[1]王宏健,于丹,徐欣,等.非对称博弈下多UUV基地防卫协同对抗策略[J].智能系统学报,2022,17(2):348-359.[doi:10.11992/tis.202012037]
WANG Hongjian,YU Dan,XU Xin,et al.Multi-UUV base defense cooperative countermeasure under the asymmetric game condition[J].CAAI Transactions on Intelligent Systems,2022,17(2):348-359.[doi:10.11992/tis.202012037]
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WANG Hongjian,YU Dan,XU Xin,et al.Multi-UUV base defense cooperative countermeasure under the asymmetric game condition[J].CAAI Transactions on Intelligent Systems,2022,17(2):348-359.[doi:10.11992/tis.202012037]
非对称博弈下多UUV基地防卫协同对抗策略
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第2期
页码:
348-359
栏目:
学术论文—智能系统
出版日期:
2022-03-05
- Title:
- Multi-UUV base defense cooperative countermeasure under the asymmetric game condition
- Keywords:
- asymmetric game; base defense; unmanned underwater vehicle; attack–defense confrontation; multi-UUV coordination; hierarchical decision-making; role assignment; strategy
- 分类号:
- TP391
- 摘要:
- 针对非对称博弈下的水下无人对抗问题，开展对基于基地防卫的多无人水下潜航器(unmanned underwater vehicles, UUVs)协同对抗策略研究。在双方能力和数量有差异的非对称博弈情况下，设计UUV基本行为，分别基于红、蓝方能力设计对抗策略选择方法；本文提出红方多UUV分层决策算法，并设计红方多UUV角色分配方法；在防卫任务中基于目标偏航角设计两种红方多UUV联合防卫方法；并相应给出设计蓝方UUV策略及决策方法。最后设计水下对抗仿真实验，从对抗过程的事件决策、UUV机动控制等方面验证了本文设计的对抗方法对水下非对称条件下的多UUV对抗问题具有有效性。
- Abstract:
- Aiming at the problem of unmanned underwater warfare under the asymmetric game condition, in this paper, we examine the cooperative countermeasure of multiple unmanned underwater vehicles (UUVs) based on base defense. In an asymmetric game with differences in ability and quantity between the red and blue sides, the basic behaviors of UUVs and the selection methods of the countermeasures of both sides are designed. This paper proposes a hierarchical decision-making algorithm for the red side and designs a role assignment method of its UUVs. In a defense task, two kinds of red-side multi-UUV joint defense methods are designed based on the target yaw angle; the strategy and the decision-making method of UUVs of the blue side are given correspondingly. Finally, the simulation experiment of underwater confrontation is designed, and the effectiveness of the proposed method with respect to multi-UUV confrontation under an underwater asymmetric game condition is verified from the aspects of event decision-making and UUV maneuver control in a confrontation.
- 参考文献/References:
-
[1] 陈强. 水下无人航行器[M]. 北京: 国防工业出版社, 2014: 1?2.
CHEN Qiang. Unmanned underwater vehicle[M]. Beijing: National Defense Industry Press, 2014: 1?2.
[2] WILLCOX S, GOLDBERG D, VAGANAY J, et al. Multi-vehicle cooperative navigation and autonomy with the bluefin CADRE system[C]//Proceedings of IFAC (International Federation of Automatic Control) Conference. [S.l.],2006: 20?22.
[3] LI Ning, CüRüKLü B, BASTOS J, et al. A probabilistic and highly efficient topology control algorithm for underwater cooperating AUV networks[J]. Sensors, 2017, 17(5): 1022.
[4] GAO Zhenyu, GUO Ge. Fixed-time leader-follower formation control of autonomous underwater vehicles with event-triggered intermittent communications[J]. IEEE access, 2018, 6: 27902–27911.
[5] YAN Zheping, XU Da, CHEN Tao, et al. Leader-follower formation control of UUVs with model uncertainties, current disturbances, and unstable communication[J]. Sensors, 2018, 18(2): 662.
