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Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle.

Yang Yang1, Quan Li1, Junnan Zhang1

  • 1Research Institute of USV Engineering, Shanghai University, Shanghai 200444, China.

Sensors (Basel, Switzerland)
|January 17, 2020
PubMed
Summary

This study introduces an adaptive iterative learning algorithm to optimize turning parameters for unmanned surface vehicles (USVs). This enhances path-following performance and measurement accuracy during hydrographic surveys.

Keywords:
USViterative parameter-tuningpath-smoothingspeed profile design

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Area of Science:

  • Robotics
  • Oceanography
  • Control Systems

Background:

  • Path-planning algorithms often overlook vehicle dynamics, impacting measurement accuracy during high-speed turns in hydrographic surveys.
  • Existing methods may not adequately account for the complex dynamic characteristics of unmanned surface vehicles (USVs).

Purpose of the Study:

  • To develop and evaluate an adaptive iterative learning algorithm for optimizing turning parameters in USVs.
  • To improve path-following performance and measurement accuracy in hydrographic survey activities by considering vehicle dynamics.

Main Methods:

  • An adaptive iterative learning algorithm was proposed to optimize turning radius and speed for USVs.
  • The optimized parameters were used to generate path and speed profiles for enhanced navigation.

Main Results:

  • The proposed algorithms demonstrated significant improvements in path-following performance.
  • Optimized turning parameters led to better path-smoothing and speed profile design.

Conclusions:

  • Accounting for USV dynamic characteristics is crucial for accurate hydrographic surveys.
  • The developed algorithm offers a promising approach to enhance the efficiency and precision of autonomous marine survey operations.