Campus Units
Electrical and Computer Engineering
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
2020
Journal or Book Title
arXiv
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.
Copyright Owner
The Author(s)
Copyright Date
2020
Language
en
File Format
application/pdf
Recommended Citation
Bertram, Joshua R.; Wei, Peng; and Zambreno, Joseph, "Scalable FastMDP for Pre-departure Airspace Reservation and Strategic De-conflict" (2020). Electrical and Computer Engineering Publications. 255.
https://lib.dr.iastate.edu/ece_pubs/255
Included in
Artificial Intelligence and Robotics Commons, Multi-Vehicle Systems and Air Traffic Control Commons
Comments
This is a pre-print of the article Bertram, Joshua R., Peng Wei, and Joseph Zambreno. "Scalable FastMDP for Pre-departure Airspace Reservation and Strategic De-conflict." arXiv preprint arXiv:2008.03518 (2020). Posted with permission.