A risk-based UAS traffic network model for adaptive urban airspace management

This paper presents a risk-based model for UAV path planning in urban low altitude environments. Firstly, the risks in urban environments were identified and classified into four groups, and the corresponding risk costs were categorized into five different levels. A general risk cost model was then...

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Main Authors: Pang, Bizhao, Tan, Qingyu, Ra, Thu, Low, Kin Huat
Other Authors: School of Mechanical and Aerospace Engineering
Format: Conference or Workshop Item
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/147356
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1473562021-05-08T20:10:20Z A risk-based UAS traffic network model for adaptive urban airspace management Pang, Bizhao Tan, Qingyu Ra, Thu Low, Kin Huat School of Mechanical and Aerospace Engineering AIAA Aviation 2020 Forum Air Traffic Management Research Institute Engineering Engineering::Mathematics and analysis Airspace Genetic Algorithm This paper presents a risk-based model for UAV path planning in urban low altitude environments. Firstly, the risks in urban environments were identified and classified into four groups, and the corresponding risk costs were categorized into five different levels. A general risk cost model was then developed for qualifying all related risk costs. Secondly, a ConOps for urban air route network with plane and cubical diagonals was built and the obtained risk cost information were ingested into the network to assist path planning. Finally, an A*cost algorithm was developed to generate a safe and cost-effective path for UAVs in urban low altitude airspace. Simulation results show that the novel route network structure with diagonals has better performance, with an average reduction of 17.56% for path distance. While the proposed A*cost algorithm has good results in terms of total cost and computational time, outperforming traditional Dijkstra and Ant Colony algorithm. Civil Aviation Authority of Singapore (CAAS) Accepted version This research is supported by the Civil Aviation Authority of Singapore and Nanyang Technological University, Singapore under their collaboration in the Air Traffic Management Research Institute. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Civil Aviation Authority of Singapore. 2021-05-06T04:25:26Z 2021-05-06T04:25:26Z 2020 Conference Paper Pang, B., Tan, Q., Ra, T. & Low, K. H. (2020). A risk-based UAS traffic network model for adaptive urban airspace management. AIAA Aviation 2020 Forum, 1 PartF. https://dx.doi.org/10.2514/6.2020-2900 9781624105982 https://hdl.handle.net/10356/147356 10.2514/6.2020-2900 2-s2.0-85090322133 1 PartF en © 2020 by Nanyang Technological University. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Engineering::Mathematics and analysis
Airspace
Genetic Algorithm
spellingShingle Engineering
Engineering::Mathematics and analysis
Airspace
Genetic Algorithm
Pang, Bizhao
Tan, Qingyu
Ra, Thu
Low, Kin Huat
A risk-based UAS traffic network model for adaptive urban airspace management
description This paper presents a risk-based model for UAV path planning in urban low altitude environments. Firstly, the risks in urban environments were identified and classified into four groups, and the corresponding risk costs were categorized into five different levels. A general risk cost model was then developed for qualifying all related risk costs. Secondly, a ConOps for urban air route network with plane and cubical diagonals was built and the obtained risk cost information were ingested into the network to assist path planning. Finally, an A*cost algorithm was developed to generate a safe and cost-effective path for UAVs in urban low altitude airspace. Simulation results show that the novel route network structure with diagonals has better performance, with an average reduction of 17.56% for path distance. While the proposed A*cost algorithm has good results in terms of total cost and computational time, outperforming traditional Dijkstra and Ant Colony algorithm.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Pang, Bizhao
Tan, Qingyu
Ra, Thu
Low, Kin Huat
format Conference or Workshop Item
author Pang, Bizhao
Tan, Qingyu
Ra, Thu
Low, Kin Huat
author_sort Pang, Bizhao
title A risk-based UAS traffic network model for adaptive urban airspace management
title_short A risk-based UAS traffic network model for adaptive urban airspace management
title_full A risk-based UAS traffic network model for adaptive urban airspace management
title_fullStr A risk-based UAS traffic network model for adaptive urban airspace management
title_full_unstemmed A risk-based UAS traffic network model for adaptive urban airspace management
title_sort risk-based uas traffic network model for adaptive urban airspace management
publishDate 2021
url https://hdl.handle.net/10356/147356
_version_ 1699245884386050048