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• W4311057589 abstract "In human-robot collaboration scenarios with shared workspaces, a highly desired performance boost is offset by high requirements for human safety, limiting speed and torque of the robot drives to levels which cannot harm the human body. Especially for complex tasks with flexible human behavior, it becomes vital to maintain safe working distances and coordinate tasks efficiently. An established approach in this regard is reactive servo in response to the current human pose. However, such an approach does not exploit expectations of the human's behavior and can therefore fail to react to fast human motions in time. To adapt the robot's behavior as soon as possible, predicting human intention early becomes a factor which is vital but hard to achieve. Here, we employ a recently developed type of brain-computer interface (BCI) which can detect the focus of the human's overt attention as a predictor for impending action. In contrast to other types of BCI, direct projection of stimuli onto the workspace facilitates a seamless integration in workflows. Moreover, we demonstrate how the signal-to-noise ratio of the brain response can be used to adjust the velocity of the robot movements to the vigilance or alertness level of the human. Analyzing this adaptive system with respect to performance and safety margins in a physical robot experiment, we found the proposed method could improve both collaboration efficiency and safety distance." @default.
• W4311057589 created "2022-12-23" @default.
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• W4311057589 date "2022-12-01" @default.
• W4311057589 modified "2023-10-17" @default.
• W4311057589 title "Coordinating human-robot collaboration by EEG-based human intention prediction and vigilance control" @default.
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• W4311057589 doi "https://doi.org/10.3389/fnbot.2022.1068274" @default.
• W4311057589 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36531919" @default.
• W4311057589 hasPublicationYear "2022" @default.
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