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Jonathan P. How. Lee, Kenneth (Kenneth King Ho) Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics 2011-04-25T14:17:25Z 2011-04-25T14:17:25Z 2010 2010 http://hdl.handle.net/1721.1/62323 712589745 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (p. 157-164). Unmanned aerial vehicles are used extensively in persistent surveillance, search and track, border patrol, and environment monitoring applications. Each of these applications requires the obtainment of information using a dynamic observer equipped with a constrained sensor. Information can only be gained when visibility exists between the sensor and a number of targets in a cluttered environment. Maximizing visibility is therefore essential for acquiring as much information about targets as possible, to subsequently enable informed decision making. Proposed is an algorithm that can design a maximum visibility path given models of the vehicle, target, sensor, environment, and visibility. An approximate visibility, finite-horizon dynamic programming approach is used to find flyable, maximum visibility paths. This algorithm is compared against a state-of-the-art optimal control solver for validation. Complex scenarios involving multiple stationary or moving targets are considered, leading to loiter patterns or pursuit paths which negotiate planar, three-dimensional, or elevation environment models. Robustness to disturbances is addressed by treating targets as regions instead of points, to improve visibility performance in the presence of uncertainty. A testbed implementation validates the algorithm in a hardware setting with a quadrotor observer, multiple moving ground vehicle targets, and an urban-like setting providing occlusions to visibility. by Kenneth Lee. S.M. 164 p. eng Massachusetts Institute of Technology 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 Aeronautics and Astronautics. Visibility maximization with unmanned aerial vehicles in complex environments Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="685a62df-7ef1-48f1-8ccf-a54c3ece296f"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Visibility maximization with unmanned aerial vehicles in complex environments</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2010</PublicationDate> <Authors> <Author> <DisplayName>Lee, Kenneth (Kenneth King Ho)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Aeronautics and Astronautics.</Keyword> <Abstract>Unmanned aerial vehicles are used extensively in persistent surveillance, search and track, border patrol, and environment monitoring applications. Each of these applications requires the obtainment of information using a dynamic observer equipped with a constrained sensor. Information can only be gained when visibility exists between the sensor and a number of targets in a cluttered environment. Maximizing visibility is therefore essential for acquiring as much information about targets as possible, to subsequently enable informed decision making. Proposed is an algorithm that can design a maximum visibility path given models of the vehicle, target, sensor, environment, and visibility. An approximate visibility, finite-horizon dynamic programming approach is used to find flyable, maximum visibility paths. This algorithm is compared against a state-of-the-art optimal control solver for validation. Complex scenarios involving multiple stationary or moving targets are considered, leading to loiter patterns or pursuit paths which negotiate planar, three-dimensional, or elevation environment models. Robustness to disturbances is addressed by treating targets as regions instead of points, to improve visibility performance in the presence of uncertainty. A testbed implementation validates the algorithm in a hardware setting with a quadrotor observer, multiple moving ground vehicle targets, and an urban-like setting providing occlusions to visibility.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>