UAV mission planning under uncertainty

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006.

Bibliographic Details
Main Author: Sakamoto, Philemon
Other Authors: Cynthia Barnhart.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2007
Subjects:
Online Access:http://hdl.handle.net/1721.1/36230
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author Sakamoto, Philemon
author2 Cynthia Barnhart.
author_facet Cynthia Barnhart.
Sakamoto, Philemon
author_sort Sakamoto, Philemon
collection MIT
description Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006.
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spelling mit-1721.1/362302020-12-12T17:11:20Z UAV mission planning under uncertainty Unmanned Aerial Vehicles mission planning under uncertainty Sakamoto, Philemon Cynthia Barnhart. Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center. Operations Research Center. Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006. Includes bibliographical references (p. 205-209). 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. (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]. by Philemon Sakamoto. S.M. 2007-02-21T13:10:41Z 2007-02-21T13:10:41Z 2006 2006 Thesis http://hdl.handle.net/1721.1/36230 77060839 eng 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. http://dspace.mit.edu/handle/1721.1/7582 209 p. application/pdf Massachusetts Institute of Technology
spellingShingle Operations Research Center.
Sakamoto, Philemon
UAV mission planning under uncertainty
title UAV mission planning under uncertainty
title_full UAV mission planning under uncertainty
title_fullStr UAV mission planning under uncertainty
title_full_unstemmed UAV mission planning under uncertainty
title_short UAV mission planning under uncertainty
title_sort uav mission planning under uncertainty
topic Operations Research Center.
url http://hdl.handle.net/1721.1/36230
work_keys_str_mv AT sakamotophilemon uavmissionplanningunderuncertainty
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