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Enhancing Collision-Free Formation Control in Multiagent Systems: An Approach Based on Time-Derivative of Artificial

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    This study introduces time-derivative of artificial potential functions (APFs) to improve collision-free formation control in multiagent systems (MASs). The method reduces oscillations and acceleration surges for smoother, more stable formations.

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    Area of Science:

    • Robotics
    • Control Systems
    • Artificial Intelligence

    Background:

    • Artificial Potential Function (APF) is a common algorithm for collision-free formation control in multiagent systems (MASs).
    • Existing APF methods often exhibit oscillations and acceleration surges, especially during formation conflicts.

    Purpose of the Study:

    • To enhance collision-free formation control in MAS by mitigating oscillations and acceleration surges.
    • To introduce a novel approach using the time-derivative of APFs for improved stability and performance.

    Main Methods:

    • Introduced the time-derivative of APFs, unifying attractive and repulsive potentials.
    • Utilized APF gradients to transform potential and kinetic energy.
    • Incorporated time-derivative of APF gradients as damping terms to dissipate energy.
    • Presented a time-variant formation tracking scheme and a collision-free control algorithm.

    Main Results:

    • The proposed method effectively mitigates oscillations and acceleration surges.
    • Demonstrated Lyapunov stability and collision avoidance capabilities.
    • Analyzed maneuverability from a geometric perspective.

    Conclusions:

    • The time-derivative of APFs offers a robust solution for collision-free formation control in MAS.
    • The approach enhances system stability and performance by addressing inherent APF limitations.
    • This method provides a unified framework encompassing existing algorithms.