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    Area of Science:

    • Robotics
    • Human-Robot Interaction
    • Control Theory

    Background:

    • Robots can assist humans in unstructured tasks using force control, particularly collaborative robots (cobots) and exoskeletons.
    • Direct force feedback can cause instability due to limited control bandwidth and coupled human-robot dynamics.
    • Existing controllers balance strength amplification with stability, often compromising system transparency.

    Purpose of the Study:

    • To extend a virtual-mass-based controller to a strength-amplifying robot arm.
    • To theoretically and experimentally demonstrate improved stability with the virtual mass term.
    • To identify robot end-effector compliance and self-deflection transfer functions for better understanding human mechanical impedance.

    Main Methods:

    • Extension of a virtual-mass-based exoskeleton controller to a robot arm.
    • Theoretical analysis and experimental validation of stability properties.
    • Identification of robot end-effector transfer functions and measurement of spring-box apparatus stiffness.

    Main Results:

    • The virtual mass term significantly improves stability properties in strength-amplifying robot arms.
    • The study successfully identified the robot's end-effector compliance and self-deflection transfer functions.
    • The robot demonstrated reliable measurement of spring-box apparatus stiffnesses.

    Conclusions:

    • Virtual-mass-based control enhances stability for human-robot interaction, enabling safer and more effective strength amplification.
    • Understanding robot end-effector dynamics is key to improving impedance identification for human-robot systems.
    • This research contributes to developing more transparent and stable collaborative robots.