Model-Based Approach to Engineering Resilience in Multi-UAV Systems

Ordoukhanian, Edwin; Madni, Azad M.

doi:10.3390/systems7010011

Cited by 14 publications

(7 citation statements)

References 40 publications

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“…Peiravi et al [24] proposed a redundancy allocation strategy that aims to increase system reliability by determining the most appropriate time for reconfiguration and activating an optimal number of components. However, in adversarial battlefields, the reasons for disrupting the UCSoS have become increasingly ambiguous and unpredictable [25]. In addition, the high cost of producing redundant combat units makes it difficult to counter interference through the implementation of redundancy strategies.…”

Section: Ucsos Reconstructionmentioning

confidence: 99%

Optimal Unmanned Combat System-of-Systems Reconstruction Strategy with Heterogeneous Cost via Deep Reinforcement Learning

Li,

Yuan,

Ren

et al. 2024

Mathematics

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The unmanned combat system-of-systems (UCSoS) in modern warfare is comprised of various interconnected entities that work together to support mission accomplishment. The soaring number of entities makes the UCSoS fragile and susceptible to triggering cascading effects when exposed to uncertain disturbances such as attacks or failures. Reconfiguring the UCSoS to restore its effectiveness in a self-coordinated and adaptive manner based on the battlefield situation and operational requirements has attracted increasing attention. In this paper, we focus on the UCSoS reconstruction with heterogeneous costs, where the collaboration nodes may have different reconstruction costs. Specifically, we adopt the heterogeneous network to capture the interdependencies among combat entities and propose a more representative metric to evaluate the UCSoS reconstruction effectiveness. Next, we model the combat network reconstruction problem with heterogeneous costs as a nonlinear optimization problem and prove its NP-hardness. Then, we propose an approach called SoS-Restorer, which is based on deep reinforcement learning (DRL), to address the UCSoS reconstruction problem. The results show that SoS-Restorer can quickly generate reconstruction strategies and improve the operational capabilities of the UCSoS by about 20∼60% compared to the baseline algorithm. Furthermore, even when the size of the UCSoS exceeds that of the training data, SoS-Restorer exhibits robust generalization capability and can efficiently produce satisfactory results in real time.

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Section: Ucsos Reconstructionmentioning

confidence: 99%

Optimal Unmanned Combat System-of-Systems Reconstruction Strategy with Heterogeneous Cost via Deep Reinforcement Learning

Li,

Yuan,

Ren

et al. 2024

Mathematics

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“…On the other hand, in a heterogeneous swarm, the drones have different physical characteristics or perform different functions [32,26]. The heterogeneity of UAVs, with their complementary capabilities, can contribute to system resilience [23,15]. However, the impact of heterogeneity on resilience has not been sufficiently explored.…”

Section: They Ingeniously Combined Internet Of Things Technologiesmentioning

confidence: 99%

Research on resilience model of UAV swarm based on complex network dynamics

Wei,

Zhang,

Zhang

2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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The resilience of UAV swarms mainly revolves around ensuring stable and uninterrupted operations. Malicious attacks can implement the adverse impacts of potential threats through swarm communication links. In this context, the SIS (Susceptible → Infected → Susceptible) method is suitable for describing the information transmission within UAV swarms. An enhanced resilience model of the UAV swarm is proposed in this study, which incorporates the factors of self-dynamics, dynamics of topology, dynamics of information transmission, and SIS into the complex network model. The model proposed in this paper has the capability to effectively capture changes in the network topology as well as the dynamics of the system. The average number of susceptible drones is utilized as the metric to evaluate the resilience of the swarm. Furthermore, an experiment is conducted where a UAV swarm successfully carries out a surveillance mission. The proposed model not only enables the support of mission planning but also facilitates the design enhancements of UAV swarms.

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“…However, the context of the interruption and valid information such as the duration and status of the interruption must be known to complete the modeling, but sometimes the necessary information is not fully available. Flexibility means the process of dealing with the occurrence of a disturbance environment and not the disturbance after the occurrence of processing, while the flexible processing method aims to not let the system fail in heavy use and then go to the manager to recover the process but rather schedule additional resources in advance to deal with this imminent risk [12]. In the multi-UAV system, the structural organization is relatively complex [13,14].…”

Section: Related Workmentioning

confidence: 99%

A Task Planning Method for UAV Swarm Dynamic Reconstruction Based on a Fourth-Order Motif

Duan

Wang

et al. 2023

Electronics

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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.

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Model-Based Approach to Engineering Resilience in Multi-UAV Systems

Cited by 14 publications

References 40 publications

Optimal Unmanned Combat System-of-Systems Reconstruction Strategy with Heterogeneous Cost via Deep Reinforcement Learning

Optimal Unmanned Combat System-of-Systems Reconstruction Strategy with Heterogeneous Cost via Deep Reinforcement Learning

Research on resilience model of UAV swarm based on complex network dynamics

A Task Planning Method for UAV Swarm Dynamic Reconstruction Based on a Fourth-Order Motif

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