Advanced Search
DSpace@MIT

Autonomous adaptation and collaboration of unmanned vehicles for tracking submerged contacts

Research and Teaching Output of the MIT Community

Show simple item record

dc.contributor.advisor John J. Leonard and Michael R. Benjamin. en_US
dc.contributor.author Privette, Andrew Jamie en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.date.accessioned 2012-11-19T19:16:35Z
dc.date.available 2012-11-19T19:16:35Z
dc.date.copyright 2012 en_US
dc.date.issued 2012 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/74898
dc.description Thesis (Nav. E.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 103-106). en_US
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. en_US
dc.description.statementofresponsibility by Andrew J. Privette. en_US
dc.format.extent 106 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
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. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Mechanical Engineering. en_US
dc.subject Electrical Engineering and Computer Science. en_US
dc.title Autonomous adaptation and collaboration of unmanned vehicles for tracking submerged contacts en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.description.degree Nav.E. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Mechanical Engineering. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.identifier.oclc 815409156 en_US


Files in this item

Name Size Format Description
815409156.pdf 14.28Mb PDF Preview, non-printable (open to all)
815409156-MIT.pdf 14.28Mb PDF Full printable version (MIT only)

This item appears in the following Collection(s)

Show simple item record

MIT-Mirage