非完全信息下无人机集群对抗研究综述

薛健; 赵琳; 向贤财; 吕科; 宏晨; 张宝琳; 岩延; 王泳

doi:10.11999/JEIT230544

非完全信息下无人机集群对抗研究综述

doi: 10.11999/JEIT230544

薛健¹,
赵琳¹,
向贤财¹,
吕科^{1, 2},
宏晨³,
张宝琳⁴,
岩延⁵,
王泳^5, ,

1.
中国科学院大学工程科学学院北京 100049
2.
鹏程实验室深圳 518055
3.
北京联合大学机器人学院北京 100101
4.
青岛科技大学自动化与电子工程学院青岛 266061
5.
中国科学院大学人工智能学院北京 100190

基金项目: 国家重点研发计划(2018AAA0100804)

详细信息

作者简介:
薛健：男，教授，研究方向为多智能体系统、模式识别和机器视觉

赵琳：女，博士生，研究方向为深度强化学习、无人机集群系统、博弈论

向贤财：男，硕士生，研究方向为深度强化学习、无人机集群系统

吕科：男，教授，研究方向为人工智能、计算机视觉

宏晨：男，副教授，研究方向为深度强化学习、无人机集群控制

张宝琳：男，教授，研究方向为无人机集群控制

岩延：男，副教授，研究方向为深度强化学习

王泳：男，讲师，研究方向为复杂系统建模与优化、模式识别、数据挖掘

通讯作者:
王泳　wangyong@ucas.ac.cn

中图分类号: TN975; TP391; TP181; V279; V249
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- 被引次数: 0
出版历程
- 收稿日期: 2023-06-02
- 修回日期: 2023-09-28
- 网络出版日期: 2023-10-16
- 刊出日期: 2024-04-24

A Review of the Research on UAV Swarm Confrontation under Incomplete Information

XUE Jian¹,
ZHAO Lin¹,
XIANG Xiancai¹,
LÜ Ke^{1, 2},
HONG Chen³,
ZHANG Baolin⁴,
YAN Yan⁵,
WANG Yong^{5
, ,}

1.
School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China
2.
Peng Cheng Laboratory, Shenzhen 518055, China
3.
College of Robotics, Beijing Union University, Beijing 100101, China
4.
College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
5.
College of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China

Funds: The National Key Research and Development Program of China (2018AAA0100804)

摘要

摘要: 无人机集群以其具备的应用优势及发展前景，成为当前人工智能领域研究者关注的热点之一。而非完全信息下的无人机集群对抗技术，因其集群结构变化的高动态性以及环境信息复杂多变且不能完全感知的特点，成为对集群协同性与智能性要求最高的研究方向之一。其研究成果可以促进智能化无人系统的快速发展和广泛应用。该文全面回顾了非完全信息环境下无人机集群对抗研究的最新进展，按照包以德循环理论的思路将无人机集群对抗过程划分为态势评估、意图推断、任务规划与机动决策4个相互衔接的关键组成部分，并进一步将其细分为8个子研究目标。通过分析比较近年来的相关研究，着重阐述了无人机集群对抗领域各项任务的研究重点和难点以及已取得的成果，并讨论了无人机集群对抗技术所面临的挑战，包括大规模异构集群的协同控制、非完全信息的处理、复杂决策过程的建模以及实际应用任务的应对等。
- 无人机集群 /
- 非完全信息 /
- 态势评估 /
- 意图推断 /
- 任务规划 /
- 机动决策
Abstract: UAV (Unmanned Aerial Vehicle) swarm, with its application advantages and development prospects, has become one of the current hot spots of interest for researchers in the field of artificial intelligence. The UAV swarm confrontation technology under incomplete information has become one of the research directions with the highest requirements for swarm cooperativeness and intelligence due to the high dynamics of swarm structure changes and the complex and variable environmental information that cannot be fully perceived. Its research achievements can promote the rapid development and wide application of intelligent unmanned systems. This paper comprehensively reviews of the recent progress in the research of UAV swarm confrontation under incomplete information environments. According to the Observe-Orient-Decide-Act (OODA) loop theory, the UAV swarm confrontation process is divided into four interlocking key components of situation assessment, intention inference, mission planning, and maneuver decision, and is further subdivided into eight sub-research objectives. By analyzing and comparing the relevant research works in recent years, the research focuses and difficulties of various tasks in the field of UAV swarm confrontation and the achieved research results are highlighted, and the challenges faced by UAV swarm confrontation technology are discussed, including the cooperative control of large-scale heterogeneous swarms, the handling of incomplete information, the modeling of complex decision-making processes, and the tackling of practical application tasks.
- Unmanned Aerial Vehicle(UAV)swarm /
- Incomplete information /
- Situation assessment /
- Intention inference /
- Task planning /
- Maneuver decision

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图 1 无人机集群对抗研究脉络

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图 2 无人机集群对抗态势评估研究框架

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图 3 无人机集群对抗中的意图推断研究框架

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图 4 无人机集群对抗机动决策研究框架

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表 1 非完全信息下无人机集群对抗具体研究内容

OODA循环	对抗循环环节	具体内容	具体含义
观察	态势评估	对抗态势评估	将态势定性分成优势、劣势、均势等，通过构造优势函数，利用双方状态信息进行态势评估，辅助集群决策
观察	态势评估	威胁因素评估	根据无人机特点，评估排序敌方目标和防空威胁，以辅助目标分配和决策
定位	意图推断	行为预测	利用已有信息推测敌机未来行为模式，预判使无人机集群获得决策优势
定位	意图推断	意图识别	对敌方集群当前行为意图进行定性判断，理解敌机意图使无人机集群获得决策优势
决策	任务规划	目标分配	考虑无人机的特性和任务需求，根据资源与优先级等约束，合理地分配目标，并实现集群内动态协同
决策	任务规划	航迹规划	根据任务需求，为无人机规划最佳路径，以安全到达目标
执行	机动决策	协同对抗	充分发挥集群力量，通过分散瓦解敌方防御，增大对敌方造成有效打击的可能性
执行	机动决策	追踪合围	对敌方的动态追踪与合围，通过限制敌方行动来保持我方优势与对敌方的压制

