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A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with Stochastic Fuel Consumption

机译:随机选择多路径飞行的两阶段方法

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摘要

The past decade has seen a substantial increase in the use of small unmanned aerial vehicles (UAVs) in both civil and military applications. This article addresses an important aspect of refueling in the context of routing multiple small UAVs to complete a surveillance or data collection mission. Specifically, this article formulates a multiple-UAV routing problem with the refueling constraint of minimizing the overall fuel consumption for all the vehicles as a two-stage stochastic optimization problem with uncertainty associated with the fuel consumption of each vehicle. The two-stage model allows for the application of sample average approximation (SAA). Although the SAA solution asymptotically converges to the optimal solution for the two-stage model, the SAA run time can be prohibitive for medium- and large-scale test instances. Hence, we develop a tabu search-based heuristic that exploits the model structure while considering the uncertainty in fuel consumption. Extensive computational experiments corroborate the benefits of the two-stage model compared to a deterministic model and the effectiveness of the heuristic for obtaining high-quality solutions.
机译:在过去的十年中,在民用和军事应用中使用小型无人机(UAV)的数量大大增加。本文介绍了在路由多个小型无人机以完成监视或数据收集任务的情况下加油的重要方面。具体而言,本文将多UAV路径问题公式化为加油约束,即将所有车辆的总油耗降至最低,这是两阶段随机优化问题,且不确定性与每辆车的油耗相关。两阶段模型允许应用样本平均近似(SAA)。尽管SAA解决方案渐近收敛于两阶段模型的最佳解决方案,但是SAA运行时间对于中型和大型测试实例可能是禁止的。因此,我们开发了一种基于禁忌搜索的启发式方法,该方法利用模型结构同时考虑了燃油消耗的不确定性。大量的计算实验证实了与确定性模型相比,两阶段模型的优势以及启发式方法获得高质量解决方案的有效性。

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