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Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle.

Hui Ma1, Xiaokai Mu2,3, Bo He4

  • 1Shanghai Marine Electronic Equipment Research Institute, Shanghai 201108, China.

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|October 13, 2021
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Summary
This summary is machine-generated.

This study introduces an adaptive deep learning navigation algorithm for autonomous underwater vehicles (AUVs). It enhances AUV position accuracy and robustness by correcting sensor errors and filtering faulty measurements.

Keywords:
autonomous underwater vehicledeep learningnavigation algorithmvariational Bayesian

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence in Navigation
  • Sensor Fusion for Autonomous Systems

Background:

  • Accurate navigation is critical for autonomous underwater vehicles (AUVs).
  • Microelectromechanical systems (MEMS) sensor deviations significantly impact AUV localization accuracy.
  • Existing deep learning methods face challenges in calculation speed and robustness for practical AUV navigation.

Purpose of the Study:

  • To develop an adaptive deep learning navigation algorithm for AUVs that overcomes the limitations of current methods.
  • To enhance the robustness and accuracy of AUV navigation systems.
  • To improve the real-world applicability of deep learning in AUV localization.

Main Methods:

  • Employing deep learning to generate low-frequency position information for correcting navigation system error accumulation.
  • Utilizing the χ2 rule to detect and mitigate interference from Doppler velocity log (DVL) outliers.
  • Implementing an adaptive filter based on the variational Bayesian (VB) method for simultaneous estimation of navigation information and measurement covariance.

Main Results:

  • The proposed algorithm demonstrated robust navigation performance in AUV field tests.
  • Significant improvements in AUV position accuracy were achieved.
  • The method effectively corrected accumulated errors and filtered sensor outliers.

Conclusions:

  • The adaptive deep learning navigation algorithm offers a robust solution for accurate AUV navigation.
  • The integration of deep learning with adaptive filtering and outlier detection enhances AUV localization.
  • This approach provides a significant advancement for reliable underwater autonomous vehicle operations.