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Updated: Feb 28, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Robust Image-Based Visual Servoing Formation Control for Quadrotors Without Communication via Reinforcement Learning.

Xinning Yi, Hao Liu, Haibin Duan

    IEEE Transactions on Neural Networks and Learning Systems
    |February 26, 2026
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    Summary
    This summary is machine-generated.

    This study uses reinforcement learning for robust quadrotor formation control in GPS-denied environments. The method enables stable leader-follower dynamics without communication, enhancing autonomous navigation.

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    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Control Systems

    Background:

    • Quadrotor formation control is challenging in GPS-denied and communication-degraded environments.
    • Existing methods often rely on inter-vehicle communication or precise models.

    Purpose of the Study:

    • To develop a robust image-based visual servoing (IBVS) formation control for quadrotors using reinforcement learning (RL).
    • To enable leader-follower formation control without relying on GNSS or inter-vehicle communication.

    Main Methods:

    • Utilized off-policy RL algorithms for robust visual servoing and attitude control.
    • Implemented a leader observer to estimate leader quadrotor states.
    • Employed virtual camera technique and sphere-based image moments for feature tracking.

    Main Results:

    • Achieved robust leader-follower formation control despite unknown uncertainties and external disturbances.
    • Demonstrated effective state estimation of the leader quadrotor without direct communication.
    • Validated the control scheme's effectiveness through theoretical analysis and simulations.

    Conclusions:

    • The proposed RL-based IBVS formation control is effective for quadrotors in challenging environments.
    • The method provides a robust solution for autonomous formation flying without GNSS or communication.
    • This research advances autonomous multi-robot systems in complex operational settings.