TY - JOUR
T1 - Brain-computer interface for hands-free teleoperation of construction robots
AU - Liu, Yizhi
AU - Habibnezhad, Mahmoud
AU - Jebelli, Houtan
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2021/3
Y1 - 2021/3
N2 - Recently, the use of collaborative robots has started to emerge at construction sites. Such incorporation into these human-dominated environments can raise safety concerns as most robots are not fully automated and require some sort of control mechanism. Conventional control systems may fall short under specific situations in which workers require robotic assistance but cannot use their hands to control the robot. Brain-computer interface (BCI) can offer such hands-free controllability, a non-muscular communicative channel that can establish an interpretive pathway between humans and robots. This paper proposes a BCI-based system to remotely control a robot by continuously capturing workers' brainwaves acquired from a wearable electroencephalogram (EEG) device and interpreting them into robotic commands with 90% accuracy. The findings revealed the proposed system holds promise for enhancing robot control in hazardous operations where the ability of the worker to physically direct the robot is limited, such as underwater and space construction.
AB - Recently, the use of collaborative robots has started to emerge at construction sites. Such incorporation into these human-dominated environments can raise safety concerns as most robots are not fully automated and require some sort of control mechanism. Conventional control systems may fall short under specific situations in which workers require robotic assistance but cannot use their hands to control the robot. Brain-computer interface (BCI) can offer such hands-free controllability, a non-muscular communicative channel that can establish an interpretive pathway between humans and robots. This paper proposes a BCI-based system to remotely control a robot by continuously capturing workers' brainwaves acquired from a wearable electroencephalogram (EEG) device and interpreting them into robotic commands with 90% accuracy. The findings revealed the proposed system holds promise for enhancing robot control in hazardous operations where the ability of the worker to physically direct the robot is limited, such as underwater and space construction.
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U2 - 10.1016/j.autcon.2020.103523
DO - 10.1016/j.autcon.2020.103523
M3 - Article
AN - SCOPUS:85098866017
SN - 0926-5805
VL - 123
JO - Automation in Construction
JF - Automation in Construction
M1 - 103523
ER -