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Route optimization under uncertainty for Unmanned Underwater Vehicles

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dc.contributor.advisor Patrick Jaillet. en_US Cates, Jacob Roy en_US
dc.contributor.other Massachusetts Institute of Technology. Operations Research Center. en_US 2011-12-19T18:49:22Z 2011-12-19T18:49:22Z 2011 en_US 2011 en_US
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2011. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 87). en_US
dc.description.abstract As our technology continues to increase, our military wishes to reduce the risk service members endure by using unmanned vehicles. These unmanned vehicles will, over time, become more independent and trustworthy in our operational military. The goal of this thesis is to improve the intelligence an unmanned vehicle has in its decision making to keep up with ever increasing capabilities. In this thesis, we assume an Unmanned Underwater Vehicle (UUV) is given tasks and must decide which ones to perform (or not perform) in which order. If there is enough time and energy to perform all of the tasks, then the UUV only needs to solve a traveling salesman problem to find the best order. We focus on a tightly constrained situation, where the UUV must choose which tasks to perform to collect the highest reward. In prize collecting traveling salesman problems, authors are often dismissive about stochastic problem parameters, and are satisfied with using expected value of random variables. In this thesis a more rigorous probabilistic model is formulated which establishes a guaranteed confidence level for the probability the UUV breaks mission critical time and energy constraints. The formulation developed takes the stochasticity of the problem parameters into account and produces solutions which are robust. The thesis first presents a linear programming problem which calculates the transition probabilities for a specific route. This linear programming problem is then used to create a constraint which forces the UUV to choose a route that maintains an appropriate confidence level for satisfying the time and energy constraints. Once the exact model is created, heuristics are discussed and analyzed. The heuristics are designed to provide "good"' solutions for larger sized problems and maintain a relatively low run time. en_US
dc.description.statementofresponsibility by Jacob Roy Cates. en_US
dc.format.extent 87 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 en_US
dc.subject Operations Research Center. en_US
dc.title Route optimization under uncertainty for Unmanned Underwater Vehicles en_US
dc.title.alternative Route optimization under uncertainty for UUVs en_US
dc.type Thesis en_US S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Operations Research Center. en_US
dc.identifier.oclc 767528290 en_US

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