The University of Southampton
University of Southampton Institutional Repository

Risk-based path planning for autonomous underwater vehicles in an oil spill environment

Risk-based path planning for autonomous underwater vehicles in an oil spill environment
Risk-based path planning for autonomous underwater vehicles in an oil spill environment
Autonomous underwater vehicles (AUVs) are advanced platforms for detecting and mapping oil spills in deep water. However, their applications in complex spill environments have been hindered by the risk of vehicle loss. Path planning for AUVs is an effective technique for mitigating such risks and ensuring safer routing. Yet previous studies did not address path searching problems for AUVs based on probabilistic risk reasoning. This study aims to propose an offboard risk-based path planning approach for AUVs operating in an oil spill environment. A risk model based on the Bayesian network was developed for probabilistic reasoning of risk states given varied environmental observations. This risk model further assisted in generating a spatially-distributed risk map covering a potential mission area. An A*-based searching algorithm was then employed to plan an optimal-risk path through the constructed risk map. The proposed planner was applied in a case study with a Slocum G1 Glider in a real-world spill environment around Baffin Bay. Simulation results proved that the optimal-risk planner outperforms in risk mitigation while achieving competitive path lengths and mission efficiency. The proposed method is not constrained to AUVs but can be adapted to other marine robotic systems.
Autonomous underwater vehicles, probabilistic risk model, global path planning, A* algorithm, oil spill environment
0029-8018
1-32
Chen, Xi
8bdb9873-52cb-4688-8cae-b4da945e0662
Bose, Neil
37b8d6e4-fd93-4bbe-827c-f060e0ce0851
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Khan, Faisal
e3810728-8747-4b3c-aa95-eae12d7e1759
Millar, Gina
42b4616e-217c-4371-9752-db02e373ec97
Bulger, Craig
b0e4b70c-db40-40ba-9b7b-7e8771fcfed3
Zou, Ting
f985d6fe-f153-44d1-820e-570b19364f25
Chen, Xi
8bdb9873-52cb-4688-8cae-b4da945e0662
Bose, Neil
37b8d6e4-fd93-4bbe-827c-f060e0ce0851
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Khan, Faisal
e3810728-8747-4b3c-aa95-eae12d7e1759
Millar, Gina
42b4616e-217c-4371-9752-db02e373ec97
Bulger, Craig
b0e4b70c-db40-40ba-9b7b-7e8771fcfed3
Zou, Ting
f985d6fe-f153-44d1-820e-570b19364f25

Chen, Xi, Bose, Neil, Brito, Mario, Khan, Faisal, Millar, Gina, Bulger, Craig and Zou, Ting (2022) Risk-based path planning for autonomous underwater vehicles in an oil spill environment. Ocean Engineering, 1-32. (In Press)

Record type: Article

Abstract

Autonomous underwater vehicles (AUVs) are advanced platforms for detecting and mapping oil spills in deep water. However, their applications in complex spill environments have been hindered by the risk of vehicle loss. Path planning for AUVs is an effective technique for mitigating such risks and ensuring safer routing. Yet previous studies did not address path searching problems for AUVs based on probabilistic risk reasoning. This study aims to propose an offboard risk-based path planning approach for AUVs operating in an oil spill environment. A risk model based on the Bayesian network was developed for probabilistic reasoning of risk states given varied environmental observations. This risk model further assisted in generating a spatially-distributed risk map covering a potential mission area. An A*-based searching algorithm was then employed to plan an optimal-risk path through the constructed risk map. The proposed planner was applied in a case study with a Slocum G1 Glider in a real-world spill environment around Baffin Bay. Simulation results proved that the optimal-risk planner outperforms in risk mitigation while achieving competitive path lengths and mission efficiency. The proposed method is not constrained to AUVs but can be adapted to other marine robotic systems.

Text
Risk-based path planning for autonomous underwater vehicles in an oil spill environment (version6) - Accepted Manuscript
Restricted to Repository staff only until 31 October 2023.
Request a copy

More information

Accepted/In Press date: 31 October 2022
Keywords: Autonomous underwater vehicles, probabilistic risk model, global path planning, A* algorithm, oil spill environment

Identifiers

Local EPrints ID: 471247
URI: http://eprints.soton.ac.uk/id/eprint/471247
ISSN: 0029-8018
PURE UUID: b3daea47-880d-44e0-a2dd-9ebfeb16165d
ORCID for Mario Brito: ORCID iD orcid.org/0000-0002-1779-4535

Catalogue record

Date deposited: 01 Nov 2022 17:41
Last modified: 05 Nov 2022 02:41

Export record

Contributors

Author: Xi Chen
Author: Neil Bose
Author: Mario Brito ORCID iD
Author: Faisal Khan
Author: Gina Millar
Author: Craig Bulger
Author: Ting Zou

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×