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潘振华1, 夏元清1, 鲍泓2, 王睿哲3, 于婷婷4(1.北京理工大学;2.北京联合大学;3.北京理工大学长三角研究院;4.黑龙江大学)

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

(1.Beijing Insititute of Technology;2.Beijing Union University)

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.