Path Planning in Uncertain Ocean Currents using Ensemble Forecasts

Yoo, C; Heon Lee, JJ; Anstee, S; Fitch, R

Path Planning in Uncertain Ocean Currents using Ensemble Forecasts

Yoo, C

Heon Lee, JJ Anstee, S Fitch, R

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, 2021-May, pp. 8323-8329
Issue Date:: 2021-10-18

Closed Access

	Filename	Description	Size
	Path_Planning_in_Uncertain_Ocean_Currents_using_Ensemble_Forecasts.pdf	Published version	7.63 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Yoo, C https://orcid.org/0000-0002-2773-0527
dc.contributor.author	Heon Lee, JJ
dc.contributor.author	Anstee, S
dc.contributor.author	Fitch, R https://orcid.org/0000-0003-2215-5188
dc.date	2021-05-30
dc.date.accessioned	2022-06-06T06:25:27Z
dc.date.available	2022-06-06T06:25:27Z
dc.date.issued	2021-10-18
dc.identifier.citation	2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, 2021-May, pp. 8323-8329
dc.identifier.isbn	9781728190778
dc.identifier.issn	1050-4729
dc.identifier.issn	2577-087X
dc.identifier.uri	http://hdl.handle.net/10453/157977
dc.description.abstract	We present a path planning framework for marine robots subject to uncertain ocean currents that exploits data from ensemble forecasting, which is a technique for current prediction used in oceanography. Ensemble forecasts represent a distribution of predicted currents as a set of flow fields that are considered to be equally likely. We show that the typical approach of computing the vector-wise mean and variance over this set can yield meaningless results, and propose an alternative approach that considers each flow field in the ensemble simultaneously. Our framework finds a sequence of vehicle controls that minimises the root-mean-square error distance (RMSE) over the full set of ensemble-induced trajectories. The key to achieving computational efficiency in this approach is our use of Monte Carlo tree search (MCTS) with a specialised heuristic that improves convergence rate while preserving asymptotic optimality and the anytime property. We demonstrate our results using real ensemble forecasts provided by the Australian Bureau of Meteorology, and provide comparisons with the deterministic mean-based approach where we observe RMSE reductions of 92% and 43% in two example scenarios. Further, we argue that the framework can be used in a plan-as-you-go manner where ensemble forecasts change over time. These results help to introduce ensemble forecasts as a viable source of data to improve path planning in marine robotics.
dc.language	en
dc.publisher	IEEE
dc.relation	Department of Industry, Innovation and ScienceICG000307
dc.relation.ispartof	2021 IEEE International Conference on Robotics and Automation (ICRA)
dc.relation.ispartof	2021 IEEE International Conference on Robotics and Automation
dc.relation.ispartofseries	IEEE International Conference on Robotics and Automation ICRA
dc.relation.isbasedon	10.1109/icra48506.2021.9561626
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Path Planning in Uncertain Ocean Currents using Ensemble Forecasts
dc.type	Conference Proceeding
utslib.citation.volume	2021-May
utslib.location.activity	Xi'an, China
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/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-06-06T06:25:19Z
pubs.finish-date	2021-06-05
pubs.publication-status	Published
pubs.start-date	2021-05-30
pubs.volume	2021-May

Abstract:

We present a path planning framework for marine robots subject to uncertain ocean currents that exploits data from ensemble forecasting, which is a technique for current prediction used in oceanography. Ensemble forecasts represent a distribution of predicted currents as a set of flow fields that are considered to be equally likely. We show that the typical approach of computing the vector-wise mean and variance over this set can yield meaningless results, and propose an alternative approach that considers each flow field in the ensemble simultaneously. Our framework finds a sequence of vehicle controls that minimises the root-mean-square error distance (RMSE) over the full set of ensemble-induced trajectories. The key to achieving computational efficiency in this approach is our use of Monte Carlo tree search (MCTS) with a specialised heuristic that improves convergence rate while preserving asymptotic optimality and the anytime property. We demonstrate our results using real ensemble forecasts provided by the Australian Bureau of Meteorology, and provide comparisons with the deterministic mean-based approach where we observe RMSE reductions of 92% and 43% in two example scenarios. Further, we argue that the framework can be used in a plan-as-you-go manner where ensemble forecasts change over time. These results help to introduce ensemble forecasts as a viable source of data to improve path planning in marine robotics.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/157977