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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01ks65hf830
Title: Autonomous Quadcopter Navigation of Trails Using Convolutional Neural Networks
Authors: Joshi, Shreyes
Advisors: Wentzlaff, David
Department: Electrical Engineering
Certificate Program: Applications of Computing Program
Class Year: 2017
Abstract: This 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.
URI: http://arks.princeton.edu/ark:/88435/dsp01ks65hf830
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Electrical Engineering, 1932-2020

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