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

TL;DR

This paper adapts and parallelizes the FastMDP algorithm on GPUs to enable scalable, on-demand pre-departure flight plan scheduling for urban air mobility and cargo drones, handling thousands of plans efficiently.

Contribution

It introduces a GPU-accelerated version of FastMDP for large-scale, conflict-free flight plan scheduling in urban airspace, demonstrating significant performance improvements.

Findings

Able to schedule 2000-3000 flight plans on commodity GPUs

Achieves higher performance with server-class hardware

Shows potential for large-scale UAM flight scheduling systems

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