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Updated: Jun 14, 2025

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An Integrated Navigation Method Aided by Position Correction Model and Velocity Model for AUVs.

Pengfei Lv1, Junyi Lv2, Zhichao Hong1,3

  • 1Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new navigation method for autonomous underwater vehicles (AUVs) that combines a velocity model and a position correction model. The approach significantly enhances underwater navigation accuracy when GPS is unavailable.

Keywords:
autonomous underwater vehicleextended kalman filterposition correction modelunderwater navigationvelocity model

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Area of Science:

  • Robotics
  • Oceanography
  • Artificial Intelligence

Background:

  • Autonomous underwater vehicles (AUVs) face navigation challenges due to the absence of GPS underwater.
  • Traditional navigation systems like the extended Kalman filter (EKF) suffer from accumulated errors without position assistance.

Purpose of the Study:

  • To develop an innovative navigation method for AUVs to improve accuracy in GPS-denied underwater environments.
  • To enhance the reliability and accuracy of AUV missions through improved positioning.

Main Methods:

  • Developed an online-trained velocity model using a dynamic model and Optimal Pruning Extreme Learning Machine (OP-ELM).
  • Constructed a position correction model (PCM) using a hybrid gated recurrent neural network (HGRNN) to correct AUV positions.
  • Integrated the velocity and position correction models with the EKF for enhanced navigation (VM-PCM-EKF algorithm).

Main Results:

  • The proposed VM-PCM-EKF algorithm significantly improves AUV positioning accuracy.
  • Achieved a maximum accuracy improvement of 87.2% compared to conventional EKF algorithms.
  • Demonstrated more consistent and reliable velocity estimation during Doppler Velocity Log (DVL) update intervals.

Conclusions:

  • The integrated velocity and position correction models effectively mitigate navigation errors in GPS-unavailable underwater scenarios.
  • The proposed method enhances the reliability of AUV operations, enabling more complex and extended underwater missions.
  • This approach offers a robust solution for precise AUV navigation in challenging underwater environments.