982—），男，研究员，博士，主要研究方向为复杂网络、智能指挥控制

Technology for countering dynamic multi-target motion control using distributed drone swarm systems

Chen WANG¹^,², Cheng ZHU¹^,²^,*, Xiangke WANG³, Zhaoyun DING¹^,², Qianzhen ZHANG¹^,², Sheng ZHANG¹^,², Xianqiang ZHU¹^,²

1. College of Systems Engineering，National University of Defense Technology，Changsha 410073，China
2. National Key Laboratory of Information System Engineering，National University of Defense Technology，Changsha 410073，China
3. College of Intelligence Science and Technology，National University of Defense Technology，Changsha 410073，China

Received:2024-12-05 Online:2025-11-25 Published:2025-12-08
Contact: Cheng ZHU

摘要/Abstract

摘要：

基于无人机分布式集群反制多个动态无人机的任务场景，提出通过基因调控网络和行为设计结合的方法实现无人机集群速度控制。无人机通过基因调控网络模型基于目标位置信息和威胁区位置信息计算适应当前环境的群体形态；考虑无人机在反制目标任务的各项子行为，设计反制任务场景中的无人机分布式集群运动速度控制器，无人机可在群体层面涌现出自适应兵力分配集群反制目标的队形。提出关于任务完成效果与集群运动性能的若干统计指标，并通过仿真对比实验进行了指标分析。指标统计结果显示本文所提方法可以很好地实现无人机分布式集群反制多个动态目标的任务。

关键词: 无人机集群, 多智能体系统, 基因调控网络, 行为设计法, 分布式控制

Abstract:

In task scenarios for countering multiple dynamic-moving drones using distributed drone swarm systems, a method that combines gene regulatory networks and behavioral design is proposed to achieve velocity control of drone swarms. Drones use a gene regulatory network model to calculate the optimal swarm morphology based on target location information and threat zone position information, adapting to the current environment. Considering various sub-behaviors of drones in countering targets, a velocity control mechanism for the distributed drone swarm in the countermeasure task scenario is designed. This allows drones to exhibit adaptive force allocation formations at the swarm level to counter targets effectively. Several statistical indicators related to task completion effectiveness and swarm movement performance are proposed, and these indicators are analyzed through simulation comparison experiments. Indicator statistical results demonstrate that the proposed method can effectively achieve distributed drone swarm to counter multiple dynamic target tasks.

Key words: drone swarm, multi-agent system, gene regulatory network, behavioral design method, distributed control

中图分类号:

TJ 301

王琛, 朱承, 王祥科, 丁兆云, 张千桢, 张胜, 朱先强. 无人机分布式集群反制动态多目标运动控制技术[J]. 系统工程与电子技术, 2025, 47(11): 3765-3778.

Chen WANG, Cheng ZHU, Xiangke WANG, Zhaoyun DING, Qianzhen ZHANG, Sheng ZHANG, Xianqiang ZHU. Technology for countering dynamic multi-target motion control using distributed drone swarm systems[J]. Systems Engineering and Electronics, 2025, 47(11): 3765-3778.

图/表 20

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表1

参考文献 38

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指标	GRN方法	AGENT方法	TACTICS方法
指标1	×	—	√
指标2	×	—	√
指标3	×	√	√
指标4	×	√	√
指标5	×	—	√
指标6	—	—	√
指标7	×	—	√
指标8	—	—	√

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无人机分布式集群反制动态多目标运动控制技术

Technology for countering dynamic multi-target motion control using distributed drone swarm systems

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