Teach‐and‐repeat path following for an autonomous underwater vehicle
This paper presents a teach-and-repeat path-following method for an autonomous underwater vehicle (AUV) navigating long distances in environments where external navigation aides are denied. This method utilizes sonar images to construct a series of reference views along a path, stored as a topologic...
Published in: | Journal of Field Robotics |
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2018
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Online Access: | https://escholarship.org/uc/item/4s73v53p https://escholarship.org/content/qt4s73v53p/qt4s73v53p.pdf https://doi.org/10.1002/rob.21776 |
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ftcdlib:oai:escholarship.org:ark:/13030/qt4s73v53p 2024-09-15T18:20:12+00:00 Teach‐and‐repeat path following for an autonomous underwater vehicle King, Peter Vardy, Andrew Forrest, Alexander L 748 - 763 2018-08-01 application/pdf https://escholarship.org/uc/item/4s73v53p https://escholarship.org/content/qt4s73v53p/qt4s73v53p.pdf https://doi.org/10.1002/rob.21776 unknown eScholarship, University of California qt4s73v53p https://escholarship.org/uc/item/4s73v53p https://escholarship.org/content/qt4s73v53p/qt4s73v53p.pdf doi:10.1002/rob.21776 public Journal of Field Robotics, vol 35, iss 5 Life Below Water extreme environments marine robotics planning underwater robotics Artificial Intelligence and Image Processing Electrical and Electronic Engineering Mechanical Engineering Industrial Engineering & Automation article 2018 ftcdlib https://doi.org/10.1002/rob.21776 2024-06-28T06:28:19Z This paper presents a teach-and-repeat path-following method for an autonomous underwater vehicle (AUV) navigating long distances in environments where external navigation aides are denied. This method utilizes sonar images to construct a series of reference views along a path, stored as a topological map. The AUV can then renavigate along this path, either to return to the start location or to repeat the route. Utilizing unique assumptions about the sonar image-generation process, this system exhibits robust image-matching capabilities, providing observations to a discrete Bayesian filter that maintains an estimate of progress along the path. Image-matching also provides an estimate of offset from the path, allowing the AUV to correct its heading and effectively close the gap. Over a series of field trials, this system demonstrated online control of an AUV in the ocean environment of Holyrood Arm, Newfoundland and Labrador, Canada. The system was implemented on an International Submarine Engineering Ltd. Explorer AUV and performed multiple path completions over both a 1 and 5 km track. These trials illustrated an AUV operating in a fully autonomous mode, in which navigation was driven solely by sensor feedback and adaptive control. Path-following performance was as desired, with the AUV maintaining close offset to the path. Article in Journal/Newspaper Newfoundland University of California: eScholarship Journal of Field Robotics 35 5 748 763 |
institution |
Open Polar |
collection |
University of California: eScholarship |
op_collection_id |
ftcdlib |
language |
unknown |
topic |
Life Below Water extreme environments marine robotics planning underwater robotics Artificial Intelligence and Image Processing Electrical and Electronic Engineering Mechanical Engineering Industrial Engineering & Automation |
spellingShingle |
Life Below Water extreme environments marine robotics planning underwater robotics Artificial Intelligence and Image Processing Electrical and Electronic Engineering Mechanical Engineering Industrial Engineering & Automation King, Peter Vardy, Andrew Forrest, Alexander L Teach‐and‐repeat path following for an autonomous underwater vehicle |
topic_facet |
Life Below Water extreme environments marine robotics planning underwater robotics Artificial Intelligence and Image Processing Electrical and Electronic Engineering Mechanical Engineering Industrial Engineering & Automation |
description |
This paper presents a teach-and-repeat path-following method for an autonomous underwater vehicle (AUV) navigating long distances in environments where external navigation aides are denied. This method utilizes sonar images to construct a series of reference views along a path, stored as a topological map. The AUV can then renavigate along this path, either to return to the start location or to repeat the route. Utilizing unique assumptions about the sonar image-generation process, this system exhibits robust image-matching capabilities, providing observations to a discrete Bayesian filter that maintains an estimate of progress along the path. Image-matching also provides an estimate of offset from the path, allowing the AUV to correct its heading and effectively close the gap. Over a series of field trials, this system demonstrated online control of an AUV in the ocean environment of Holyrood Arm, Newfoundland and Labrador, Canada. The system was implemented on an International Submarine Engineering Ltd. Explorer AUV and performed multiple path completions over both a 1 and 5 km track. These trials illustrated an AUV operating in a fully autonomous mode, in which navigation was driven solely by sensor feedback and adaptive control. Path-following performance was as desired, with the AUV maintaining close offset to the path. |
format |
Article in Journal/Newspaper |
author |
King, Peter Vardy, Andrew Forrest, Alexander L |
author_facet |
King, Peter Vardy, Andrew Forrest, Alexander L |
author_sort |
King, Peter |
title |
Teach‐and‐repeat path following for an autonomous underwater vehicle |
title_short |
Teach‐and‐repeat path following for an autonomous underwater vehicle |
title_full |
Teach‐and‐repeat path following for an autonomous underwater vehicle |
title_fullStr |
Teach‐and‐repeat path following for an autonomous underwater vehicle |
title_full_unstemmed |
Teach‐and‐repeat path following for an autonomous underwater vehicle |
title_sort |
teach‐and‐repeat path following for an autonomous underwater vehicle |
publisher |
eScholarship, University of California |
publishDate |
2018 |
url |
https://escholarship.org/uc/item/4s73v53p https://escholarship.org/content/qt4s73v53p/qt4s73v53p.pdf https://doi.org/10.1002/rob.21776 |
op_coverage |
748 - 763 |
genre |
Newfoundland |
genre_facet |
Newfoundland |
op_source |
Journal of Field Robotics, vol 35, iss 5 |
op_relation |
qt4s73v53p https://escholarship.org/uc/item/4s73v53p https://escholarship.org/content/qt4s73v53p/qt4s73v53p.pdf doi:10.1002/rob.21776 |
op_rights |
public |
op_doi |
https://doi.org/10.1002/rob.21776 |
container_title |
Journal of Field Robotics |
container_volume |
35 |
container_issue |
5 |
container_start_page |
748 |
op_container_end_page |
763 |
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1810458571092000768 |