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Area of Science:

  • Marine robotics
  • Oceanography
  • Underwater navigation

Background:

  • Autonomous Underwater Vehicles (AUVs) face challenges in marine environments.
  • Power loss can cause AUVs to lose buoyancy and drift, especially in thermoclines.
  • Current methods for predicting long-term AUV drift are insufficient.

Purpose of the Study:

  • To develop a prediction method for AUV drift trajectories after long-term power loss.
  • To forecast potential resurfacing locations for disabled AUVs.
  • To provide technical support for search and salvage operations.

Main Methods:

  • Proposed a three-dimensional trajectory prediction method using the Lagrange tracking approach.
  • Incorporated AUV longitudinal velocity, ascent time, and ocean current data.
  • Developed a method for estimating thermocline currents to predict lateral drift.

Main Results:

  • The proposed method accurately predicts AUV drift trajectories.
  • Simulations showed small directional and positional errors compared to real accident data.
  • Validated the effectiveness of the trajectory prediction approach.

Conclusions:

  • The developed method offers a reliable way to predict AUV drift after power loss.
  • This aids in locating disabled AUVs, improving salvage efficiency.
  • Addresses a critical gap in current AUV accident response capabilities.