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Pre-flight conflict detection and resolution for UAV integration in shared airspace: Sendai 2030 model case

Pre-flight conflict detection and resolution for UAV integration in shared airspace: Sendai 2030 model case

Ho, Florence ORCID: 0000-0003-4703-7727, Geraldes, Ruben, Goncalves, Artur, Rigault, Bastien, Oosedo, Atsushi, Cavazza, Marc ORCID: 0000-0001-6113-9696 and Prendinger, Helmut ORCID: 0000-0003-4654-9835 (2019) Pre-flight conflict detection and resolution for UAV integration in shared airspace: Sendai 2030 model case. IEEE Access, 7. pp. 170226-170237. ISSN 2169-3536 (Online) (doi:https://doi.org/10.1109/ACCESS.2019.2954987)

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Abstract

The increasing demand for services performed by Unmanned Aerial Vehicles (UAVs) requires the simulation of Unmanned Aircraft System Traffic Management (UTM) systems. In particular, Pre-Flight Conflict Detection and Resolution (CDR) methods need to scale to future demand levels and generate conflict-free paths for a potentially large number of UAVs before actual takeoff. However, few studies have examined realistic scenarios and the requirements for the UTM system. In this paper, we focus on the Sendai 2030 model case, a realistic projection of UAV usage for deliveries in one area in Japan. This model case considers up to 21,000 requests for Unmanned Aircraft Systems (UAS) operations over a 13 hour service time, and thus poses a challenge for the Pre-Flight CDR methods. Therefore, we propose an airspace reservation method based on 4DT (3D plus time Trajectories) and map the Pre-Flight CDR problem to a Multi-Agent Path Finding (MAPF) problem. We study first-come first-served (FCFS) and “batch” processing of UAS operation requests, and compare the throughput of those methods. We analyze the air traffic topology of deliveries by UAVs, and discuss several metrics to better understand the complexity of air traffic in the Sendai model case.

Item Type: Article
Uncontrolled Keywords: Unmanned aircraft system traffic management (UTM), pre-flight conflict detection and resolution (CDR), multi-agent path finding (MAPF), air traffic complexity metrics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Department of Computing & Information Systems
Last Modified: 19 Feb 2020 12:35
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/26980

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