[6] 魏娜, 刘明雍, 张帅, 等. 基于协同对抗的水下博弈策略优化[J]. 西北工业大学学报, 2019, 37: 63–69
WEI Na, LIU Mingyong, ZHANG Shuai, et al. Optimizing underwater game strategy based on cooperative confrontation[J]. Journal of Northwestern Polytechnical University, 2019, 37: 63–69
[7] 邓鹏, 李伟, 王新华. 水下无人平台“蜂群”作战体系研究[J]. 兵器装备工程学报, 2018, 39(8): 8–10
DENG Peng, LI Wei, WANG Xinhua. Research on battle system of “swarm” of underwater unmanned veicle (UUV)[J]. Journal of ordnance equipment engineering, 2018, 39(8): 8–10
[8] 冯卉, 毛红保, 吴天爱. 侦察打击一体化无人机关键技术及其发展趋势分析[J]. 飞航导弹, 2014(3): 42–46
FENG Hui, MAO Hongbao, WU Tianai. Analysis on key technologies and deelopment trend of reconnaissance strike integrated UAV[J]. Aerodynamic missile journal, 2014(3): 42–46
[9] 张昊. 无人系统作战样式分析[J]. 飞航导弹, 2020(6): 50–54
Zhang Hao. Operational style analysis of unmanned systems[J]. Aerodynamic missile journal, 2020(6): 50–54
[10] GREGORIN L, FREIRE E, CARVALHO E, et al. Evolutionary robotics applied to the multi-robot worst-case pursuit-evasion problem[C]//Proceedings of the 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference. New York, USA, 2016: 1?7.
[11] HUANG Haomiao, ZHANG Wei, DING J, et al. Guaranteed decentralized pursuit-evasion in the plane with multiple pursuers[C]//Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference. Orlando, USA, 2011: 4835?4840.
[12] WANG Chen, ZHANG Ting, WANG Kai, et al. A new approach of multi-robot cooperative pursuit[C]//Proceedings of the 32nd Chinese Control Conference. Xi’an, China, 2013: 7252?7256.
[13] ZHU Daqi, LV Ruofan, CAO Xiang, et al. Multi-AUV hunting algorithm based on bio-inspired neural network in unknown environments[J]. International journal of advanced robotic systems, 2015, 12(11): 1–166.
[14] BODó Z, HARMATI I. High-level strategy design in RoboCup environment[C]//Proceedings of the 16th IEEE International Symposium on Computational Intelligence and Informatics. Budapest, Hungary, 2015: 129?134.
[15] 王杰. 多AUV围捕决策与行为协同控制研究[D]. 哈尔滨: 哈尔滨工程大学, 2018: 45?60.
WANG Jie. Research on multi-AUVs hunting decision-making and behavior cooperative[D]. Harbin: Harbin Engineering University, 2018: 45?60.
[16] 郭胜伟. 无人化战争[M]. 北京: 国防大学出版社, 2011: 9?11.
GUO Shengwei. Unmanned war[M]. Beijing: National Defense University Press, 2011: 9?11.
[17] 魏瑞轩, 李学仁. 无人机系统及作战使用[M]. 北京: 国防工业出版社, 2009: 27?29.
WEI Ruixuan, LI Xueren. UAV system and operational use[M]. Beijing: National Defense Industry Press, 2009: 27?29.
[18] 赵民全. 基于改进遗传算法的多无人机协同任务规划[J]. 舰船电子对抗, 2020, 43(4): 44–47
ZHAO Minquan. Collaborative task planning for multi-UAV based on modified genetic algorithm[J]. Shipboard electronic countermeasure, 2020, 43(4): 44–47
[19] BRAND M, MASUDA M, WEHNER N, et al. Ant Colony Optimization algorithm for robot path planning[C]//Proceedings of 2010 International Conference on Computer Design and Applications. Qinhuangdao, China, 2010.
