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Distributed Localization for Multi-Agent Systems With Random Noise Based on Iterative Learning.

Yunkai Lv, Hao Zhang, Zhuping Wang

    IEEE Transactions on Neural Networks and Learning Systems
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    Summary
    This summary is machine-generated.

    This study presents a robust distributed localization algorithm for dynamic multi-agent systems, effectively handling measurement and communication noise for accurate real-time positioning.

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    Area of Science:

    • Robotics
    • Control Systems
    • Distributed Systems

    Background:

    • Real-time localization is crucial for dynamic multi-agent systems.
    • Measurement and communication noises pose significant challenges.
    • Existing methods struggle with noise under directed communication topologies.

    Purpose of the Study:

    • To develop a robust distributed localization algorithm for dynamic multi-agent systems.
    • To address challenges posed by measurement and communication noises.
    • To ensure accurate real-time positioning under directed graphs.

    Main Methods:

    • Introduced barycentric coordinates for relative agent positioning.
    • Developed a robust distributed localization estimation algorithm using iterative learning.
    • Employed a relative-distance unbiased estimator to suppress measurement noise.
    • Utilized a stochastic approximation method with varying gains to inhibit communication noise.

    Main Results:

    • The proposed algorithm effectively suppresses measurement and communication noises.
    • Asymptotic convergence of the estimation method is mathematically derived.
    • Numerical simulations validated the algorithm's effectiveness.
    • Experiments with QBot-2e robots demonstrated practical applicability.

    Conclusions:

    • The novel iterative learning-based algorithm provides robust real-time localization for dynamic multi-agent systems.
    • The method is effective in mitigating both measurement and communication noise.
    • The approach is validated through simulations and real-world robotic experiments.