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Massachusetts Institute of Technology and Woods Hole Oceanographic Institution

Autonomous & adaptive oceanographic feature tracking on board autonomous underwater vehicles

Abstract

dc:description.abstract

The capabilities of autonomous underwater vehicles (AUVs) and their ability to perform tasks both autonomously and adaptively are rapidly improving, and the desire to quickly and efficiently sample the ocean environment as Earth's climate changes and natural disasters occur has increased significantly in the last decade. As such, this thesis proposes to develop a method for single and multiple AUVs to collaborate autonomously underwater while autonomously adapting their motion to changes in their local environments, allowing them to sample and track various features of interest with greater efficiency and synopticity than previously possible with preplanned AUV or ship-based surveys. This concept is demonstrated to work in field testing on multiple occasions: with a single AUV autonomously and adaptively tracking the depth range of a thermocline or acousticline, and with two AUVs coordinating their motion to collect a data set in which internal waves could be detected. This research is then taken to the next level by exploring the problem of adaptively and autonomously tracking spatiotemporally dynamic underwater fronts and plumes using individual and autonomously collaborating AUVs.

Degree

thesis:*
Grantor dc:publisher
Massachusetts Institute of Technology and Woods Hole Oceanographic Institution
Year dc:date.issued
2015

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Petillo, Stephanie M.

Subjects

dc:subject × 2

Rights

Language dc:language.iso
en_US

Identifiers

dc:identifier.*
OAI identifier oai:identifier
oai:darchive.mblwhoilibrary.org:1912/7129

Chain of custody

source
Harvested from
Woods Hole Oceanographic Institute
Base URL
darchive.mblwhoilibrary.org/server/oai/request
Last updated
2026-07-27
Source record
OAI-PMH GetRecord
citation

Petillo, Stephanie M.. Autonomous & adaptive oceanographic feature tracking on board autonomous underwater vehicles. Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2015. https://hdl.handle.net/1912/7129