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DRL-RNP: Deep Reinforcement Learning-Based Optimized RNP Flight Procedure Execution

The required navigation performance (RNP) procedure is one of the two basic navigation specifications for the performance-based navigation (PBN) procedure as proposed by the International Civil Aviation Organization (ICAO) through an integration of the global navigation infrastructures to improve th...

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Detalles Bibliográficos
Autores principales: Zhu, Longtao, Wang, Jinlin, Wang, Yi, Ji, Yulong, Ren, Jinchang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460910/
https://www.ncbi.nlm.nih.gov/pubmed/36080933
http://dx.doi.org/10.3390/s22176475
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author Zhu, Longtao
Wang, Jinlin
Wang, Yi
Ji, Yulong
Ren, Jinchang
author_facet Zhu, Longtao
Wang, Jinlin
Wang, Yi
Ji, Yulong
Ren, Jinchang
author_sort Zhu, Longtao
collection PubMed
description The required navigation performance (RNP) procedure is one of the two basic navigation specifications for the performance-based navigation (PBN) procedure as proposed by the International Civil Aviation Organization (ICAO) through an integration of the global navigation infrastructures to improve the utilization efficiency of airspace and reduce flight delays and the dependence on ground navigation facilities. The approach stage is one of the most important and difficult stages in the whole flying. In this study, we proposed deep reinforcement learning (DRL)-based RNP procedure execution, DRL-RNP. By conducting an RNP approach procedure, the DRL algorithm was implemented, using a fixed-wing aircraft to explore a path of minimum fuel consumption with reward under windy conditions in compliance with the RNP safety specifications. The experimental results have demonstrated that the six degrees of freedom aircraft controlled by the DRL algorithm can successfully complete the RNP procedure whilst meeting the safety specifications for protection areas and obstruction clearance altitude in the whole procedure. In addition, the potential path with minimum fuel consumption can be explored effectively. Hence, the DRL method can be used not only to implement the RNP procedure with a simulated aircraft but also to help the verification and evaluation of the RNP procedure.
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spelling pubmed-94609102022-09-10 DRL-RNP: Deep Reinforcement Learning-Based Optimized RNP Flight Procedure Execution Zhu, Longtao Wang, Jinlin Wang, Yi Ji, Yulong Ren, Jinchang Sensors (Basel) Article The required navigation performance (RNP) procedure is one of the two basic navigation specifications for the performance-based navigation (PBN) procedure as proposed by the International Civil Aviation Organization (ICAO) through an integration of the global navigation infrastructures to improve the utilization efficiency of airspace and reduce flight delays and the dependence on ground navigation facilities. The approach stage is one of the most important and difficult stages in the whole flying. In this study, we proposed deep reinforcement learning (DRL)-based RNP procedure execution, DRL-RNP. By conducting an RNP approach procedure, the DRL algorithm was implemented, using a fixed-wing aircraft to explore a path of minimum fuel consumption with reward under windy conditions in compliance with the RNP safety specifications. The experimental results have demonstrated that the six degrees of freedom aircraft controlled by the DRL algorithm can successfully complete the RNP procedure whilst meeting the safety specifications for protection areas and obstruction clearance altitude in the whole procedure. In addition, the potential path with minimum fuel consumption can be explored effectively. Hence, the DRL method can be used not only to implement the RNP procedure with a simulated aircraft but also to help the verification and evaluation of the RNP procedure. MDPI 2022-08-28 /pmc/articles/PMC9460910/ /pubmed/36080933 http://dx.doi.org/10.3390/s22176475 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Zhu, Longtao
Wang, Jinlin
Wang, Yi
Ji, Yulong
Ren, Jinchang
DRL-RNP: Deep Reinforcement Learning-Based Optimized RNP Flight Procedure Execution
title DRL-RNP: Deep Reinforcement Learning-Based Optimized RNP Flight Procedure Execution
title_full DRL-RNP: Deep Reinforcement Learning-Based Optimized RNP Flight Procedure Execution
title_fullStr DRL-RNP: Deep Reinforcement Learning-Based Optimized RNP Flight Procedure Execution
title_full_unstemmed DRL-RNP: Deep Reinforcement Learning-Based Optimized RNP Flight Procedure Execution
title_short DRL-RNP: Deep Reinforcement Learning-Based Optimized RNP Flight Procedure Execution
title_sort drl-rnp: deep reinforcement learning-based optimized rnp flight procedure execution
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460910/
https://www.ncbi.nlm.nih.gov/pubmed/36080933
http://dx.doi.org/10.3390/s22176475
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