Path planning of Autonomous Underwater Vehicles for adaptive sampling

This thesis develops new methods for path planning of Autonomous Underwater Vehicles for adaptive sampling. The problem is approached in an optimization framework and two methods are developed to solve it based on Mixed Integer Programming (MIP). The first method is predicated on network-flow ideas and is shown to have similarities to the Selective Traveling Salesman Problem. An important classification of the set of path-planning problems to be solved is the number of days that are involved in the complete sampling mission. Intrinsic limitations of the first network-flow-based method limit it to single day adaptive sampling missions. The second method emerged by taking into consideration the limitations of the first method and is based on a general MIP formulation. It is a more powerful formulation as it can handle multiple-day adaptive sampling missions, it is scalable as it can address the multiple vehicle case with ease, and it is more easily extensible to cover unforeseen situations that might arise as adaptive sampling concepts and needs evolve. The second formulation also allowed solution of auxiliary problems, required to determine suitable initial conditions for the main method. The method is applied to various test problems including evaluation of performance issues.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• Yilmaz, Namik Kemal, 1975-

Advisor dc:contributor.advisor

• Nicholas M. Patrikalakis.

• Mechanical 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/1721.1/35618

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/35618