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Kinodynamic Motion Planning With Continuous-Time Q-Learning: An Online, Model-Free, and Safe Navigation Framework.

George P Kontoudis, Kyriakos G Vamvoudakis

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    This study introduces RRT-Q*, an online motion planning framework combining Rapidly-exploring Random Trees (RRT*) and Q-learning for efficient, collision-free robot navigation. It ensures stability and path optimality in dynamic environments.

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    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Control Theory

    Background:

    • Online kinodynamic motion planning is crucial for autonomous systems.
    • Existing methods often struggle with real-time adaptation and optimality.
    • Integrating learning-based approaches with sampling-based planners offers potential improvements.

    Purpose of the Study:

    • To present a novel online kinodynamic motion planning framework, RRT-Q*, that integrates asymptotically optimal Rapidly-exploring Random Tree (RRT*) with continuous-time Q-learning.
    • To develop a model-free Q-based advantage function and utilize integral reinforcement learning for online approximation of optimal cost and policy in continuous-time linear systems.
    • To ensure stability, asymptotic convergence, and collision-free navigation through Lyapunov-based proofs and novel obstacle handling techniques.

    Main Methods:

    • Formulation of a model-free Q-based advantage function.
    • Application of integral reinforcement learning for tuning laws in online policy approximation.
    • Development of a terminal state evaluation procedure for online implementation.
    • Introduction of static obstacle augmentation and a local replanning framework based on topological connectedness.

    Main Results:

    • Rigorous Lyapunov-based proofs demonstrating stability and asymptotic convergence properties.
    • Successful online approximation of optimal cost and policy for continuous-time linear systems.
    • Demonstrated capability for collision-free navigation through local replanning and obstacle augmentation.
    • Validation of the RRT-Q* framework's efficacy via simulations and qualitative comparisons.

    Conclusions:

    • The RRT-Q* framework provides an effective online solution for kinodynamic motion planning.
    • The integration of RRT* and continuous-time Q-learning ensures asymptotic optimality and stability.
    • The proposed methods enable robust, collision-free navigation in complex environments.