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An FDES-Based Shared Control Method for Asynchronous Brain-Actuated Robot.

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    This study introduces a novel shared controller for asynchronous brain-computer interfaces (BCI) to enhance robot control. The fuzzy discrete event system (FDES) controller improves performance, robustness, and reduces workload for human-machine interaction.

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    Area of Science:

    • Robotics
    • Neuroscience
    • Control Systems

    Background:

    • Asynchronous brain-computer interfaces (BCI) enable natural human-machine interaction but struggle with complex robotic control.
    • Advanced control methods are needed to overcome limitations in speed and precision for robotic tasks.

    Purpose of the Study:

    • To propose a new shared controller using supervisory theory of fuzzy discrete event systems (FDES) for brain-actuated robot control.
    • To enhance the reliability, robustness, and reduce the workload of robotic control systems utilizing asynchronous BCI.

    Main Methods:

    • Development of a supervisory FDES-based shared controller.
    • Experimental validation through real-time direct manual control and BCI control tests with ten volunteers.
    • Online BCI experiments involving obstacle circumnavigation and target reaching tasks.

    Main Results:

    • The proposed FDES-based shared controller significantly improved robotic control performance and robustness compared to standard methods.
    • Eight out of ten participants successfully controlled the robot using a three-mental-state asynchronous BCI.
    • The shared control method demonstrated a reduction in user workload during robotic operation.

    Conclusions:

    • Asynchronous BCI combined with the FDES-based shared controller is a feasible solution for real-time and robust robotic control.
    • The supervisory FDES approach enhances system reliability by prioritizing more dependable control modes.
    • This integration offers a promising direction for advanced human-robot collaboration and control.