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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9460910 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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