Date on Master's Thesis/Doctoral Dissertation

8-2024

Document Type

Master's Thesis

Degree Name

M.S.

Department

Electrical and Computer Engineering

Degree Program

Electrical Engineering, MS

Committee Chair

Popa, Dan

Committee Co-Chair (if applicable)

Inanc, Tamer

Committee Member

Inanc, Tamer

Committee Member

McIntyre, Michael

Author's Keywords

Human-robot interaction; brain-computer interface; assistive robot; embedded system; obstacle detection; haptic interface

Abstract

The increased presence and deployment of robotics in sectors such as the medical field results in the demand for robots to, directly and indirectly, interface with people and their environment, making human-robot interaction (HRI) a vital thrust of robotics research. Assistive robots, for example, aid humans in accomplishing tasks or by providing support in the workforce. As the demand for nurses and the aging population increases, the assistive robots deployed will be deeply rooted in environments that require constant interaction with humans. This work contributed to improving aspects of HRI through 1) Expanding accessibility of the methods used for interfacing with a robot; 2) Enhancing the environmental awareness for navigation and obstacles; 3) Implementation of new interfaces to increase data availability from the user for human-intent interpretation. This thesis makes contributions to HRI through three distinct projects related to the Adaptive Robot Nursing Assistant (ARNA), a custom assistive robot by the Louisville Automation & Robotics Research Institute. The first is the design and experimentation of a Brain-Computer Interface (BCI) as a novel interface, conducting experimentation to study one-hand vs. two-hand training methods. The second project aimed to increase a walker robot’s navigational capabilities through the design and construction of an improved Instrumentation Board and navigational Sensor Suite to address navigational flaws presented by the slow data rate of the prior system. This project consisted of the design, construction, and integration of the new system into the ARNA robot and performed experimentation to assess the performance of the sonars in a static and dynamic state. In the third project, a sensorized handlebar was implemented using haptic sensors to provide data from user grip forces, providing more data available to human-intent interpretation algorithms. Our work took a newly produced handlebar grip design and integrated a system of Force Sensitive Resistors (FSRs) to integrate into a robotic handlebar.

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