Indoor Navigation for Assistive Robots using EEG Signals as Feedback

Abstract

Assistive robots are used by various individuals with medical disabilities to help with tasks such as movement. A subset of these individuals are patients with the locked- in syndrome; these patients cannot communicate with a robot through traditional means, such as with a joystick. This work designs a navigation scheme which allows for an assistive robot to be controlled by patients suffering locked-in syndrome, thus allowing the patient to move about their environment. Navigation is accomplished using an algorithm that combines autonomous robot movement and communicated commands from the patient. To bridge the communication gap between the patient and robot, naturally occurring error-related potentials are used. These ERPs can be used to establish communication between the patient and robot without relying on the patient interacting with physical stimuli, such as a keyboard or joystick. The commands commu- nicated to the robot comes in the form of a binary: correct or incorrect command in response to the movements of the robot at an intersection in a structured building. While more complicated commands can be classified from event-realted potentials (ERPs), such as directional movement, this simple command allows for fast reliable classifications and responses. To make up for the lack of complexity from patient commands, the robot is leveraged to handle tasks such as wall avoidance, while a navigation algorithm is designed to minimize the inputs required by the user when taking a commonly traveled path. The benefits of using a semi- controlled robot for navigation vs a fully autonomous robot is compared in terms of the time taken to discover and navigate an initial path to a destination. This work serves as a proof of concept for the proposed semi-autonomous navigation scheme to validate future work into the proposed design.

Publication
SoutheastCon 2019
Sunny Arokia Swamy Bellary
Sunny Arokia Swamy Bellary
Engineer/Scientist II

EPRI Engineer | AI Enthusiast | Computer Vision Researcher | Robotics Tech Savvy | Food Lover | Wanderlust | Team Leader @Belaku | Musician |

James M. Conrad
James M. Conrad
Professor, Electrical and Computer Engineering

James M. Conrad received his bachelor’s degree in computer science from the University of Illinois, Urbana, and his master’s and doctorate degrees in computer engineering from North Carolina State University. He is currently a professor at the University of North Carolina at Charlotte. He has served as an assistant professor at the University of Arkansas and as an instructor at North Carolina State University. He has also worked at IBM, Ericsson/Sony Ericsson, and BPM Technology. Dr. Conrad is a Professional Engineer, a Senior Member of the IEEE and a Certified Project Management Professional (PMP). He is also a member of Eta Kappa Nu and the Project Management Institute. He served on the IEEE Board of Directors as Region 3 director for 2016-2017, and again as a director in 2020 when he also served as IEEE-USA President. He is the author of numerous books, book chapters, journal articles, and conference papers in the areas of embedded systems, robotics, parallel processing, and engineering education.

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