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Area of Science:

  • Robotics and Autonomous Systems
  • Artificial Intelligence
  • Control Theory

Background:

  • Underwater motion planning is complex due to environmental disturbances.
  • Optimal control problems require solving continuous time and state Hamilton-Jacobi-Bellman PDEs.
  • Deep learning offers novel approaches to complex mathematical problems.

Purpose of the Study:

  • To apply deep learning, specifically the Deep Galerkin Method (DGM), to solve underwater motion planning.
  • To incorporate realistic underwater disturbances into the motion planning problem.
  • To evaluate the efficiency and cost-effectiveness of the DGM approach.

Main Methods:

  • Utilized the Deep Galerkin Method (DGM) to approximate the Hamilton-Jacobi-Bellman PDE.
  • Adapted DGM using a surrogate approach to handle underwater disturbances.
  • Compared the DGM approach against a baseline control method.

Main Results:

  • The DGM approach efficiently solved the underwater motion planning problem.
  • Significant cost improvements were observed compared to the baseline control.
  • The method showed particular effectiveness in scenarios with significant environmental disturbances.

Conclusions:

  • Deep learning, via DGM, provides an effective solution for complex underwater motion planning.
  • The surrogate-adapted DGM is robust to environmental disturbances.
  • This method offers substantial advantages in cost reduction for autonomous underwater vehicles.