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    This study introduces a novel autonomous exploration framework for mobile robots, featuring a field-of-view (FOV) expansion mechanism. The new method significantly reduces exploration time and improves trajectory continuity, outperforming current state-of-the-art approaches.

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    Area of Science:

    • Robotics
    • Artificial Intelligence

    Background:

    • Autonomous exploration is crucial for mobile robots but faces challenges with inefficient trajectories.
    • Existing methods often result in discontinuous paths, increasing exploration duration.

    Purpose of the Study:

    • To develop a rapid autonomous exploration framework utilizing a novel field-of-view (FOV) expansion mechanism.
    • To improve the efficiency and continuity of mobile robot exploration trajectories.

    Main Methods:

    • Implemented a 1-degree-of-freedom (DOF) FOV expansion mechanism.
    • Utilized a frontier-gravitation FOV direction planning method to decouple sensor and robot movement.
    • Employed principal component analysis (PCA) for rapid frontier viewpoint generation.
    • Introduced a sliding window travelling salesman problem (TSP) with an attenuation coefficient for global coverage path planning.

    Main Results:

    • Reduced exploration time by 30%-44% compared to state-of-the-art (SOTA) methods.
    • Enhanced the continuity of robot movement in both simulated and real-world tests.
    • Prioritized exploration of independent frontiers and minimized revisits.

    Conclusions:

    • The proposed FOV expansion mechanism and exploration framework significantly improve mobile robot exploration efficiency.
    • The approach offers a practical solution for reducing exploration time and enhancing path continuity in robotics.