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Model-Based Autonomous Navigation with Moment of Inertia Estimation for Unmanned Aerial Vehicles

The dominant navigation system for low-cost, mass-market Unmanned Aerial Vehicles (UAVs) is based on an Inertial Navigation System (INS) coupled with a Global Navigation Satellite System (GNSS). However, problems tend to arise during periods of GNSS outage where the navigation solution degrades rapi...

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Autores principales: Mwenegoha, Hery, Moore, Terry, Pinchin, James, Jabbal, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603762/
https://www.ncbi.nlm.nih.gov/pubmed/31146481
http://dx.doi.org/10.3390/s19112467
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author Mwenegoha, Hery
Moore, Terry
Pinchin, James
Jabbal, Mark
author_facet Mwenegoha, Hery
Moore, Terry
Pinchin, James
Jabbal, Mark
author_sort Mwenegoha, Hery
collection PubMed
description The dominant navigation system for low-cost, mass-market Unmanned Aerial Vehicles (UAVs) is based on an Inertial Navigation System (INS) coupled with a Global Navigation Satellite System (GNSS). However, problems tend to arise during periods of GNSS outage where the navigation solution degrades rapidly. Therefore, this paper details a model-based integration approach for fixed wing UAVs, using the Vehicle Dynamics Model (VDM) as the main process model aided by low-cost Micro-Electro-Mechanical Systems (MEMS) inertial sensors and GNSS measurements with moment of inertia calibration using an Unscented Kalman Filter (UKF). Results show that the position error does not exceed 14.5 m in all directions after 140 s of GNSS outage. Roll and pitch errors are bounded to 0.06 degrees and the error in yaw grows slowly to 0.65 degrees after 140 s of GNSS outage. The filter is able to estimate model parameters and even the moment of inertia terms even with significant coupling between them. Pitch and yaw moment coefficient terms present significant cross coupling while roll moment terms seem to be decorrelated from all of the other terms, whilst more dynamic manoeuvres could help to improve the overall observability of the parameters.
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spelling pubmed-66037622019-07-17 Model-Based Autonomous Navigation with Moment of Inertia Estimation for Unmanned Aerial Vehicles Mwenegoha, Hery Moore, Terry Pinchin, James Jabbal, Mark Sensors (Basel) Article The dominant navigation system for low-cost, mass-market Unmanned Aerial Vehicles (UAVs) is based on an Inertial Navigation System (INS) coupled with a Global Navigation Satellite System (GNSS). However, problems tend to arise during periods of GNSS outage where the navigation solution degrades rapidly. Therefore, this paper details a model-based integration approach for fixed wing UAVs, using the Vehicle Dynamics Model (VDM) as the main process model aided by low-cost Micro-Electro-Mechanical Systems (MEMS) inertial sensors and GNSS measurements with moment of inertia calibration using an Unscented Kalman Filter (UKF). Results show that the position error does not exceed 14.5 m in all directions after 140 s of GNSS outage. Roll and pitch errors are bounded to 0.06 degrees and the error in yaw grows slowly to 0.65 degrees after 140 s of GNSS outage. The filter is able to estimate model parameters and even the moment of inertia terms even with significant coupling between them. Pitch and yaw moment coefficient terms present significant cross coupling while roll moment terms seem to be decorrelated from all of the other terms, whilst more dynamic manoeuvres could help to improve the overall observability of the parameters. MDPI 2019-05-29 /pmc/articles/PMC6603762/ /pubmed/31146481 http://dx.doi.org/10.3390/s19112467 Text en © 2019 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mwenegoha, Hery
Moore, Terry
Pinchin, James
Jabbal, Mark
Model-Based Autonomous Navigation with Moment of Inertia Estimation for Unmanned Aerial Vehicles
title Model-Based Autonomous Navigation with Moment of Inertia Estimation for Unmanned Aerial Vehicles
title_full Model-Based Autonomous Navigation with Moment of Inertia Estimation for Unmanned Aerial Vehicles
title_fullStr Model-Based Autonomous Navigation with Moment of Inertia Estimation for Unmanned Aerial Vehicles
title_full_unstemmed Model-Based Autonomous Navigation with Moment of Inertia Estimation for Unmanned Aerial Vehicles
title_short Model-Based Autonomous Navigation with Moment of Inertia Estimation for Unmanned Aerial Vehicles
title_sort model-based autonomous navigation with moment of inertia estimation for unmanned aerial vehicles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603762/
https://www.ncbi.nlm.nih.gov/pubmed/31146481
http://dx.doi.org/10.3390/s19112467
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