可扩展的FastMDP，用于起飞前空域预留和战略冲突消除
Scalable FastMDP for Pre-departure Airspace Reservation and Strategic De-conflict
摘要：
城市空中机动性（UAM）和货运无人机的起飞前飞行计划调度将需要按需调度大量飞机。我们研究的可扩展性的算法被称为FastMDP表现良好，在一个密集的空域环境与地形的飞机deconflicting几十。我们表明，该算法可以适用于执行先到先服务的出发前的飞行计划调度冲突的自由飞行计划产生的需求。我们展示了一个并行实现的算法上的图形处理器单元（GPU），我们长期FastMDP-GPU和显示的性能和可扩展性，可以实现的水平。我们的研究结果表明，在商品GPU硬件上，我们可以对2000-3000个已知的飞行计划执行飞行计划调度，而服务器级硬件的性能可以更高。我们相信，这些结果显示出实现能够执行按需飞行调度的大规模UAM调度器的希望，该调度器适用于集中式或分布式飞行计划系统
英文摘要：
Pre-departure flight plan scheduling for Urban Air Mobility (UAM) and cargo delivery drones will require on-demand scheduling of large numbers of aircraft. We examine the scalability of an algorithm known as FastMDP which was shown to perform well in deconflicting many dozens of aircraft in a dense airspace environment with terrain. We show that the algorithm can adapted to perform first-come-first-served pre-departure flight plan scheduling where conflict free flight plans are generated on demand. We demonstrate a parallelized implementation of the algorithm on a Graphics Processor Unit (GPU) which we term FastMDP-GPU and show the level of performance and scaling that can be achieved. Our results show that on commodity GPU hardware we can perform flight plan scheduling against 2000-3000 known flight plans and with server-class hardware the performance can be higher. We believe the results show promise for implementing a large scale UAM scheduler capable of performing on-demand flight scheduling that would be suitable for both a centralized or distributed flight planning system
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