Hybrid AI-based Dynamic Re-routing Method for Dense Low-Altitude Air Traffic Operations

Yibing Xie; Alessandro Gardi; Roberto Sabatini; Annie Liang

doi:10.1109/DASC55683.2022.9925777

Hybrid AI-based Dynamic Re-routing Method for Dense Low-Altitude Air Traffic Operations

Yibing Xie
, Alessandro Gardi
, Roberto Sabatini
, Annie Liang

Aerospace Engineering

RMIT University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

In this paper, we propose a rerouting method based on hybrid Artificial Intelligence (AI) algorithms for managing Unmanned Aircraft Systems (UAS) and Urban Air Mobility (UAM) traffic during their cruise and approach phases. The adopted approach capitalizes upon FourDimensional Trajectory (4DT) functionalities, supporting an uncertainty-resilient and flexible strategic deconfliction framework to improve the operational efficiency and security of Demand-Capacity Balancing (DCB) services. The objective is to accommodate future UAM and other autonomous vehicle-based business models by safely implementing traffic management in dense low-altitude airspace around cities and suburbs. The proposed UAS Traffic Management (UTM) system uses metaheuristic algorithm, especially the Tabu-search algorithm, to determine a global optimised rerouting solution. The calculated solutions can be continuously used as labelled data to train and optimise a machine learning process for real-time decision making, greatly improving the computational performance of intelligent UTM systems.

Original language	British English
Title of host publication	2022 IEEE/AIAA 41st Digital Avionics Systems Conference, DASC 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665486071
DOIs	https://doi.org/10.1109/DASC55683.2022.9925777
State	Published - 2022
Event	41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022 - Portsmouth, United States Duration: 18 Sep 2022 → 22 Sep 2022

Publication series

Name	AIAA/IEEE Digital Avionics Systems Conference - Proceedings
Volume	2022-September
ISSN (Print)	2155-7195
ISSN (Electronic)	2155-7209

Conference

Conference	41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022
Country/Territory	United States
City	Portsmouth
Period	18/09/22 → 22/09/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

A
ATM
Cybersecurity
DCB
Metaheuristics Algorithm
Path Planning
Re-routing
UTM

Access to Document

10.1109/DASC55683.2022.9925777

OpenUrl availability

Full text

Cite this

Xie, Y., Gardi, A., Sabatini, R., & Liang, A. (2022). Hybrid AI-based Dynamic Re-routing Method for Dense Low-Altitude Air Traffic Operations. In 2022 IEEE/AIAA 41st Digital Avionics Systems Conference, DASC 2022 - Proceedings (AIAA/IEEE Digital Avionics Systems Conference - Proceedings; Vol. 2022-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC55683.2022.9925777

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title = "Hybrid AI-based Dynamic Re-routing Method for Dense Low-Altitude Air Traffic Operations",

abstract = "In this paper, we propose a rerouting method based on hybrid Artificial Intelligence (AI) algorithms for managing Unmanned Aircraft Systems (UAS) and Urban Air Mobility (UAM) traffic during their cruise and approach phases. The adopted approach capitalizes upon FourDimensional Trajectory (4DT) functionalities, supporting an uncertainty-resilient and flexible strategic deconfliction framework to improve the operational efficiency and security of Demand-Capacity Balancing (DCB) services. The objective is to accommodate future UAM and other autonomous vehicle-based business models by safely implementing traffic management in dense low-altitude airspace around cities and suburbs. The proposed UAS Traffic Management (UTM) system uses metaheuristic algorithm, especially the Tabu-search algorithm, to determine a global optimised rerouting solution. The calculated solutions can be continuously used as labelled data to train and optimise a machine learning process for real-time decision making, greatly improving the computational performance of intelligent UTM systems.",

keywords = "A, ATM, Cybersecurity, DCB, Metaheuristics Algorithm, Path Planning, Re-routing, UTM",

