共享型无人机企业运营商池化框架(SUAVE)的机会约束池化扇出排队分析

L. Bush
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引用次数: 2

摘要

空军库存中无人驾驶飞行器(uav)的数量正在迅速增加,而人力却没有随之增加。军事规划者目前正在寻求技术,使操作员能够同时控制更多数量的无人机。技术规划和推荐过程需要UAV操作及其对各种约束的敏感性的系统级工程分析。Olsen和Wood引入了一种称为扇出的概念,该概念可以估算出有效操作一组给定无人机所需的操作人员数量。扇形输出概念假设无人机永久分配给单个操作员或操作员团队。我们设计了一个集合的无人机-操作员团队分配方案,该方案允许操作员团队在整个无人机机群中共享资源。我们的架构不是将给定的无人机永久地分配给操作员团队,而是在多无人机操作期间根据需要动态地将操作员团队分配给无人机。我们构建了一个基于排队理论的体系结构,以经验比较池化和非池化的性能。对该体系结构的排队系统分析表明,它比非组队方法执行得更好。此外,我们的架构分析导致扇出更一般的定义。更重要的是,封闭式排队分析非常高效,允许我们分析更多的问题配置。对问题空间的更大控制也为确定适当的自主权和团队技术以进行进一步开发提供了优势。
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Shared UAV enterprise operator pooling framework (SUAVE) chance constrained pooled fan-out queueing analysis
The number of unmanned aerial vehicles (UAVs) in the Air Force inventory is rapidly increasing without a concomitant increase in manpower. Military planners are currently seeking technologies that enable operators to simultaneously control a greater number of UAVs. The technology planning and recommendation process requires a systems-level engineering analysis of UAV operations and their sensitivity to various constraints. Olsen and Wood introduced a concept called fan-out, which estimates how many operators are required to effectively operate a given set of UAVs. The fan-out concept assumes that UAVs are permanently assigned to a single operator or operator team. We designed a pooled UAV-to-operator team allocation scheme, which allows sharing of operator team resources across the entire UAV fleet. Rather than permanently assigning a given UAV to an operator team, our architecture dynamically allocates operator teams to UAVs on an as-needed basis during multi-UAV operations. We constructed an architecture based on queueing theory to empirically compare pooled and non-pooled performance. Queueing systems analysis of this architecture demonstrates that it performs better than a non-teaming approach. Moreover, our architectural analysis leads to a more general definition of fanout. More importantly, the closed-form queueing analysis is highly efficient, allowing us to analyze a greater number of problem configurations. This greater command of the problem space also offers advantages in determining appropriate autonomy and teaming technologies for further development.
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