论文标题
可扩展的FASTMDP，用于前部空间保留和战略性冲突
Scalable FastMDP for Pre-departure Airspace Reservation and Strategic De-conflict
论文作者
论文摘要
城市空气移动性（UAM）和货物送货无人机的开发前飞行计划将需要大量飞机的按需安排。我们检查了一种称为FastMDP的算法的可伸缩性，该算法在具有地形的密集空域环境中表现出很好的表现，可以很好地解散数十架飞机。我们表明，该算法可以调整以执行第一个上班前的前部飞行计划计划，在该计划中生成了无冲突的飞行计划。我们演示了在图形处理器单元（GPU）上的算法的并行实现，我们将其称为FastMDP-GPU并显示可以实现的性能和缩放水平。我们的结果表明，在商品GPU硬件上，我们可以针对2000-3000个已知飞行计划执行飞行计划计划，并且使用服务器级硬件，性能可以更高。我们相信结果显示了实施大型UAM调度程序的希望
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
