Active perception for plume source localisation with underwater gliders

Lee, KMB; Lee, JJH; Yoo, C; Hollings, B; Fitch, R

Active perception for plume source localisation with underwater gliders

Lee, KMB

Lee, JJH Yoo, C

Hollings, B Fitch, R

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Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Robotics and Automation, ACRA, 2018, 2018-December
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Lee, KMB https://orcid.org/0000-0003-1449-2125	en_US
dc.contributor.author	Lee, JJH	en_US
dc.contributor.author	Yoo, C https://orcid.org/0000-0002-2773-0527	en_US
dc.contributor.author	Hollings, B	en_US
dc.contributor.author	Fitch, R https://orcid.org/0000-0003-2215-5188	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Australasian Conference on Robotics and Automation, ACRA, 2018, 2018-December	en_US
dc.identifier.issn	1448-2053	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129514
dc.description.abstract	© 2018 Australasian Robotics and Automation Association. All rights reserved. We consider the problem of localising an unknown underwater plume source in an energy-optimal manner. We first develop a specialised Gaussian process (GP) regression technique for estimating the source location given concentration measurements and an ambient flow field. Then, we use the GP upper confidence bound (GP-UCB) for active perception to choose sampling locations that both improve the estimate of the source and lead the glider to the correct source location. A trim-based FMT∗planner is then used to find the sequence of controls that minimise the energy consumption. We provide a theoretical guarantee on the performance of the algorithm, and demonstrate the algorithm using both artificial and experimental datasets.	en_US
dc.relation.ispartof	Australasian Conference on Robotics and Automation, ACRA	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Active perception for plume source localisation with underwater gliders	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-December	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-December	en_US

Abstract:

© 2018 Australasian Robotics and Automation Association. All rights reserved. We consider the problem of localising an unknown underwater plume source in an energy-optimal manner. We first develop a specialised Gaussian process (GP) regression technique for estimating the source location given concentration measurements and an ambient flow field. Then, we use the GP upper confidence bound (GP-UCB) for active perception to choose sampling locations that both improve the estimate of the source and lead the glider to the correct source location. A trim-based FMT∗planner is then used to find the sequence of controls that minimise the energy consumption. We provide a theoretical guarantee on the performance of the algorithm, and demonstrate the algorithm using both artificial and experimental datasets.

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http://hdl.handle.net/10453/129514