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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01ks65hf830
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dc.contributor.advisorWentzlaff, David-
dc.contributor.authorJoshi, Shreyes-
dc.date.accessioned2017-07-24T13:10:04Z-
dc.date.available2017-07-24T13:10:04Z-
dc.date.created2017-05-07-
dc.date.issued2017-5-7-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01ks65hf830-
dc.description.abstractThis research focuses on developing an artificially intelligent quadcopter that implements computer vision through Deep Neural Networks to autonomously navigate trail paths for search and rescue. The challenge of search and rescue along trails has normally been a completely manual process, but following the recent growth in UAV technologies and big data processing, the potential of solving this problem autonomously has become feasible. Currently, a variety of startups and research groups have begun working towards developing similar technologies for different applications, but very few have actually demonstrated successful prototypes. Thus, there is great value in pursuing research and experimentation in this direction for both the safety of hikers around the world and the advancement of UAV technologies through machine learning.en_US
dc.language.isoen_USen_US
dc.titleAutonomous Quadcopter Navigation of Trails Using Convolutional Neural Networksen_US
dc.typePrinceton University Senior Theses-
pu.date.classyear2017en_US
pu.departmentElectrical Engineeringen_US
pu.pdf.coverpageSeniorThesisCoverPage-
pu.contributor.authorid960827737-
pu.contributor.advisorid960753095-
pu.certificateApplications of Computing Programen_US
Appears in Collections:Electrical Engineering, 1932-2020

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