基于协同对抗的水下博弈策略优化
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摘要
针对多自主水下航行器(autonomous underwater vehicle,AUV)的水下协同对抗博弈问题,以博弈论为基础,多AUV的多次对抗为作战背景,从同时考虑敌我双方对抗策略的角度出发,对多AUV的动态协同攻防对抗策略问题进行了研究。考虑生存概率指标函数和水下环境影响,建立了基于动态博弈的多自主水下航行器的单元目标分配模型,构建博弈矩阵。在此基础上,采用粒子群算法,通过求解博弈模型的纳什均衡解,形成博弈对抗双方的最优攻防决策方案,并对所研究的攻防策略优化方法进行了仿真验证,结果表明该模型和方法的可行性和有效性。
Based on the multi-round confrontation of multiple Autonomous Underwater Vehicles( AUVS),the concept of Nash equilibrium is used to solve the problem of underwater dynamic cooperative confrontation of multiple AUVs. From the perspective of confrontation strategies of both sides of an AUV and considering the influence of survival probability index function and the uncertain factors of underwater environment,the unit target allocation model of multiple AUVs based on dynamic game and game matrix are established. By solving the Nash equilibrium solution of the game model,the particle swarm optimization algorithm is applied to solve the Nash equilibrium point for obtaining the optimal attack and defense strategies of both sides. The feasibility and effectiveness of the method was verified by simulation.
Based on the multi-round confrontation of multiple Autonomous Underwater Vehicles( AUVS),the concept of Nash equilibrium is used to solve the problem of underwater dynamic cooperative confrontation of multiple AUVs. From the perspective of confrontation strategies of both sides of an AUV and considering the influence of survival probability index function and the uncertain factors of underwater environment,the unit target allocation model of multiple AUVs based on dynamic game and game matrix are established. By solving the Nash equilibrium solution of the game model,the particle swarm optimization algorithm is applied to solve the Nash equilibrium point for obtaining the optimal attack and defense strategies of both sides. The feasibility and effectiveness of the method was verified by simulation.
引文
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[2] MCGREW J S,HOW J P,WILLIAM B,et al. Air-Combat Strategy Using Approximate Dynamic Programming[J]. Journal of Guidance,Control,and Dynamics,2010,33(5):1641-1654
[3] POROPUDAS J,VIRTANEN K. Game-Theoretic Validation and Analysis of Air Combat Simulation Models[J]. IEEE Trans on Systems,Man and Cybernetics,Part A:Systems and Humans,2010,40(5):1057-1070
[4] SURESH M,GHOSE D. UAV Grouping and Coordination Tactics for Ground Attack Missions[J]. IEEE Trans on Aerospace and Electronic Systems,2012,48(1):673-692
[5] HERNANDEZ E,CERRO J,BARRIENTOS A. Game Theory Models for Multi-Robot Patrolling of Infrastructures[J]. International Journal of Advanced Robotic Systems,2013,10(181):1-9
[6] TORBJRN S D,MAJA M,GAURAV S. Sukhatme Multi-Robot Task Allocation through Vacancy Chain Scheduling[J]. Robotics and Autonomous Systems,2009,57:674-687
[7]王训,王兆魁,张育林.基于合作博弈的智能集群自主聚集策略[J].国防科技大学学报,2017,39(2):146-151WANG Xun, WANG Zhaokui, ZHANG Yulin. Strategy about Autonomous Aggregation of Intelligent Swarm Based on Cooperative Game[J]. Journal of National University of Defense Technology,2017,39(2):146-151(in Chinese)
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[9]闫雪飞,李新明,刘东,等.基于Nash-Q的网络信息体系对抗仿真技术[J].系统工程与电子技术,2018(1):217-224YAN Xuefei,LI Xinming,LIU Dong,et al. Confrontation Simulation for Network Information System-of-Systems Based on Nash-Q[J]. Systems Engineering and Electronics,2018(1):217-224
[10]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):150-173
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