Robust digital-twin airspace discretization and trajectory optimization for autonomous unmanned aerial vehicles.
Aerial mobility operation model
Autonomous UAV logistics
Digital twin models
Drones
Energy-efficient trajectory optimization
Resilient transportation infrastructure
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
31 May 2024
31 May 2024
Historique:
received:
30
01
2023
accepted:
15
05
2024
medline:
1
6
2024
pubmed:
1
6
2024
entrez:
31
5
2024
Statut:
epublish
Résumé
The infiltration of heterogenous fleets of autonomous Unmanned Aerial Vehicles (UAVs) in smart cities is leading to the consumerization of city air space which includes infrastructure creation of roads, traffic design, capacity estimation, and trajectory optimization. This study proposes a novel autonomous Advanced Aerial Mobility (AAM) logistical system for high density city centers. First, we propose a real-time 3D geospatial mining framework for LiDAR data to create a dynamically updated digital twin model. This enables the identification of viable airspace volumes in densely populated 3D environments based on the airspace policy/regulations. Second, we propose a robust city airspace dynamic 4D discretization method (Skyroutes) for autonomous UAVs to incorporate the underlying real-time constraints coupled with externalities, legal, and optimal UAV operation based on kinematics. An hourly trip generation model was applied to create 1138 trips in two scenarios comparing the cartesian discretization to our proposed algorithm. The results show that the AAM enables a precise airspace capacity/cost estimation, due to its detailed 3D generation capabilities. The AAM increased the airspace capacity by up to 10%, the generated UAV trajectories are 50% more energy efficient, and significantly safer.
Identifiants
pubmed: 38822002
doi: 10.1038/s41598-024-62421-4
pii: 10.1038/s41598-024-62421-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
12506Subventions
Organisme : Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada)
ID : RGPIN-2018-05994
Organisme : Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC Canadian Network for Research and Innovation in Machining Technology)
ID : RGPIN-2018-05994
Informations de copyright
© 2024. The Author(s).
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