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。本文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，这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 our 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. These two methodological pillars, carefully chosen for their unique contributions, play a pivotal role in steering unmanned swarm systems towards heightened efficiency and adaptability across diverse environments. Firstly, 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 significantly to the augmentation of system efficiency. By promoting a cohesive environment where agents work in tandem, cooperative control establishes a foundation for improved information exchange and seamless cooperation. Secondly, 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 game enhances the adaptive capabilities of intelligent system clusters in diverse environments. This approach effectively resolves conflicts of interest and decision-making challenges that may arise between agents. Regarding the implementation of these two methods, we concentrate on utilizing the collaborative control method for tasks such as formation control, cluster path planning, and cluster task collaboration. We allocate specific technical implementations to corresponding sub-items. The game control methods center around various game types, including Self-Play, evolutionary play, and reinforcement learning play. These approaches offer new prospects for addressing optimization challenges in cluster control. This paper comprehensively reviews the application of cooperative control and game control methods in the Unmanned Swarm System. It provides explicit explanations of fundamental concepts, including agents, swarm intelligence, and unmanned swarm systems, to establish a basic understanding for readers. Subsequently, the mathematical models of cooperative and game control, swarm cooperation and game decisions, swarm cooperative control methods, swarm game control methods, and other algorithms are introduced. Emphasis is placed on the theoretical foundations of cooperative decision-making and game control, along with their applications in improving overall system performance in the unmanned swarm system. Furthermore, the paper delves into illustrative application scenarios, providing concrete examples of how swarm cooperation and game control methodologies find practical relevance across diverse fields. These exemplary cases span a spectrum of industries, including intelligent transportation, UAV formation, logistics and distribution, and military domains. By showcasing the tangible applications of these technologies in real-world settings, the paper offers valuable insights into the versatility and adaptability of unmanned swarm systems. Finally, the paper discusses future research directions and challenges, emphasizing the necessity for new technologies and methods to address evolving needs and problems. The highlighted complex challenges, including the intricacy of large-scale swarm systems, collaboration among heterogeneous agents, adaptability to dynamic environments, autonomy of clusters, interpretability and safety of unmanned swarm systems, and self-healing capability, undoubtedly serve as key research focal points for future unmanned systems. Overall, this paper serves as a comprehensive guide and reference, not only delving into the theoretical underpinnings but also providing practical insights into the application of cooperative decision-making and game control in unmanned swarm systems. Its forward-looking approach positions it as a valuable resource for those seeking to advance the field, fostering development, innovation, and contributing to the ongoing scientific and technological progress in this domain.