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表 2 无人机集群空战对抗态势评估方法研究对比

参考文献	方法	仿真维度	无人机数量	影响因子权重	未来状态影响	非完全信息	异构集群
Wu等人^[6]	模糊推理	3D	1 vs. 1	客观计算	√	×	–
Zhang等人^[7]	模糊神经网络	3D	${n} $ vs. ${n} $	客观计算	√	×	×
Huang等人^[8]	贝叶斯推理	3D	1 vs. 1	客观计算	√	√	–
Xie等人^[9]	动态关联权值	3D	1 vs. 1	客观计算	√	×	–
高杨等人^[10]	改进态势模型	–	${n} $ vs. ${n} $	–	×	×	√
Shin等人^[12]	几何评分	3D	2 vs. 2	客观计算	×	×	×
√：考虑；×：不考虑；–：不涉及

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表 3 无人机集群空战对抗中对敌方行为预测方法研究对比

参考文献	方法	仿真维度	非完全信息	目标的复杂运动	计算难度的降低	目标数量	研究目标
Pan等人^[26]	状态预测影响图	2D	√	×	×	多个	机动决策
Xi 等人^[27]	PSR-RBF	3D	×	×	×	单个	空战情况感知和威胁评估
Liu等人^[28]	GA-MASAC-TP Net	2D	×	√	×	多个	预测目标情况
Tan等人^[29]	Bi-LSTM	2D	×	×	×	单个	机动决策
Yang等人^[30]	MPC	3D	×	×	√	单个	机动决策
√: 考虑；×: 不考虑；–:不涉及

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表 4 无人机集群目标分配方法研究对比

参考文献	方法	仿真维度	异构集群	非完全信息	目标威胁	资源约束	时间约束
严飞等人^[40]	CPN	2D	√	×	×	√	√
Zhen等人^[41]	改进CPN	2D	√	×	√	√	√
王峰等人^[42]	KnCMPSO	2D	√	×	×	√	√
Zhao等人^[43]	FTA	–	√	√	×	√	×
Jia等人^[44]	改进GA-随机规划	2D	√	√	×	√	√
赵玉亮等人^[45]	多策略融合粒子群	–	×	√	√	√	×
张安等人^[46]	IMSGWO	2D	√	√	√	×	√
Liu等人^[47]	MARL	–	×	√	×	√	√
√：考虑；×：不考虑；–：不涉及

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表 5 无人机集群航迹规划方法研究对比

参考文献	方法	空间维度	异构集群	非完全信息	目标数量	环境威胁	防碰撞约束	时间协同约束
蔡星娟等人^[57]	NSGAIII-ICO	3D	×	×	单目标	√	√	√
左燕等人^[58]	GCRLB	2D	×	×	单目标	×	√	×
Li等人^[59]	MN-DDPG	2D	×	√	单目标	×	√	×
王祝等人^[60]	SCP	3D	×	×	多目标	√	√	√
严飞等人^[40]	协同PSO	2D	√	×	多目标	×	×	√
Zhang等人^[61]	模糊蚁群	2D	×	×	多目标	×	×	×
√：考虑；×：不考虑

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表 6 无人机集群协同对抗方法研究对比

参考文献	方法	空间维度	异构集群	非完全信息	目标分配	协同态势评估	意图推断	协同优势
Ren等人^[63]	MADDPG	3D	×	√	×	√	√	√
Zhang等人^[64]	Actor-Critic	3D	×	×	√	√	×	√
Li等人^[65]	Actor-Critic	3D	×	×	×	≈	×	√
Wang等人^[66]	PD-MADDPG	2D	×	×	×	≈	×	√
Yu等人^[67]	CLPIO	3D	×	×	√	×	×	√
Deng等人^[68]	分布式任务分配	2D	√	√	√	×	√	√
√：考虑；×：不考虑；≈：采用了与协同态势评估类似的方法

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表 7 无人机集群追踪决策方法研究对比

参考文献	方法	仿真维度	异构集群	目标数量	目标不确定运动	避障研究	复杂环境
Memon等人^[69]	sLM-IPDA	3D	×	多目标	√	×	√
Cybulski等人^[70]	多重有向图	2D	×	多目标	√	×	√
Yu等人^[71]	自适应差分进化-Nash	3D	×	单目标	√	×	√
Zhou等人^[72]	最大互惠奖励MARL	2D	×	多目标	√	×	√
Zhou等人^[73]	D3QN	2D	×	多目标	×	×	×
Hua等人^[74]	策略梯度 - 注意力	2D	×	单目标	×	√	√
√: 考虑；×: 不考虑

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表 8 无人机集群合围决策相关研究对比

参考文献	方法	仿真维度	异构集群	复杂环境适应性	有限感知	目标数量	目标状态
张岱峰等人^[75]	狼群交互动力学模型	3D	√	√	√	多目标	移动
Ma等人^[76]	DO-NS	2D	×	×	×	单目标	移动
Murat Ozbek等人^[77]	改进DDPG	2D	×	×	×	单目标	移动
Li等人^[78]	改进DDPG	3D	×	√	×	单目标	移动
过劲劲等人^[79]	一致性协议	2D	×	×	√	多目标	移动
√: 考虑；×: 不考虑

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