Latest revision as of 21:29, 28 January 2021

Abstract

The number of daily small Unmanned Aircraft Systems (sUAS) operations in uncontrolled low altitude airspace is expected to reach into the millions. UAS Traffic Management (UTM) is an emerging concept aiming at the safe and efficient management of such very dense traffic, but few studies are addressing the policies to accommodate such demand and the required ground infrastructure in suburban or urban environments. Searching for the optimal air traffic management policy is a combinatorial optimization problem with intractable complexity when the number of sUAS and the constraints increases. As the demands on the airspace increase and traffic patterns get complicated, it is difficult to forecast the potential low altitude airspace hotspots and the corresponding ground resource requirements. This work presents a Multi-agent Air Traffic and Resource Usage Simulation (MATRUS) framework that aims for fast evaluation of different air traffic management policies and the relationship between policy, environment and resulting traffic patterns. It can also be used as a tool to decide the resource distribution and launch site location in the planning of a next-generation smart city. As a case study, detailed comparisons are provided for the sUAS flight time, conflict ratio, cellular communication resource usage, for a managed (centrally coordinated) and unmanaged (free flight) traffic scenario.

Comment: The Integrated Communications Navigation and Surveillance (ICNS) Conference in 2019

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1902.04035

• http://arxiv.org/pdf/1902.04035

• http://xplorestaging.ieee.org/ielx7/8732965/8735100/08735348.pdf?arnumber=8735348,

http://dx.doi.org/10.1109/icnsurv.2019.8735348

• https://dblp.uni-trier.de/db/journals/corr/corr1902.html#abs-1902-04035,

https://experts.syr.edu/en/publications/a-simulation-framework-for-fast-design-space-exploration-of-unman,

http://ui.adsabs.harvard.edu/abs/2019arXiv190204035Z/abstract,

https://academic.microsoft.com/#/detail/2949619358