Decision algorithms for Unmanned Underwater Vehicles during offensive operations

The field of research involving autonomous vehicles has expanded greatly over the past decade. This thesis addresses the case of a system of Unmanned Underwater Vehicles (UUVs) operating in littoral areas in an offensive capacity. A series of complementary algorithms were designed to collect information about an enemy vessell, and subsequently use this information to both select and move to a prefered intercept location that maximizes the opportunity to both re-acquire and destroy an enemy vessel. Additionally, within the context of a specifically designed simulation, key parameter changes were analyzed to determine their effectiveness to improve the system's performance as measured by four measures of effectiveness. A methodology was also designed to optimize the location of the engaging UUVs to maximize their effectiveness, and capitalize on the enemy movement within the operational area. Results are presented for both original locations and optimized locations, and initial findings provide insight into the effectiveness of the designed algorithms and statistical inference of these key parameter changes.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• Smith, Tyler B. (Tyler Bradford)

Advisor dc:contributor.advisor

• Margaret F. Nervegna and Cynthia Barnhart.

• Civil and Environmental Engineering.

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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/35081

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/35081