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MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
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Online and Robust Intermittent Motion Planning in Dynamic and Changing Environments.

Zirui Xu, George P Kontoudis, Kyriakos G Vamvoudakis

    IEEE Transactions on Neural Networks and Learning Systems
    |August 28, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces RRT-, a motion planning framework for robots in dynamic environments. It ensures stability and optimal control despite unknown robot dynamics and disturbances.

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    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Control Theory

    Background:

    • Dynamic environments pose challenges for robot motion planning.
    • Unknown robot dynamics and external disturbances complicate control.
    • Online and intermittent planning is crucial for adaptability.

    Purpose of the Study:

    • To propose RRT-, an online and intermittent kinodynamic motion planning framework.
    • To address challenges of unknown robot dynamics and disturbances.
    • To ensure closed-loop stability in dynamic environments.

    Main Methods:

    • Leveraging Rapidly-exploring Random Trees (RRT) for global path planning and replanning.
    • Formulating waypoint generation as a sequence of boundary-value problems (BVPs).
    • Employing a robust intermittent Q-learning controller with a relaxed persistence of excitation technique.

    Main Results:

    • The proposed framework effectively plans motion in dynamic environments.
    • The Q-learning controller converges to optimal control despite unknown dynamics.
    • Lyapunov-based proofs confirm closed-loop stability.

    Conclusions:

    • RRT- provides a robust solution for kinodynamic motion planning in uncertain dynamic environments.
    • The integration of RRT and Q-learning offers adaptive and stable robot control.
    • Numerical experiments validate the framework's effectiveness.