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    This study introduces a human-machine shared control method for magnetic microrobots, enhancing navigation in complex environments. The system integrates human intelligence and machine autonomy for safer, more efficient microrobot control.

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    Area of Science:

    • Robotics
    • Control Systems
    • Biomedical Engineering

    Background:

    • Precise control of magnetic microrobots is crucial for applications like targeted therapy but challenging in dynamic environments.
    • Manual teleoperation and fully autonomous systems have limitations in robustness and adaptability.

    Purpose of the Study:

    • To propose a human-machine shared cascade control method for magnetically driven microrobots.
    • To achieve collision-free navigation in dynamic environments by integrating human intelligence and machine autonomy.

    Main Methods:

    • An outer-loop hybrid shared control unit modulates control authority based on collision risk.
    • An inner-loop adaptive orientation controller uses Gaussian Process Regression (GPR) and Virtual Reference Feedback Tuning (VRFT).

    Main Results:

    • Human-subject studies showed the shared control strategy significantly outperformed manual and autonomous modes in success rate, completion time, stability, and safety (p < 0.001).
    • Successful navigation in a complex gastric model demonstrated practical applicability in unstructured environments.

    Conclusions:

    • The proposed human-machine shared control method offers a robust and adaptive solution for magnetic microrobot navigation.
    • This approach enhances microrobot performance and safety, paving the way for advanced therapeutic and micromanipulation applications.