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Title:EEG-based brain-computer interface for human-robot collaboration
Author(s):Li, Yao
Director of Research:Kesavadas, Thenkurussi
Doctoral Committee Chair(s):Kesavadas, Thenkurussi
Doctoral Committee Member(s):Reis, Henrique M; Sreenivas, Ramavarapu S; Chowdhary, Girish
Department / Program:Industrial&Enterprise Sys Eng
Discipline:Systems & Entrepreneurial Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):BCI
EEG
SSVEP
Industrial robot
Abstract:One of the expectations for the next-generation industrial robots is to work collaboratively with humans. Collaborative robots must be able to communicate with human collaborators intelligently and seamlessly. However, industrial robots in prevalence are not good at understanding human intentions and decisions. We propose to develop human-robot interactions based on Brain-Computer Interfaces (BCIs) transferring human cognition to robots directly. By collecting and encoding brain activities with BCIs, human can actively send commands to robots in thought or passively let robots monitor mental activities. We conduct two major experiments, i.e. BCI for welding robot and BCI for defective part picking robot, through which human operators can actively communicate with robots and work collaboratively on manufacturing tasks. The BCI for welding robot allows operators to select weld beads and command the robot to weld in thought. In the picking robot study, the robot picks defective part from a conveyor based on the decisions made when operators examining the qualities visually. Besides, to build faster and more accurate BCIs, we propose a Conv-CA model, which combines convolutional neural network (CNN) and canonical correlation analysis (CCA) to improve the performance of the state-of-art steady-state visually evoked potential (SSVEP) algorithm. We also conduct a study for passive BCI communication, i.e. the robot detects the circumstance when operators feel unsafe in the human-robot collaboration. When a fear response is detected, the robot can stop immediately to protect human safety.
Issue Date:2020-11-19
Type:Thesis
URI:http://hdl.handle.net/2142/109363
Rights Information:Copyright 2020 Yao Li
Date Available in IDEALS:2021-03-05
Date Deposited:2020-12


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