无人智能集群系统决策与控制研究进展  

Research progress in decision-making for unmanned intelligent swarm system and control

作　　者：潘振华 夏元清[1] 鲍泓 王睿哲 于婷婷 Pan Zhenhua;Xia Yuanqing;Bao Hong;Wang Ruizhe;Yu Tingting(School of Automation,Beijing Institute of Technology,Beijing 100081,China;Yangtze River Delta Institute of Beijing Institute of Technology,Jiaxing 314003,China;College of Robotics,Beijing Union University,Beijing 100020,China;School of Artificial Intelligence,China University of Mining and Technology,Beijing 100083,China;School of Mathematical Science,Heilongjiang University,Harbin 150086,China)

机构地区：[1]北京理工大学自动化学院,北京100081 [2]北京理工大学长三角研究院,嘉兴314003 [3]北京联合大学机器人学院,北京100020 [4]中国矿业大学(北京)人工智能学院,北京100083 [5]黑龙江大学数学科学学院,哈尔滨150086

出　　处：《中国图象图形学报》2024年第11期3195-3215,共21页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62203050);航空科学基金项目(2023M031072001);黑龙江省自然科学基金项目(YQ2022F015);中国博士后面上项目(2022M720441)。

摘　　要：无人集群系统是当前人工智能和机器人领域备受关注的研究热点,已在多个领域展现出广阔的应用前景。对无人集群系统进行了深入综述和分析,着重探讨了协同决策和博弈控制两个关键方面,旨在通过智能体之间的信息共享和协作,提高系统效率,解决在智能体之间可能出现的利益冲突和决策问题。首先,对一些基本概念进行了明确阐述,包括智能体、集群智能和无人集群系统,有助于读者建立对这一领域的基本理解。随后,介绍了协同与博弈控制数学模型、集群协同与博弈决策、集群协同控制方法、集群博弈控制方法等算法,着重强调了协同决策和博弈控制的理论基础,以及它们如何应用于无人集群系统中,从而提高系统的整体性能。接下来,列举了集群协同与博弈在多个领域的一些典型应用案例,包括智能交通、无人机编队、物流配送和军事领域。这些实际案例展示了该技术的广泛应用领域,以及它对提高效率和解决复杂问题的潜力。最后,讨论了未来研究方向和挑战,包括对新技术和方法的需求,以应对不断发展的需求和问题,以及如何进一步推动无人集群系统的发展。本文为无人集群系统的进一步发展提供指导和参考,以推动该领域的发展和创新,为未来的科学和技术进步做出了一定贡献。In the pursuit of furthering the understanding of unmanned swarm systems,this paper embarks on an expansive journey,delving even deeper into the intricacies of cooperative decision-making and game control.The two methodological pillars,carefully chosen for their unique contributions,play a pivotal role in steering unmanned swarm systems toward heightened efficiency and adaptability across diverse environments.First,the implementation of cooperative control stands as a cornerstone,fostering enhanced communication and collaboration among agents within the unmanned swarm system.This strategic approach not only minimizes conflicts but also streamlines tasks,contributing substantially to the augmentation of system efficiency.Cooperative control establishes a foundation for improved information exchange and seamless cooperation by promoting a cohesive environment where agents work in tandem.Second,the integration of game control methodologies plays a pivotal role in empowering agents to navigate conflicts of interest effectively.This approach goes beyond conflict resolution;it actively contributes to elevating decision-making processes and optimizing the overall interests of the cluster system.The dynamic nature of game control ensures that agents can strategically navigate complex scenarios,maximizing collective interests and ensuring the sustained efficiency of the unmanned swarm system.Additionally,in practical large-scale problems,a balanced combination of cooperation and games enhances the adaptive capabilities of intelligent system clusters in diverse environments.This approach effectively resolves conflicts of interest and decisionmaking challenges that may arise between agents.Regarding the implementation of the two methods,this study concen⁃trates on utilizing the collaborative control method for tasks such as formation control,cluster path planning,and cluster task collaboration.Specific technical implementations are allocated to corresponding sub-items.The game control methods center around various game ty

关 键 词：无人集群系统(USS) 智能决策 博弈控制 协同控制 强化学习(RL) 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]