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Bounded Cost Path Planning for Underwater Vehicles Assisted by a Time-Invariant Partitioned Flow Field Model.

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This study introduces a novel bounded cost path planning method for underwater vehicles using data-driven flow modeling. The approach efficiently finds optimal paths in complex flow fields, ensuring cost constraints are met.

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Area of Science:

  • Robotics
  • Ocean Engineering
  • Computational Fluid Dynamics

Background:

  • Underwater vehicles require efficient path planning in dynamic ocean currents.
  • Existing methods often struggle with computational efficiency and accuracy in complex flow fields.
  • Data-driven modeling offers a promising approach for representing environmental conditions.

Purpose of the Study:

  • To develop a bounded cost path planning method for underwater vehicles.
  • To integrate data-driven flow modeling for enhanced path planning accuracy.
  • To improve computational efficiency compared to existing path planning algorithms.

Main Methods:

  • A data-driven flow modeling method partitions the flow field into cells with piece-wise constant flow speeds.
  • Flow partition and parameter estimation algorithms are proposed with convergence guarantees.
  • A bounded cost path planning algorithm utilizes the partitioned flow model and an extended potential search method.
  • Constrained optimization problems solve for the optimal path within each partition.

Main Results:

  • The proposed extended potential search method theoretically guarantees optimal solutions.
  • The planned paths demonstrate a high probability of satisfying bounded cost constraints.
  • Experimental and simulation results validate the method's performance.
  • The developed method shows superior computational efficiency over existing approaches.

Conclusions:

  • The novel bounded cost path planning method effectively guides underwater vehicles in complex flow fields.
  • The integration of data-driven flow modeling significantly enhances planning capabilities.
  • The method offers a computationally efficient and reliable solution for autonomous underwater navigation.