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A Physics-Informed Neural Network Approach to Augmented Dynamics Visual Servoing of Multirotors.

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    Area of Science:

    • Robotics
    • Control Systems
    • Machine Learning

    Background:

    • Visual servoing enables robots to use camera feedback for precise motion control.
    • Multirotor dynamics present challenges due to complex control inputs and potential uncertainties.
    • Physics-informed neural networks (PINNs) offer a powerful tool for modeling complex systems with limited data.

    Purpose of the Study:

    • To develop a robust visual servoing strategy for multirotors by integrating PINNs with dynamics-centered control.
    • To eliminate the need for inverse Jacobian calculations in multirotor motion control.
    • To enhance the robustness of visual servoing against uncertainties in camera and multirotor parameters.

    Main Methods:

    • A physics-informed neural network (PINN) is employed to estimate system uncertainties and inaccuracies.
    • The PINN model is integrated with a dynamics-centered visual servoing technique, directly mapping pixel variations to torque and thrust inputs.
    • A nonlinear model predictive controller (NMPC) with an adaptive horizon is utilized for real-time implementation.

    Main Results:

    • The proposed method reduces the need for labeled data by 65% compared to existing data-driven approaches.
    • The integrated system demonstrates robustness against up to 70% uncertainty in camera parameters.
    • The NMPC enables control effort processing 10 times faster than conventional MPC strategies.

    Conclusions:

    • The combined PINN and dynamics-centered visual servoing strategy offers a robust and data-efficient solution for multirotor control.
    • This approach effectively handles system uncertainties and modeling inaccuracies, crucial for real-world applications.
    • The real-time capabilities of the NMPC ensure practical implementation for dynamic trajectory tracking.