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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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An Intelligent Collaborative System for Robot Dynamics.

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    This study introduces an intelligent collaborative system for robotic navigation and control (CNaC) using neural networks for precise state reconstruction. The novel approach significantly improves navigation accuracy, even without landmarks.

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    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Control Systems

    Background:

    • Accurate robot localization and control are critical for autonomous systems.
    • Traditional methods often struggle with partial information or lack of landmarks.
    • Existing systems may rely on predetermined motion models prone to errors.

    Purpose of the Study:

    • To propose an intelligent collaborative system for robotic navigation and control (CNaC).
    • To develop a state reconstruction based on neural networks navigation (SR-NNN) law for precise robot positioning.
    • To enhance navigation performance and control accuracy, especially in challenging environments.

    Main Methods:

    • Designed a state reconstruction based on neural networks navigation (SR-NNN) law.
    • Utilized neural networks' local fitting ability and partial truth information for state estimation.
    • Employed Euler-Lagrange equations to govern the CNaC system.

    Main Results:

    • The SR-NNN achieved a maximum RMSE of 0.053, a 55% improvement over dead reckoning (DR) (max RMSE 0.096).
    • Demonstrated high-precision navigation performance even in the absence of landmarks.
    • Online training of the motion model by SR-NNN mitigated errors from predetermined models and external interference.

    Conclusions:

    • The intelligent CNaC system achieves satisfactory control performance using SR-NNN estimated positions.
    • The proposed SR-NNN provides a robust and accurate method for robot state estimation.
    • Effectiveness validated through simulations and real-world experiments.