Deakin University
Browse

Controlling minimally-actuated vehicles for applications in ocean observation

Version 2 2024-06-04, 07:43
Version 1 2016-10-13, 10:33
conference contribution
posted on 2024-06-04, 07:43 authored by RN Smith, Van Thanh HuynhVan Thanh Huynh
Establishing a persistent presence in the ocean with an AUV to observe temporal variability of large-scale ocean processes requires a unique sensor platform. In this paper, we propose a strategy that utilizes ocean model predictions to increase the autonomy and control of Lagrangian or profiling floats for precisely this purpose. An A * planner is applied to a local controllability map generated from predictions of ocean currents to compute a path between prescribed waypoints that has the highest likelihood of successful execution. The control to follow the planned path is computed by use of a model predictive controller. This controller is designed to select the best depth for the vehicle to exploit ambient currents to reach the goal waypoint. Mission constraints are employed to simulate a practical data collection mission. Results are presented in simulation for a mission off the coast of Los Angeles, CA USA, and show surprising results in the ability of a Lagrangian float to reach a desired location.

History

Volume

45

Pagination

31-36

Location

Porto, Portugal

Start date

2012-04-10

End date

2012-04-12

ISSN

1474-6670

ISBN-13

9783902823199

Language

eng

Publication classification

E Conference publication, E1.1 Full written paper - refereed

Copyright notice

2012, IFAC

Editor/Contributor(s)

Pereira FL

Title of proceedings

IFAC Proceedings Volumes : NGCUV 2012 : Proceedings of the Navigation, Guidance & Control of Underwater Vehicles 2012 Workshop

Event

Navigation, Guidance & Control of Underwater Vehicles. Workshop (3rd : 2012 : Porto, Portugal)

Issue

5

Publisher

Elsevier

Place of publication

Amsterdam, The Netherlands

Usage metrics

    Research Publications

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC