SENSOR FUSION FRAMEWORK AND SIMULATION ON A TURTLEBOT ROBOTIC VEHICLE

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Gangadhar, S. (2017). SENSOR FUSION FRAMEWORK AND SIMULATION ON A TURTLEBOT ROBOTIC VEHICLE. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Autonomous robots are being designed and used in a wide variety of environments and unforeseen ways. Intelligent interpretation of sensor data is the key factor in a robot’s decision making ability while operating in such environments. Current day robots are also expected to adapt, have human interaction and accomplish many more tasks, than ever before. This work focuses on developing a sensor fusion framework and achieving human robot interaction for a robot navigating an unknown terrain.The selective sensor fusion framework proposed in this work, combines heterogeneous data from sensors to analyze the parameters of the environment required to accomplish a task. It is scalable, modular and accounts for human interaction. The framework uses fusion algorithms and develops confidence at various stages before arriving at a decision. The framework is simulated and tested on a robotic vehicle called the TurtleBot along with additional LIDAR (Light detection and ranging) sensors on a simulated, unknown terrain. The vehicle navigates successfully through the terrain, avoiding obstacles and terrain irregularities while querying the user for assistance when it enters an uncertain mode. The framework is designed to accommodate higher levels of decision making before deciding on an action. Furthermore, the framework can be customized based on the application.

Details
Author

Gangadhar, Shruti

Title

SENSOR FUSION FRAMEWORK AND SIMULATION ON A TURTLEBOT ROBOTIC VEHICLE

Physical Description

1 online resource (96 pages) : PDF

Date

2017

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Autonomous robots are being designed and used in a wide variety of environments and unforeseen ways. Intelligent interpretation of sensor data is the key factor in a robot’s decision making ability while operating in such environments. Current day robots are also expected to adapt, have human interaction and accomplish many more tasks, than ever before. This work focuses on developing a sensor fusion framework and achieving human robot interaction for a robot navigating an unknown terrain.The selective sensor fusion framework proposed in this work, combines heterogeneous data from sensors to analyze the parameters of the environment required to accomplish a task. It is scalable, modular and accounts for human interaction. The framework uses fusion algorithms and develops confidence at various stages before arriving at a decision. The framework is simulated and tested on a robotic vehicle called the TurtleBot along with additional LIDAR (Light detection and ranging) sensors on a simulated, unknown terrain. The vehicle navigates successfully through the terrain, avoiding obstacles and terrain irregularities while querying the user for assistance when it enters an uncertain mode. The framework is designed to accommodate higher levels of decision making before deciding on an action. Furthermore, the framework can be customized based on the application.

Genre

masters theses

Subjects--Topics

Electrical engineering
Computer engineering
Robotics

Degree

M.S.

Keywords

Autonomous
HRI
Robotic Navigation
Robot Operating System
Sensor Fusion Framework
Track-To-Track Fusion

Subject Area

Electrical Engineering

Advisor(s)

Conrad, James

Committee Members

Ebong, Abasifreke
Sass, Ronald

Degree Note

Thesis (M.S.)--University of North Carolina at Charlotte, 2017.

Rights Statement

This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). For additional information, see http://rightsstatements.org/page/InC/1.0/.

Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Gangadhar_uncc_0694N_11406

Permalink

http://hdl.handle.net/20.500.13093/etd:19