With the rapid growth of application demands and the real-time change of environmental situations, the defects of the UAV task network in adaptability, flexibility, and resilience are becoming more and more prominent. The current network architecture that the junction of points and lines is fixed cannot dynamically provide capacity requirements in real-time due to the failure nodes encountered in the Unmanned Aerial Vehicle (UAV) task scheduling process. To address this challenging issue, this paper proposes a flexible network architecture supporting dynamic fault-tolerant task scheduling model (DSM-FNA) for the UAV cluster. To be specific this paper resorts to super network theory, combining the management theory of flexible network and resilience network to carry out the organizational calculation on the model, and also draw upon linear transformation function to weight and stratify the capability value according to the ability requirement required by the task. Then, a flexible network architecture dynamic scheduling algorithm (FDSA) is proposed, and the substitution strategy is designed for the failure point, to realize the capability and dynamically adapt to the task. Finally, compared with the classical Max-Min algorithm and other algorithms, it is verified that the FDSA algorithm performs better dynamic adjustment for quick response in case of UAV cluster emergencies.

The primary purpose of task allocation is to build each equipment platform and quickly complete integration planning at the actual combat speed to achieve efficient management of the entire task. In this process, higher requirements are put forward for dynamic, cooperative, and highly adaptive drone colony organization. In this paper, the scheduling problem of hybrid unmanned aerial vehicle (UAV) systems is studied under an uncertain environment. First, the system-capability-task organizational structure is defined and quantified, which lays a foundation for dynamic adjustment of the organizational structure in the future. Then, combined with the theory of flexible network and elastic network management, the model is calculated, and the linear transformation function and fuzzy theory are used to stratify and cluster the capability layers. On this basis, four motif structures are introduced for abnormal nodes in the process of dynamic adjustment, and a dynamic group reconstruction algorithm (DRA-M) is established. Finally, the time and communication load indexes are determined, and the alternative strategy is designed for the failure point. The performance of the classical scheduling algorithm is evaluated by benchmarking it under different conditions. The results show that the algorithm has a good dynamic adjustment ability in the event of a UAV swarm emergency, which is a bright light for the future study of highly adaptive UAV cluster organization.

The future of warfare will be the swarm, intelligent unmanned, and even systematic, but it hasn't been described in terms of a grouping confrontation. Specifically, due to the nature of the battlefield and the development trend of the future battlefield, swarm gaming is suitable. In this article, we investigated and concluded the attack defense problem for complex scenarios from game-theoretic a perspective. More than 60 key contributions are included in this survey, covering many aspects of unmanned swarm gaming research: modeling methods and algorithms of swarm gaming communication control, task allocation, resource management, and cooperative control. The consistency of existing research is summarized by examining performance characteristics. We provide each game-theoretic model's abstractly expressed bilevel programming problem and combed the potential applications for complex scenarios. Finally, we propose promising future directions to shed light on future opportunities and applications.

With the rapid development of intelligent unmanned technology, unmanned combat swarms are faced with a highly aggressive, highly uncertain, and highly dynamic battlefield environment, and the operation mode of unmanned combat has gradually shifted from single-platform operations to swarm networking collaboration combat development. Aiming at the typical characteristics of the unmanned swarm combat system, this paper proposes a role assignment model for organizational reconfiguration at the swarm layer and builds an unmanned swarm organization reconfiguration role-assignment mechanism model (SORAM) based on the fourth-order directed motif. The method starts from the organizational domain of the swarm system and takes the task as the the dependent variable of the role assignment of the swarm organization, quantifies the importance of the motif from a statistical point of view, and establishes a multi-objective model considering the similarity of the structure. The swarm reconfiguration role optimization method of SR-NSGA-2 provides a reference for the online adaptation of the swarm links. Finally, combined with a simulated combat simulation case, the usability and effectiveness of the method are tested.

With the rapid development of digital intelligence, drones can provide many conveniences for people’s lives, especially in executing rescue missions in special areas. When executing rescue missions in remote areas, communication cannot be fully covered. Therefore, to improve the online adaptability of the task chain link in task planning with a complex system structure as the background, a distributed source-task-capability allocation (DSTCA) problem was constructed. The first task chain coordination mechanism scheme was proposed, and a DSTCA architecture based on the task chain coordination mechanism was constructed to achieve the online adaptability of the swarm. At the same time, the existing algorithms cannot achieve this idea, and the DSTCA-CBBA algorithm based on CNP is proposed. The efficiency change, agent score, and time three indicators are evaluated through specific cases. In response to sudden changes in nodes in the task chain link, the maximum spanning tree algorithm is used to reconstruct the task chain link in a short time, thereby completing the mission task assigned to the drone entity. Meanwhile, the experimental results also prove the effectiveness of the proposed algorithm.