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Book Chapter: Optimization-Based Online Flow Fields Estimation for AUVs Navigation

TitleOptimization-Based Online Flow Fields Estimation for AUVs Navigation
Authors
KeywordsAutonomous underwater vehicle
Flow field
Navigation
Online estimation
Optimization
Issue Date8-Mar-2023
Abstract

The motion of an autonomous underwater vehicle (AUV) is affected by its surrounding water flows, so an accurate estimation of the flow field could be used to assist the vehicle’s navigation. We propose an optimization-based approach to the problem of online flow field learning with limited amounts of data. To compensate for the shortage of online measurements, we identify two types of physically meaningful constraints from eddy geometry of the flow field and the property of fluid incompressibility respectively. By parameterizing the flow field as a polynomial vector field, the optimization problem could be solved efficiently via semi-definite programming (SDP). The effectiveness of the proposed algorithm in terms of flow field estimation is experimentally validated on real-world ocean data by providing performance comparisons with a related method. Further, the proposed estimation algorithm is proved to be able to be combined with a motion planning method to allow an AUV to navigate efficiently in an underwater environment where the flow field is unknown beforehand.


Persistent Identifierhttp://hdl.handle.net/10722/340429
ISBN
ISSN
2023 SCImago Journal Rankings: 0.296
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXu, Hao-
dc.contributor.authorLu, Yupu-
dc.contributor.authorPan, Jia-
dc.date.accessioned2024-03-11T10:44:34Z-
dc.date.available2024-03-11T10:44:34Z-
dc.date.issued2023-03-08-
dc.identifier.isbn9783031255540-
dc.identifier.issn2511-1256-
dc.identifier.urihttp://hdl.handle.net/10722/340429-
dc.description.abstract<p>The motion of an autonomous underwater vehicle (AUV) is affected by its surrounding water flows, so an accurate estimation of the flow field could be used to assist the vehicle’s navigation. We propose an optimization-based approach to the problem of online flow field learning with limited amounts of data. To compensate for the shortage of online measurements, we identify two types of physically meaningful constraints from eddy geometry of the flow field and the property of fluid incompressibility respectively. By parameterizing the flow field as a polynomial vector field, the optimization problem could be solved efficiently via semi-definite programming (SDP). The effectiveness of the proposed algorithm in terms of flow field estimation is experimentally validated on real-world ocean data by providing performance comparisons with a related method. Further, the proposed estimation algorithm is proved to be able to be combined with a motion planning method to allow an AUV to navigate efficiently in an underwater environment where the flow field is unknown beforehand.</p>-
dc.languageeng-
dc.relation.ispartofExperimental Robotics, Springer Proceedings in Advanced Robotics-
dc.subjectAutonomous underwater vehicle-
dc.subjectFlow field-
dc.subjectNavigation-
dc.subjectOnline estimation-
dc.subjectOptimization-
dc.titleOptimization-Based Online Flow Fields Estimation for AUVs Navigation-
dc.typeBook_Chapter-
dc.identifier.doi10.1007/978-3-031-25555-7_24-
dc.identifier.scopuseid_2-s2.0-85151061956-
dc.identifier.volume27 SPAR-
dc.identifier.spage351-
dc.identifier.epage367-
dc.identifier.isiWOS:001008380600024-
dc.identifier.eisbn9783031255557-
dc.identifier.issnl2511-1256-

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