[20] BHATTACHARJEE P, RAKSHIT P, GOSWAMI I, et al. Multi-robot path-planning using artificial bee colony optimization algorithm[C]//Proceedings of the Third World Congress on Nature and Biologically Inspired Computing. Salamanca, Spain, 2011: 219?224.
[21] 吴虎胜, 张凤鸣, 吴庐山. 一种新的群体智能算法——狼群算法[J]. 系统工程与电子技术, 2013, 35(11): 2430–2438
WU Husheng, ZHANG Fengming, WU Lushan. New swarm intelligence algorithm——wolf pack algorithm[J]. Journal of systems engineering and electronics, 2013, 35(11): 2430–2438
[22] DAHL T S, MATARI? M, SUKHATME G S. Multi-robot task allocation through vacancy chain scheduling[J]. Robotics and autonomous systems, 2009, 57(6/7): 674–687.
[23] MCGREW J S, HOW J P, WILLIAMS B, et al. Air-combat strategy using approximate dynamic programming[J]. Journal of guidance, control, and dynamics, 2010, 33(5): 1641–1654.
[24] SURESH M, GHOSE D. UAV grouping and coordination tactics for ground attack missions[J]. IEEE transactions on aerospace and electronic systems, 2012, 48(1): 673–692.
[25] 罗德林, 张海洋, 谢荣增, 等. 基于多agent系统的大规模无人机集群对抗[J]. 控制理论与应用, 2015, 32(11): 1498–1504
LUO Delin, ZHANG Haiyang, XIE Rongzeng, et al. Unmanned aerial vehicles swarm conflict based on multi-agent system[J]. Control theory & applications, 2015, 32(11): 1498–1504
[26] FAN Jieru, LI Dongguang, LI Rupeng, et al. Analysis on MAV/UAV cooperative combat based on complex network[J]. Defence technology, 2020, 16(1): 150–157.
[27] MUHAMMED D, ANISI M H, ZAREEI M, et al. Game theory-based cooperation for underwater acoustic sensor networks: taxonomy, review, research challenges and directions[J]. Sensors, 2018, 18(2): 425.
[28] 徐欣. 面向区域防卫任务的UUV协同攻防方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2020: 66?67.
XU Xin. Research on multi UUV countermeasure method based on base defense[D]. Harbin: Harbin Engineering University, 2020: 66?67.
[29] 郭银景, 刘琦, 鲍建康, 等. 基于人工势场法的AUV避障算法研究综述[J]. 计算机工程与应用, 2020, 56(4): 16–23
GUO Yinjing, LIU Qi, BAO Jiankang, et al. Overview of AUV obstacle avoidance algorithm based on artificial potential field method[J]. Computer engineering and applications, 2020, 56(4): 16–23
[30] 周东波. 面向典型任务的UUV空间运动规划方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2014: 82?83.
ZHOU Dongbo. Research on UUV spatial motion planning method for typical tasks[D]. Harbin: Harbin Engineering University, 2014: 82?83.
[31] 黄斌. 潜艇艇群反航母协同作战仿真试验[J]. 舰船科学技术, 2013, 35(6): 75–79
HUANG Bin. Analysis of the joint operation simulation test of the submarine group against aircraft carrier[J]. Ship science and technology, 2013, 35(6): 75–79
备注/Memo
收稿日期:2020-12-22。
基金项目:国家自然科学基金重点项目（61633008）
作者简介:王宏健，教授、博士生导师，主要研究方向为无人系统自主控制与群智能、智能优化。获国家科技进步二等奖2项、国防科技进步特等奖1项、省科技进步一等奖3项、二等奖1项。获技术发明专利授权30余项，发表学术文章100余篇;于丹，博士研究生，主要研究方向为多UUV水下对抗;徐欣，硕士，主要研究方向为UUV编队、UUV协同对抗策略、机器人路径规划、AGV调度
通讯作者:王宏健.E-mail:cctime99@163.com
更新日期/Last Update:
1900-01-01