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.

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 cooperative action of multi-unmanned swarms faces increasing uncertainties such as tasks and environments. For the whole-process management of swarm cooperative action, the capabilities required for action management are analyzed in detail from the three dimensions of situation, task and action, and the functional architecture of multi-unmanned swarm cooperative action is given. Furthermore, an enhanced functional flow diagram method is introduced, and the corresponding cooperative action control architecture is designed by combining the action flow with the control process, so as to support the efficient management of cooperative actions of multi-unmanned swarms. Finally, based on the proposed architecture, a design case of swarm heterogeneous cooperative attack task chain is given, and the chain completion time index is used to verify the proposed architecture.

This paper starts from the functional modules of the literature index map and task planning modeling, and sorts out and studies the advanced methods and hot technologies currently used. The related research progress is described through knowledge graph, and from a pre-planning point of view analyse task planning method and the reverse typical task planning and related algorithms are sorted out respectively. In this way, the automation and intelligence of task planning can be improved, and some references can be provided for the subsequent research on swarm task planning. Finally, We present several future directions to elucidate the opportunities and applications for the next step.

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.

As the situation of unmanned combat changes rapidly, it is necessary to carry out dynamic task planning. How to conduct dynamic task scheduling for distributed intelligent unmanned combat systems has become a hot and difficult problem. In order to solve this problem, this paper proposes a method of fusing multi-attribute decisions under the framework of multi-objective optimization by using the characteristics of elastic networks, which can generate scheduling policies quickly when the execution sequence of input tasks is finite. Secondly, a multi-attribute decision-making method combining filter and Pearce correlation coefficient is set up to select the optimal compromise scheme on the Pareto surface. Finally, an experimental case of UAV group rescue is designed to obtain the Pareto front of the problem, and the optimal compromise scheme is selected on the Pareto surface to verify the effectiveness of the proposed method.

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.

作战体系面临的战场环境与态势存在较强的不确定性和动态变化性,必须从体系能力生成机理要求出发,开展体系对抗条件下的动态适变机制设计,为作战体系结构形式与行为模式合理适变提供支撑.本文针对智能化多无人集群作战体系的典型特征,提出了基于模式切换规则的动态适变机制设计方法,并且构建了设计视图元模型.该方法根据体系的能力领域划分和兵力层次划分,分别在体系,集群,平台三个层次,设计任务流程适变,组织指控柔性和装备功能抗毁等三个方面的动态适变机制.最后以集群行动序列适应性生成机制为例阐释了方法的应用流程和作用.

针对无人机(Unmanned Aerial Vehicle,UAV)集群访问控制机制通常稳定性策略性和安全性低等问题,提出基于以太坊区块链智能合约的UAV集群访问控制机制.进行基于角色的访问控制机制模型的改选,给出针对UAV集群基于访问控制模型的形式化定义.提出基于区块链技术的UAV集群访问控制架构,并提出对应的基本框架与执行流程,该机制有效降低了UAV集群作战管理资源成本,解决了无人集群因区块分叉导致状态信息不完全的问题.

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.

Visual Dialog: aiming at holding a meaningful conversation with humans based on natural images, is a 'high-level' AI task of multimodal fusion. Since the challenge for visual dialog was proposed in 2017, multimodal fusion has been developed and made significant breakthroughs with the help of deep learning techniques. The goal of this paper is to provide a comprehensive survey of the recent achievements in the Visual Dialog task. This survey covers many aspects of multimodal fusion research: Visual Co-reference Resolution, Attention Mechanism, Graph Neural Networks, evaluation issues, specifically benchmark datasets, evaluation metrics, and state of the art performance.

As unmanned and systematic operational becomes more and more complex, unmanned combat architecture design plays an increasingly important role in the war. However, there is no swarms resource sharing system built to meet the needs of more complex swarms task. To address this challenge, we propose a combat architecture model and an algorithm to explore the architecture solution space. An unmanned operational architecture model based on hyper network (Unmanned combat system-of-system architecture model, USoSAM). We propose an exploration architecture algorithm based on tabu search, which simultaneously introduces efficiency, cost, and time evaluation architecture. Finally, a benchmark case is used to verify the effectiveness of the combat network architecture model based on abstraction. The results show that the improved algorithm proposed in this paper has obvious advantages over other classical algorithms, and the optimized architecture can provide better decisions for commanders.

The network architecture design of the UAV cluster is an important basis for the development and operation of unmanned combat equipment. The current UAV cluster network architecture cannot dynamically schedule the unfinished task nodes in the process of task scheduling. This paper proposes a flexible network scheduling model (FNSM) for cluster task scheduling. Drawing on the adaptive thinking of the “Mosaic War” of DARPA, the model is described by using the theoretical method of hypernetwork, forming the appropriate variable network, and the capability value is stratified by the fuzzy theory membership degree according to the capability requirements of the mission. And according to the needs of the model, a design scheduling algorithm based on alternative measures (TSA) is designed. Finally, a case of recovering lost land is designed, and two algorithms, TSA and Max-Min, are respectively used for simulation verification. The results preliminarily prove the effectiveness of the TSA algorithm under the FNSM model.