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Massachusetts Institute of Technology

Autonomous adaptation and collaboration of unmanned vehicles for tracking submerged contacts

Abstract

dc:description.abstract

Autonomous operations are vital to future naval operations. Unmanned systems, including autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs), are anticipated to play a key role for critical tasks such as mine countermeasures (MCM) and anti-submarine warfare (ASW). Addressing these issues with autonomous systems poses a host of difficult research challenges, including sensing, power, acoustic communications, navigation, and autonomous decision-making. This thesis addresses the issues of sensing and autonomy, studying the benefits of adaptive motion in overcoming partial observability of sensor observations. We focus on the challenge of target tracking with range-only measurements, relying on adaptive motion to localize and track maneuvering targets. Our primary contribution has been to develop new MOOS-IvP autonomy and state estimation modules to enable an autonomous surface vehicle to locate and track a submerged contact using range-only sensor information. These capabilities were initially tested in simulation for increasing levels of complexity of target motion, and subsequently evaluated in a field test with a Kingfisher ASV. Our results demonstrate the feasibility, in a controlled environment, to localize and track a maneuvering undersea target using range-only measurements.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Dept. of Mechanical Engineering.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2012

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Privette, Andrew Jamie
Advisor dc:contributor.advisor
  • John J. Leonard and Michael R. Benjamin.

Subjects

dc:subject × 2

Rights

dc:rights
Statement dc:rights
  • M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/74898
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/74898

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Privette, Andrew Jamie. Autonomous adaptation and collaboration of unmanned vehicles for tracking submerged contacts. Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/74898