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Emilio Frazzoli. Mueller, Erich, Ph. D. Massachusetts Institute of Technology Massachusetts Institute of Technology. Department of Aeronautics and Astronautics. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics 2016-12-05T19:54:26Z 2016-12-05T19:54:26Z 2015 2016 http://hdl.handle.net/1721.1/105601 962368122 Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, February 2016. Cataloged from PDF version of thesis. "February 2015." Includes bibliographical references (pages 162-173). The recent development of neuromorphic vision sensors, which provide an asynchronous, high-speed alternative to conventional cameras has lead to a considerable amount of research into their applicability to robotic control systems. However, algorithms for onboard control of mobile robotic platforms such as automobiles or aircraft using these sensors are lacking and in fact almost all existing implementations keep the sensor stationary. This research has several objectives. First, to develop a rigorous understanding of how to use asynchronous temporal contrast vision sensors for heading regulation and tracking in such a way as to fully leverage the remarkable properties of these sensors including high bandwidth, low latency and low power consumption. Second, to provide a theoretical and experimental comparison between neuromorphic vision sensors and conventional cameras in the context of this problem. Finally, to describe and test algorithms for high-speed motion planning in cluttered environments using neuromorphic vision sensors. by Erich Mueller. Ph.D. 173 pages 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. Feedback control of dynamical systems using neuromorphic vision sensors Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="ae436ce7-0385-41c6-99b9-0535662f9662"> <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>Feedback control of dynamical systems using neuromorphic vision sensors</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2016</PublicationDate> <Authors> <Author> <DisplayName>Mueller, Erich, Ph. D. Massachusetts Institute of Technology</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>The recent development of neuromorphic vision sensors, which provide an asynchronous, high-speed alternative to conventional cameras has lead to a considerable amount of research into their applicability to robotic control systems. However, algorithms for onboard control of mobile robotic platforms such as automobiles or aircraft using these sensors are lacking and in fact almost all existing implementations keep the sensor stationary. This research has several objectives. First, to develop a rigorous understanding of how to use asynchronous temporal contrast vision sensors for heading regulation and tracking in such a way as to fully leverage the remarkable properties of these sensors including high bandwidth, low latency and low power consumption. Second, to provide a theoretical and experimental comparison between neuromorphic vision sensors and conventional cameras in the context of this problem. Finally, to describe and test algorithms for high-speed motion planning in cluttered environments using neuromorphic vision sensors.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>