Graduation Semester and Year
2017
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Electrical Engineering
Department
Electrical Engineering
First Advisor
Frank Lewis
Abstract
Autonomous 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.
Keywords
Autonomous systems, Robotic skin
Disciplines
Electrical and Computer Engineering | Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Margasahayam Subrahamanyam, Raghavendra Sriram, "Multi-source UAV-based object classification using CNN’s and Data acquisition system for Robotic Skin" (2017). Electrical Engineering Dissertations. 347.
https://mavmatrix.uta.edu/electricaleng_dissertations/347
Comments
Degree granted by The University of Texas at Arlington