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    This study introduces a novel algorithm to infer hidden drone objectives from sensor data, enhancing safety in transport and smart living. The policy error inverse reinforcement learning (PEIRL) method helps identify potentially malicious drone behavior.

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    Area of Science:

    • Robotics and Autonomous Systems
    • Artificial Intelligence
    • Control Theory

    Background:

    • Drones are increasingly integrated into critical sectors like transportation and smart living.
    • Distinguishing between benign and potentially malicious drone operations is essential for safety and security.
    • Existing methods may lack the capability to accurately infer hidden drone objectives from real-world data.

    Purpose of the Study:

    • To propose a novel algorithm for inferring the hidden objectives of drones using online trajectory data.
    • To enhance drone safety and security by accurately identifying their intended purpose.
    • To develop a method robust enough for integration with current flight controller hardware.

    Main Methods:

    • A policy error inverse reinforcement learning (PEIRL) algorithm is developed.
    • Error-based polynomial features are employed to approximate value and policy functions.
    • An integral inverse reinforcement learning (IRL) batch least-squares (LS) rule is used with an objective constraint for inference.
    • Lyapunov recursions are utilized to assess the convergence of the proposed method.

    Main Results:

    • The PEIRL algorithm successfully infers hidden drone objectives from cooperative sensor data.
    • The proposed feature set is compatible with onboard flight controller memory constraints.
    • Simulation studies with a quadcopter model validate the effectiveness of the approach.
    • The method demonstrates convergence, ensuring reliable objective inference.

    Conclusions:

    • The PEIRL algorithm offers a viable solution for identifying drone intentions, crucial for safety applications.
    • This research contributes to the secure integration of drones in civilian infrastructure.
    • The findings support the development of more sophisticated drone monitoring and threat assessment systems.