Parametric study of structured UTM separation recommendations with physics-based Monte Carlo distribution for collision risk model

With the increasing demand for unmanned aircraft system (UAS) traffic management (UTM) airspace comes the need to ensure the safe operation and management of said airspace. One layer of defense against mid-air-collision and the ensuing third-party injury or fatality is the pre-flight separation assu...

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Main Authors: Wang, John Chung-Hung, Deng, Chao, Low, Kin Huat
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/170259
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1702592023-09-12T15:31:08Z Parametric study of structured UTM separation recommendations with physics-based Monte Carlo distribution for collision risk model Wang, John Chung-Hung Deng, Chao Low, Kin Huat School of Mechanical and Aerospace Engineering Air Traffic Management Research Institute Engineering::Aeronautical engineering::Accidents and air safety Engineering::Aeronautical engineering::Air navigation Separation Collision Risk With the increasing demand for unmanned aircraft system (UAS) traffic management (UTM) airspace comes the need to ensure the safe operation and management of said airspace. One layer of defense against mid-air-collision and the ensuing third-party injury or fatality is the pre-flight separation assurance. This could be achieved by establishing the separation requirements for the UTM traffic based on the flight dynamics and communication navigation surveillance (CNS) performance that could be achieved in the airspace in question. A modified Reich collision risk model, typically used in civil aviation for separation minima evaluation, was used for the evaluation of the initial separation that would meet the target level of safety within a prescribed look-ahead time. This paper presents the parametric evaluation of using this physics-based and Monte Carlo-driven Reich collision risk model to evaluate the separation recommendation needed to achieve (Formula presented.) mid-air-collision risk in UTM. The evaluation was conducted for an encounter pair consisting of identical ∼1.2 kg quadrotors with various encounter geometries, cruise velocities, navigation uncertainties, and communication latency. Published version 2023-09-05T23:38:58Z 2023-09-05T23:38:58Z 2023 Journal Article Wang, J. C., Deng, C. & Low, K. H. (2023). Parametric study of structured UTM separation recommendations with physics-based Monte Carlo distribution for collision risk model. Drones, 7(6), 345-. https://dx.doi.org/10.3390/drones7060345 2504-446X https://hdl.handle.net/10356/170259 10.3390/drones7060345 2-s2.0-85163803843 6 7 345 en Drones © 2023 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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::Aeronautical engineering::Accidents and air safety
Engineering::Aeronautical engineering::Air navigation
Separation
Collision Risk
spellingShingle Engineering::Aeronautical engineering::Accidents and air safety
Engineering::Aeronautical engineering::Air navigation
Separation
Collision Risk
Wang, John Chung-Hung
Deng, Chao
Low, Kin Huat
Parametric study of structured UTM separation recommendations with physics-based Monte Carlo distribution for collision risk model
description With the increasing demand for unmanned aircraft system (UAS) traffic management (UTM) airspace comes the need to ensure the safe operation and management of said airspace. One layer of defense against mid-air-collision and the ensuing third-party injury or fatality is the pre-flight separation assurance. This could be achieved by establishing the separation requirements for the UTM traffic based on the flight dynamics and communication navigation surveillance (CNS) performance that could be achieved in the airspace in question. A modified Reich collision risk model, typically used in civil aviation for separation minima evaluation, was used for the evaluation of the initial separation that would meet the target level of safety within a prescribed look-ahead time. This paper presents the parametric evaluation of using this physics-based and Monte Carlo-driven Reich collision risk model to evaluate the separation recommendation needed to achieve (Formula presented.) mid-air-collision risk in UTM. The evaluation was conducted for an encounter pair consisting of identical ∼1.2 kg quadrotors with various encounter geometries, cruise velocities, navigation uncertainties, and communication latency.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Wang, John Chung-Hung
Deng, Chao
Low, Kin Huat
format Article
author Wang, John Chung-Hung
Deng, Chao
Low, Kin Huat
author_sort Wang, John Chung-Hung
title Parametric study of structured UTM separation recommendations with physics-based Monte Carlo distribution for collision risk model
title_short Parametric study of structured UTM separation recommendations with physics-based Monte Carlo distribution for collision risk model
title_full Parametric study of structured UTM separation recommendations with physics-based Monte Carlo distribution for collision risk model
title_fullStr Parametric study of structured UTM separation recommendations with physics-based Monte Carlo distribution for collision risk model
title_full_unstemmed Parametric study of structured UTM separation recommendations with physics-based Monte Carlo distribution for collision risk model
title_sort parametric study of structured utm separation recommendations with physics-based monte carlo distribution for collision risk model
publishDate 2023
url https://hdl.handle.net/10356/170259
_version_ 1779156665484967936