author = "Yibing Xie and Alessandro Gardi and Roberto Sabatini and Annie Liang",

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year = "2022",

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Xie, Y, Gardi, A , Sabatini, R & Liang, A 2022, Hybrid AI-based Dynamic Re-routing Method for Dense Low-Altitude Air Traffic Operations. in 2022 IEEE/AIAA 41st Digital Avionics Systems Conference, DASC 2022 - Proceedings. AIAA/IEEE Digital Avionics Systems Conference - Proceedings, vol. 2022-September, Institute of Electrical and Electronics Engineers Inc., 41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022, Portsmouth, United States, 18/09/22. https://doi.org/10.1109/DASC55683.2022.9925777

Hybrid AI-based Dynamic Re-routing Method for Dense Low-Altitude Air Traffic Operations. / Xie, Yibing; Gardi, Alessandro ; Sabatini, Roberto et al.
2022 IEEE/AIAA 41st Digital Avionics Systems Conference, DASC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (AIAA/IEEE Digital Avionics Systems Conference - Proceedings; Vol. 2022-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Hybrid AI-based Dynamic Re-routing Method for Dense Low-Altitude Air Traffic Operations

AU - Xie, Yibing

AU - Gardi, Alessandro

AU - Sabatini, Roberto

AU - Liang, Annie

PY - 2022

Y1 - 2022

N2 - In this paper, we propose a rerouting method based on hybrid Artificial Intelligence (AI) algorithms for managing Unmanned Aircraft Systems (UAS) and Urban Air Mobility (UAM) traffic during their cruise and approach phases. The adopted approach capitalizes upon FourDimensional Trajectory (4DT) functionalities, supporting an uncertainty-resilient and flexible strategic deconfliction framework to improve the operational efficiency and security of Demand-Capacity Balancing (DCB) services. The objective is to accommodate future UAM and other autonomous vehicle-based business models by safely implementing traffic management in dense low-altitude airspace around cities and suburbs. The proposed UAS Traffic Management (UTM) system uses metaheuristic algorithm, especially the Tabu-search algorithm, to determine a global optimised rerouting solution. The calculated solutions can be continuously used as labelled data to train and optimise a machine learning process for real-time decision making, greatly improving the computational performance of intelligent UTM systems.

AB - In this paper, we propose a rerouting method based on hybrid Artificial Intelligence (AI) algorithms for managing Unmanned Aircraft Systems (UAS) and Urban Air Mobility (UAM) traffic during their cruise and approach phases. The adopted approach capitalizes upon FourDimensional Trajectory (4DT) functionalities, supporting an uncertainty-resilient and flexible strategic deconfliction framework to improve the operational efficiency and security of Demand-Capacity Balancing (DCB) services. The objective is to accommodate future UAM and other autonomous vehicle-based business models by safely implementing traffic management in dense low-altitude airspace around cities and suburbs. The proposed UAS Traffic Management (UTM) system uses metaheuristic algorithm, especially the Tabu-search algorithm, to determine a global optimised rerouting solution. The calculated solutions can be continuously used as labelled data to train and optimise a machine learning process for real-time decision making, greatly improving the computational performance of intelligent UTM systems.

KW - A

KW - ATM

KW - Cybersecurity

KW - DCB

KW - Metaheuristics Algorithm

KW - Path Planning

KW - Re-routing

KW - UTM

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022

Y2 - 18 September 2022 through 22 September 2022

ER -

Xie Y, Gardi A , Sabatini R, Liang A. Hybrid AI-based Dynamic Re-routing Method for Dense Low-Altitude Air Traffic Operations. In 2022 IEEE/AIAA 41st Digital Avionics Systems Conference, DASC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (AIAA/IEEE Digital Avionics Systems Conference - Proceedings). doi: 10.1109/DASC55683.2022.9925777

Hybrid AI-based Dynamic Re-routing Method for Dense Low-Altitude Air Traffic Operations

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

OpenUrl availability

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Cite this