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    Area of Science:

    • Robotics
    • Control Systems
    • Artificial Intelligence

    Background:

    • Autonomous aerial systems require robust formation control strategies.
    • Inter-vehicle communication and relative position sensing are often limitations in multi-quadrotor systems.
    • Existing methods may struggle with input constraints and model uncertainties.

    Purpose of the Study:

    • To propose an input-constrained visual servoing formation controller for multiple quadrotor systems.
    • To enable communication-free aerial formation control using only visual information.
    • To address visibility and attitude constraints using reinforcement learning.

    Main Methods:

    • Formulating leader-follower dynamics using a virtual camera and sphere-based image moments.
    • Developing an adaptive velocity observer for relative velocity estimation without communication.
    • Employing an off-policy reinforcement learning (RL) algorithm for input-constrained control.

    Main Results:

    • Successfully demonstrated aerial formation control without inter-vehicle communication.
    • The adaptive velocity observer effectively estimated relative velocities in communication-free scenarios.
    • The RL-based controller handled visibility and attitude constraints without precise model knowledge.

    Conclusions:

    • The proposed input-constrained visual servoing controller is effective for multi-quadrotor formation.
    • The system achieves stable and autonomous aerial coordination using visual feedback and RL.
    • This approach offers a viable solution for decentralized multi-robot systems.