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UAV mission planning under uncertainty

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dc.contributor.advisor Cynthia Barnhart. en_US
dc.contributor.author Sakamoto, Philemon en_US
dc.contributor.other Massachusetts Institute of Technology. Operations Research Center. en_US
dc.date.accessioned 2007-02-21T13:10:41Z
dc.date.available 2007-02-21T13:10:41Z
dc.date.copyright 2006 en_US
dc.date.issued 2006 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/36230
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006. en_US
dc.description Includes bibliographical references (p. 205-209). en_US
dc.description.abstract With the continued development of high endurance Unmanned Aerial Vehicles (UAV) and Unmanned Combat Aerial Vehicles (UCAV) that are capable of performing autonomous fiunctions across the spectrum of military operations, one can envision a future military in which Air Component Commanders control forces comprised exclusively of unmanned vehicles. In order to properly manage and fully realize the capabilities of this UAV force, a control system must be in place that directs UAVs to targets and coordinates missions in a manner that provides an efficient allocation of resources. Additionally, a mission planner should account for the uncertainty inherent in the operations. Uncertainty, or stochasticity, manifests itself in most operations known to man. In the battlefield, such unknowns are especially real; the phenomenon is known as the fog of war. A good planner should develop plans that provide an efficient allocation of resources and take advantage of the system's true potential, while still providing ample "robustness" ill plans so that they are more likely executable and for a longer period of time. en_US
dc.description.abstract (cont.) In this research, we develop a UAV Mission Planner that couples the scheduling of tasks with the assignment of these tasks to UAVs, while maintaining the characteristics of longevity and efficiency in its plans. The planner is formulated as a Mixed Integer Program (MIP) that incorporates the Robust Optimization technique proposed by Bertsimas and Sim [12]. en_US
dc.description.statementofresponsibility by Philemon Sakamoto. en_US
dc.format.extent 209 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
dc.subject Operations Research Center. en_US
dc.title UAV mission planning under uncertainty en_US
dc.title.alternative Unmanned Aerial Vehicles mission planning under uncertainty en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Operations Research Center. en_US
dc.identifier.oclc 77060839 en_US


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