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dc.contributor.advisorLewis, Frank L
dc.creatorMargasahayam Subrahamanyam, Raghavendra Margasahayam
dc.date.accessioned2017-10-02T14:45:22Z
dc.date.available2017-10-02T14:45:22Z
dc.date.created2017-08
dc.date.issued2017-08-16
dc.date.submittedAugust 2017
dc.identifier.urihttp://hdl.handle.net/10106/26987
dc.description.abstractAutonomous robots are intelligent systems capable of performing tasks in the world by themselves, without explicit human control. Examples range from autonomous helicopters to Roomba, the robot vacuum cleaner to robotic manipulators. The numerous sensors onboard gather data related to the desired actions and this poses several challenges and design constraints in terms of computation and hardware design that can prove to be extremely difficult and expensive to avoid. The objective of this research is to study, develop and implement various Intelligent solutions to help solve several real-world problems with respect to multi agent configurations of unmanned systems. We shall see examples of integration of machine learning especially Deep learning capabilities in swarms. We shall also discuss the development of robotic skin modules and the several constraints and design schemes adopted and tested to support the development of more social robots and help study and determine certain requirements and develop smart systems to help give robots a more natural sensory based interface with its surroundings.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.subjectAutonomous Systems
dc.subjectRobotic Skin
dc.titleMulti-source UAV based object classification using CNN’s and Data acquisition system for Robotic Skin
dc.typeThesis
dc.date.updated2017-10-02T14:47:30Z
thesis.degree.departmentElectrical Engineering
thesis.degree.grantorThe University of Texas at Arlington
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Electrical Engineering
dc.type.materialtext
dc.creator.orcid0000-0002-9494-017X


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