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Terrain-aided navigation for long-endurance and deep-rated autonomous underwater vehicles

Terrain-aided navigation for long-endurance and deep-rated autonomous underwater vehicles
Terrain-aided navigation for long-endurance and deep-rated autonomous underwater vehicles

Terrain-aided navigation (TAN) is a localisation method which uses bathymetric measurements for bounding the growth in inertial navigation error. The minimisation of navigation errors is of particular importance for long-endurance autonomous underwater vehicles (AUVs). This type of AUV requires simple and effective on-board navigation solutions to undertake long-range missions, operating for months rather than hours or days, without reliance on external support systems. Consequently, a suitable navigation solution has to fulfil two main requirements: (a) bounding the navigation error, and (b) conforming to energy constraints and conserving on-board power. This study proposes a low-complexity particle filter-based TAN algorithm for Autosub Long Range, a long-endurance deep-rated AUV. This is a light and tractable filter that can be implemented on-board in real time. The potential of the algorithm is investigated by evaluating its performance using field data from three deep (up to 3,700 m) and long-range (up to 195 km in 77 hr) missions performed in the Southern Ocean during April 2017. The results obtained using TAN are compared to on-board estimates, computed via dead reckoning, and ultrashort baseline (USBL) measurements, treated as baseline locations, sporadically recorded by a support ship. Results obtained through postprocessing demonstrate that TAN has the potential to prolong underwater missions to a range of hundreds of kilometres without the need for intermittent surfacing to obtain global positioning system fixes. During each of the missions, the system performed 20 Monte Carlo runs. Throughout each run, the algorithm maintained convergence and bounded error, with high estimation repeatability achieved between all runs, despite the limited suite of localisation sensors.

long-range AUV navigation, marine robotics, nonlinear filtering, terrain-aided navigation
1556-4959
447-474
Salavasidis, Georgios
d412a9ad-2659-4e19-9547-fcf0262b0066
Munafò, Andrea
cac7d755-d119-49a3-b99f-ca62778ee9bf
Harris, Catherine A.
34cf0f4b-4e11-4ed2-a3ad-5596dd765572
Prampart, Thomas
9823a667-706b-4cf8-920f-ab3226f67e96
Templeton, Robert
2d8b6500-94f7-4492-bac9-b82e66838165
Smart, Micheal
1699f613-b3ff-460a-aaf9-2546e0de4b51
Roper, Daniel T.
d8e1c667-1956-4a33-a50c-955c3941ed52
Pebody, Miles
061eafd0-c200-4701-b465-274140a4602b
McPhail, Stephen D.
58ac4bcd-26a6-4845-8e81-6d6a8f18aed7
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Phillips, Alexander B.
f565b1da-6881-4e2a-8729-c082b869028f
Salavasidis, Georgios
d412a9ad-2659-4e19-9547-fcf0262b0066
Munafò, Andrea
cac7d755-d119-49a3-b99f-ca62778ee9bf
Harris, Catherine A.
34cf0f4b-4e11-4ed2-a3ad-5596dd765572
Prampart, Thomas
9823a667-706b-4cf8-920f-ab3226f67e96
Templeton, Robert
2d8b6500-94f7-4492-bac9-b82e66838165
Smart, Micheal
1699f613-b3ff-460a-aaf9-2546e0de4b51
Roper, Daniel T.
d8e1c667-1956-4a33-a50c-955c3941ed52
Pebody, Miles
061eafd0-c200-4701-b465-274140a4602b
McPhail, Stephen D.
58ac4bcd-26a6-4845-8e81-6d6a8f18aed7
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Phillips, Alexander B.
f565b1da-6881-4e2a-8729-c082b869028f

Salavasidis, Georgios, Munafò, Andrea, Harris, Catherine A., Prampart, Thomas, Templeton, Robert, Smart, Micheal, Roper, Daniel T., Pebody, Miles, McPhail, Stephen D., Rogers, Eric and Phillips, Alexander B. (2019) Terrain-aided navigation for long-endurance and deep-rated autonomous underwater vehicles. Journal of Field Robotics, 36 (2), 447-474. (doi:10.1002/rob.21832).

Record type: Article

Abstract

Terrain-aided navigation (TAN) is a localisation method which uses bathymetric measurements for bounding the growth in inertial navigation error. The minimisation of navigation errors is of particular importance for long-endurance autonomous underwater vehicles (AUVs). This type of AUV requires simple and effective on-board navigation solutions to undertake long-range missions, operating for months rather than hours or days, without reliance on external support systems. Consequently, a suitable navigation solution has to fulfil two main requirements: (a) bounding the navigation error, and (b) conforming to energy constraints and conserving on-board power. This study proposes a low-complexity particle filter-based TAN algorithm for Autosub Long Range, a long-endurance deep-rated AUV. This is a light and tractable filter that can be implemented on-board in real time. The potential of the algorithm is investigated by evaluating its performance using field data from three deep (up to 3,700 m) and long-range (up to 195 km in 77 hr) missions performed in the Southern Ocean during April 2017. The results obtained using TAN are compared to on-board estimates, computed via dead reckoning, and ultrashort baseline (USBL) measurements, treated as baseline locations, sporadically recorded by a support ship. Results obtained through postprocessing demonstrate that TAN has the potential to prolong underwater missions to a range of hundreds of kilometres without the need for intermittent surfacing to obtain global positioning system fixes. During each of the missions, the system performed 20 Monte Carlo runs. Throughout each run, the algorithm maintained convergence and bounded error, with high estimation repeatability achieved between all runs, despite the limited suite of localisation sensors.

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More information

Accepted/In Press date: 19 August 2018
e-pub ahead of print date: 8 November 2018
Published date: March 2019
Keywords: long-range AUV navigation, marine robotics, nonlinear filtering, terrain-aided navigation

Identifiers

Local EPrints ID: 428141
URI: https://eprints.soton.ac.uk/id/eprint/428141
ISSN: 1556-4959
PURE UUID: 4e095427-fc38-44de-a7f1-6167c55bd291
ORCID for Eric Rogers: ORCID iD orcid.org/0000-0003-0179-9398
ORCID for Alexander B. Phillips: ORCID iD orcid.org/0000-0003-3234-8506

Catalogue record

Date deposited: 12 Feb 2019 17:30
Last modified: 20 Jul 2019 01:24

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Contributors

Author: Georgios Salavasidis
Author: Andrea Munafò
Author: Catherine A. Harris
Author: Thomas Prampart
Author: Robert Templeton
Author: Micheal Smart
Author: Daniel T. Roper
Author: Miles Pebody
Author: Stephen D. McPhail
Author: Eric Rogers ORCID iD

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