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    Area of Science:

    • Robotics
    • Control Systems
    • Computer Vision

    Background:

    • Visual servoing enables robots to use camera feedback for precise movement.
    • Mobile robot control often faces challenges with unknown dynamics and real-time adjustments.
    • Hierarchical control strategies offer improved performance by separating kinematic and dynamic tasks.

    Purpose of the Study:

    • To design a hierarchical predictive control (PC) algorithm for visual servoing mobile robots.
    • To address challenges in controlling mobile robots with unknown dynamics using data-driven methods.
    • To ensure system stability and quantify prediction errors in the control loop.

    Main Methods:

    • Established an image-based visual servoing model for wheeled mobile robots at the kinematic level.
    • Employed an iterative linear quadratic regulator (iLQR) to derive a velocity controller.
    • Developed a data-driven PC controller using Gaussian processes (GP) for torque control under unknown dynamics.

    Main Results:

    • Successfully obtained a velocity controller using iLQR and a torque controller using GP-based PC.
    • Proved the input-to-state practical stability (ISpS) of the system with the proposed data-driven PC method.
    • Analyzed the maximum upper bound of Gaussian process inference error.

    Conclusions:

    • The proposed hierarchical predictive control algorithm effectively manages visual servoing for mobile robots.
    • The data-driven approach enhances control robustness, particularly with unknown system dynamics.
    • Simulations and experiments validate the practical applicability and effectiveness of the hierarchical controller.