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Distributed Neural Adaptive Impedance Control for Cooperative Manipulation With Unknown Objects.

Danping Zeng, Yaonan Wang, Yiming Jiang

    IEEE Transactions on Neural Networks and Learning Systems
    |October 24, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces distributed neural adaptive impedance control (AIC) for multi-robot systems, enabling cooperative manipulation without prior knowledge of grasp or trajectory. The novel approach enhances robot coordination and adaptability in complex tasks.

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    Area of Science:

    • Robotics
    • Control Systems
    • Artificial Intelligence

    Background:

    • Cooperative manipulation in multi-robot systems often requires pre-defined grasp matrices and trajectories.
    • Existing methods lack adaptability to uncertainties and real-time state estimation.
    • Decentralized control is crucial for scalability and robustness in complex robotic tasks.

    Purpose of the Study:

    • To develop distributed neural adaptive impedance control (AIC) strategies for multi-manipulator systems.
    • To eliminate the need for prior knowledge of grasp matrices and desired trajectories.
    • To enhance cooperative manipulation capabilities by addressing system uncertainties and improving adaptability.

    Main Methods:

    • Design of two fully distributed finite-time observers for estimating reference point states without global information.
    • Utilization of kinematic constraints and state estimates to derive grasp matrix and end-effector (EE) trajectory.
    • Development of a distributed adaptive impedance model and neural network (NN)-based tracking control for adaptive trade-offs.
    • Integration of a virtual energy tank (EK) to ensure system passivity.

    Main Results:

    • Successfully estimated grasp matrices and desired EE trajectories using distributed observers.
    • Achieved adaptive trade-off between tracking performance and compliance through the distributed adaptive impedance model.
    • Demonstrated asymptotic realization of desired adaptive impedance dynamics under uncertainties via NN-based control.
    • Validated the proposed strategies through simulations of four mobile manipulators cooperatively transporting an unknown object.

    Conclusions:

    • The proposed distributed neural AIC effectively addresses limitations of existing cooperative manipulation methods.
    • The integration of distributed observers and NNs enables robust and adaptive control in uncertain environments.
    • The method enhances cooperative manipulation by allowing real-time estimation and adaptation without global information, ensuring system passivity.