Scalable FastMDP for Pre-departure Airspace Reservation and Strategic De-conflict
Joshua R. Bertram, Peng Wei, Joseph Zambreno · AIAA Scitech 2021 Forum · 2021
View Video Presentation: https://doi.org/10.2514/6.2021-0779.vid 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 demonstrate an algorithm known as FastMDP-GPU that performs 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) 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. Our results show promise for implementing a large scale UAM scheduler capable of performing on-demand flight scheduling that would be suitable for both centralized or distributed flight planning systems.