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A Macrogrid Approach for Routing UAVs in Support of Information Gathering

机译:支持信息收集的用于路由无人机的宏网格方法

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In this work, we propose a method for determining the best ingress and egress points and time allocations for a series of linked unmanned aerial vehicle (UAV) routing instances, to maximize global information gain across all routes within an area of operation (AO). The AO is decomposed into a network of macrocells similar to standardized geocoordinate systems such as the Military Grid Reference System. The macrocells are further partitioned into a series of microcells, which represent numerically valued areas of surveillance interest which effectively become potential waypoints for the UAV. Given a sequence of macrocells for a time-constrained intelligence, surveillance, and reconnaissance (ISR) mission, a series of heuristics are used to generate and score potential ingress and egress points which serve as an input to a mathematical allocation model to determine a set of parameters that improves global information collection. The full-factorial study conducted on the allocation model revealed the sensitivity of the solution quality and solve time due to the size of the time allocation set for each macrocell. A comparison of the scoring methods on moderately sized problems, each requiring 1,000 score values on the information gain potential, is presented.
机译:在这项工作中,我们提出了一种方法,用于确定一系列链接的无人飞行器(UAV)路由实例的最佳进出点和时间分配,以最大程度地提高运营区域(AO)内所有路径的全局信息增益。 AO被分解为类似于标准化地理坐标系统(例如,军事网格参考系统)的宏单元网络。宏小区被进一步划分为一系列的微小区,这些微小区代表了具有监视价值的数值区域,这些区域有效地成为了无人机的潜在航路点。给定一系列用于时间受限的情报,监视和侦察(ISR)任务的宏单元,可以使用一系列试探法来生成和计入潜在的入口和出口点,将其用作数学分配模型以确定一组的输入改善全局信息收集的参数。在分配模型上进行的全要素研究表明,由于每个宏小区的时间分配集的大小,解决方案质量和求解时间非常敏感。给出了对中等大小问题的评分方法的比较,每种问题都需要在信息获取潜力上获得1,000个得分值。

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