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Related Experiment Video

Updated: Sep 11, 2025

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A Data-Driven Distributed Recurrent Neural Network for a Collaborative System of Multiple Redundant Manipulators With

Mingyang Zhang, Zhijun Zhang

    IEEE Transactions on Cybernetics
    |August 14, 2025
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    Summary

    This study introduces a novel neural network for precise collaborative motion in robotic arms, even with unknown parameters. The method enhances accuracy and applicability in multi-manipulator systems.

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    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Control Systems

    Background:

    • Precise collaborative motion generation in multi-manipulator systems (MMCs) is challenging, especially with unknown system parameters.
    • Traditional methods often require accurate models or rely on limited Jacobian estimation techniques.
    • Existing data-driven methods struggle with the dynamic, time-varying nature of robotic systems.

    Purpose of the Study:

    • To propose a novel data-driven distributed recurrent neural network (DDD-RNN) for precise collaborative motion generation in MMCs.
    • To address the limitations of existing methods by enabling online estimation of both first- and second-order Jacobian matrices.
    • To develop a neurodynamics-based recurrent neural network solver that accounts for time-varying manipulator characteristics.

    Main Methods:

    • Design of a novel data-driven distributed recurrent neural network (DDD-RNN) incorporating neurodynamics principles.
    • Development of an improved Jacobian matrix estimation law (IJM) for simultaneous online estimation of first- and second-order Jacobian matrices.
    • Implementation of a recurrent neural network solver utilizing neurodynamics criteria to process time-varying information.

    Main Results:

    • The DDD-RNN method successfully generated collaborative motions in simulations and experiments on Ufactory XArm6 robots.
    • The approach demonstrated feasibility even when the structural parameters of the robotic arms were unknown.
    • Online estimation of both first- and second-order Jacobian matrices effectively captured the time-varying dynamics of the manipulators.

    Conclusions:

    • The proposed DDD-RNN method offers a superior approach for precise collaborative motion generation in MMCs compared to traditional and existing data-driven techniques.
    • The method enhances end-effector accuracy and applicability, particularly in systems with unknown dynamics.
    • The DDD-RNN provides a robust solution for real-world robotic applications requiring accurate multi-arm coordination.