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arXiv · 2008.03518

Scalable FastMDP for Pre-departure Airspace Reservation and Strategic De-conflict

Joshua R Bertram·Peng Wei·Joseph Zambreno
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Abstract

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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Joshua R Bertram, Peng Wei, Joseph Zambreno. 2020-08-08. Scalable FastMDP for Pre-departure Airspace Reservation and Strategic De-conflict. https://arxiv.org/abs/2008.03518

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