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    Area of Science:

    • Robotics and Navigation
    • Geophysics
    • Animal Behavior

    Background:

    • Animals utilize spatial cognition and dead reckoning for navigation, often using the geomagnetic field (GF).
    • Existing underwater navigation systems may require prior geographical knowledge or detailed maps.
    • Autonomous navigation in challenging environments like underwater remains a significant challenge.

    Purpose of the Study:

    • To develop a novel long-distance underwater geomagnetic navigation (LDUGN) method.
    • To enable navigation using only the declination (D) and inclination (I) components of the GF.
    • To achieve autonomous underwater navigation without prior location or map data.

    Main Methods:

    • Utilizing high-precision geomagnetic sensors to measure D and I components.
    • Periodically comparing measured D and I with destination values to determine next-step velocity and direction.
    • Implementing a model predictive control (MPC) algorithm with constraints for trajectory optimization.
    • Recalculating optimal control at each sampling instant to adapt to environmental changes.

    Main Results:

    • The proposed LDUGN method demonstrates feasibility and accuracy in simulations.
    • The MPC algorithm effectively overcomes interferences from geomagnetic fluctuations, storms, ocean currents, and local anomalies.
    • Navigation trajectory is optimized through continuous control adjustments.

    Conclusions:

    • The novel LDUGN method offers a viable solution for autonomous underwater navigation.
    • The reliance solely on GF components (D and I) simplifies navigation requirements.
    • The MPC approach enhances robustness against environmental disturbances, paving the way for reliable underwater exploration.