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    Area of Science:

    • Human-Computer Interaction
    • Virtual Reality
    • Robotics

    Background:

    • Redirected walking (RDW) enables virtual environment exploration in limited physical spaces.
    • Current RDW reset methods often ignore spatial features and user intent, leading to frequent boundary collisions.

    Purpose of the Study:

    • To develop an innovative predictive reset method for RDW that minimizes physical boundary collisions.
    • To enhance user immersion and continuous walking distance in virtual reality.

    Main Methods:

    • Proposed a predictive reset method using spatial probability density distribution.
    • Incorporated spatial features (layout energy) and user walking intention (intention inference model).
    • Developed a fusion energy map considering both virtual and real spaces for optimal reset direction.

    Main Results:

    • Significantly reduced physical collisions compared to prevalent reset methods.
    • Increased continuous walking distance in RDW.
    • Demonstrated superior applicability with various RDW controllers.

    Conclusions:

    • The predictive reset method effectively addresses RDW boundary collision issues.
    • The approach enhances user experience by considering spatial features and walking intention.
    • This method offers a robust solution for immersive virtual reality interactions.