Adaptive path planning for depth‐constrained bathymetric mapping with an autonomous surface vessel.
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| Title: | Adaptive path planning for depth‐constrained bathymetric mapping with an autonomous surface vessel. |
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| Authors: | Wilson, Troy1 t.wilson@acfr.usyd.edu.au, Williams, Stefan B.1 |
| Source: | Journal of Field Robotics. May2018, Vol. 35 Issue 3, p345-358. 14p. |
| Subjects: | Robotics in oceanography, Adaptive control systems, Bathymetric maps, Autonomous vehicles, Submarine topography, Gaussian processes |
| Abstract: | Abstract: This paper describes the design, implementation, and testing of a suite of algorithms to enable depth‐constrained autonomous bathymetric (underwater topography) mapping by an autonomous surface vessel (ASV). Given a target depth and a bounding polygon, the ASV will find and follow the intersection of the bounding polygon and the depth contour as modeled online with a Gaussian process (GP). This intersection, once mapped, will then be used as a boundary within which a path will be planned for coverage to build a map of the bathymetry. Efficient methods are implemented enabling online fitting, prediction and hyperparameter optimization within the GP framework on a small embedded PC. New algorithms are introduced for the partitioning of convex polygons to allow efficient path planning for coverage. These algorithms are tested both in simulation and in the field with a small twin hull differential thrust vessel built for the task. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Field Robotics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 129102944 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Adaptive path planning for depth‐constrained bathymetric mapping with an autonomous surface vessel. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wilson%2C+Troy%22">Wilson, Troy</searchLink><relatesTo>1</relatesTo><i> t.wilson@acfr.usyd.edu.au</i><br /><searchLink fieldCode="AR" term="%22Williams%2C+Stefan+B%2E%22">Williams, Stefan B.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Field+Robotics%22">Journal of Field Robotics</searchLink>. May2018, Vol. 35 Issue 3, p345-358. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Robotics+in+oceanography%22">Robotics in oceanography</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Bathymetric+maps%22">Bathymetric maps</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Submarine+topography%22">Submarine topography</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+processes%22">Gaussian processes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: This paper describes the design, implementation, and testing of a suite of algorithms to enable depth‐constrained autonomous bathymetric (underwater topography) mapping by an autonomous surface vessel (ASV). Given a target depth and a bounding polygon, the ASV will find and follow the intersection of the bounding polygon and the depth contour as modeled online with a Gaussian process (GP). This intersection, once mapped, will then be used as a boundary within which a path will be planned for coverage to build a map of the bathymetry. Efficient methods are implemented enabling online fitting, prediction and hyperparameter optimization within the GP framework on a small embedded PC. New algorithms are introduced for the partitioning of convex polygons to allow efficient path planning for coverage. These algorithms are tested both in simulation and in the field with a small twin hull differential thrust vessel built for the task. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Field Robotics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=129102944 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/rob.21718 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 345 Subjects: – SubjectFull: Robotics in oceanography Type: general – SubjectFull: Adaptive control systems Type: general – SubjectFull: Bathymetric maps Type: general – SubjectFull: Autonomous vehicles Type: general – SubjectFull: Submarine topography Type: general – SubjectFull: Gaussian processes Type: general Titles: – TitleFull: Adaptive path planning for depth‐constrained bathymetric mapping with an autonomous surface vessel. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wilson, Troy – PersonEntity: Name: NameFull: Williams, Stefan B. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 15564959 Numbering: – Type: volume Value: 35 – Type: issue Value: 3 Titles: – TitleFull: Journal of Field Robotics Type: main |
| ResultId | 1 |
