Brainwave-driven human-robot collaboration in construction

被引:79
|
作者
Liu, Yizhi [1 ]
Habibnezhad, Mahmoud [2 ]
Jebelli, Houtan [3 ]
机构
[1] Penn State Univ, Dept Architectural Engn, 201 Engn Unit B, University Pk, PA 16802 USA
[2] Penn State Univ, Dept Architectural Engn, 202 Engn Unit B, University Pk, PA 16802 USA
[3] Penn State Univ, Dept Architectural Engn, 224 Engn Unit A, University Pk, PA 16802 USA
关键词
Human-robot collaboration (HRC); Electroencephalogram (EEG); Construction robots; Workers’ cognitive load; Workplace safety and productivity; COGNITIVE-LOAD; WORKING-MEMORY; VIRTUAL-REALITY; NEURAL-NETWORKS; RECOGNITION; FRAMEWORK; SIGNALS; ELECTROENCEPHALOGRAM; PERFORMANCE; INTERFACES;
D O I
10.1016/j.autcon.2021.103556
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Due to the unstructured, fast-changing environment of construction sites, robots require human assistance to perform various tasks, especially those involving high dexterity and nuanced human judgment. However, in shared physical spaces, human-robot collaboration (HRC) can raise new safety concerns as workers' mental health can be adversely affected by poor communication between the two peers. To create a harmonized, safe HRC, this study proposes a worker-centered collaborative framework that enables robots to capture workers' brainwaves from wearable electroencephalograph, evaluate their task-related cognitive load, and adjust the robotic performance accordingly. The framework was examined by asking 14 subjects to execute a collaborative construction task with a terrestrial robot under various levels of cognitive loads. The results showed the robot could regulate its working pace with 81.91% accuracy. This level of communication can instill trust in HRC and facilitate future endeavors in safety design of collaborative robotics.
引用
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页数:14
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