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Game-Based Approximate Optimal Motion Planning for Safe Human-Swarm Interaction.

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    IEEE Transactions on Cybernetics
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    This study introduces a novel game theory approach for safe human-swarm interaction, ensuring robots autonomously adjust to unsafe commands and obstacles for robust cooperation.

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    Area of Science:

    • Robotics
    • Control Theory
    • Game Theory

    Background:

    • Human-swarm interaction safety is crucial and challenging.
    • Existing methods require high real-time computation for constrained optimization.
    • Need for robust and autonomous safety solutions in human-robot systems.

    Purpose of the Study:

    • To develop a safe and robust framework for human-swarm interaction.
    • To address real-time limitations of current safety approaches.
    • To enable autonomous trajectory modification for unsafe human commands.

    Main Methods:

    • Formulating the problem as a Stackelberg-Nash game over the entire time domain.
    • Designing best-response controllers (Nash equilibrium) for follower robots.
    • Developing a Lyapunov-like control barrier function for leader safety and a learning-based controller for formation tracking.

    Main Results:

    • Robotic swarms successfully maintain desired formations while tracking human commands.
    • Controllers autonomously modify trajectories to ensure safety against unsafe commands.
    • Safety is maintained even with dynamic obstacles present, verified by simulations and experiments.

    Conclusions:

    • The proposed Stackelberg-Nash game approach effectively ensures safety in human-swarm interaction.
    • The method overcomes real-time constraints by optimizing over the entire time domain.
    • The system demonstrates robust cooperative behavior and autonomous safety adjustments.