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Related Experiment Video

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Surgical Continuum Manipulator Control Using Multiagent Team Deep Q Learning.

Guanglin Ji, Qian Gao, Minyi Sun

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary
    This summary is machine-generated.

    A new Team Deep Q learning framework (TDQN) enhances control for surgical continuum manipulators. This robotic control method improves targeting accuracy, especially under disturbance, for complex endoscopic surgeries.

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    Area of Science:

    • Robotics
    • Surgical Technology
    • Artificial Intelligence

    Background:

    • Continuum manipulators offer flexibility for complex surgeries like neurosurgery and vascular surgery.
    • Accurate control is crucial for the success of robotic-assisted endoscopic procedures.

    Purpose of the Study:

    • To propose and evaluate a Team Deep Q learning framework (TDQN) for controlling a 2-Degrees of Freedom (DoF) surgical continuum manipulator.
    • To compare the performance of TDQN against Multiagent Deep Q Learning (MADQN).

    Main Methods:

    • Development of a TDQN framework where pairs of cables act as agents sharing state and reward information (centralized learning).
    • Verification of the TDQN framework on a 2-DoF cable-driven surgical continuum manipulator.
    • Quantitative assessment of targeting accuracy using root mean square error (RMSE) with and without disturbance.

    Main Results:

    • TDQN achieved significantly lower RMSE compared to MADQN.
    • RMSE for TDQN tracking was 0.82mm (with disturbance) and 0.16mm (without disturbance).
    • RMSE for MADQN tracking was 1.52mm (with disturbance) and 0.98mm (without disturbance).

    Conclusions:

    • The proposed TDQN demonstrates superior targeting accuracy over MADQN for surgical continuum manipulators.
    • TDQN shows significant potential for improving control accuracy and maneuverability in robotic-assisted endoscopic surgery, particularly under disturbed conditions.