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An AUV localization and path planning algorithm for terrain-aided navigation.

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|April 28, 2020
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Summary
This summary is machine-generated.

This study introduces a terrain-aided navigation (TAN) algorithm for autonomous underwater vehicles (AUVs). The method enhances AUV localization and path planning by considering topographic complexity, improving navigation accuracy and performance.

Keywords:
Autonomous underwater vehiclesParticle filterPath planningTerrain-aided navigationTopographic complexity

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Area of Science:

  • Robotics
  • Autonomous Underwater Vehicles
  • Navigation Systems

Background:

  • Terrain-aided navigation (TAN) is crucial for accurate long-term navigation of autonomous underwater vehicles (AUVs).
  • Path planning algorithms are vital in TAN to mitigate positioning errors by avoiding featureless terrain.

Purpose of the Study:

  • To propose an integrated AUV localization and path planning algorithm for terrain-aided navigation.
  • To enhance navigation accuracy and maneuvering performance of AUVs in complex underwater environments.

Main Methods:

  • Developed a novel algorithm combining value function calculation and online path planning for TAN.
  • Incorporated topographic complexity into AUV state transition probabilities for optimal policy calculation.
  • Utilized a particle filter for AUV localization and command, with particle weights influenced by terrain complexity.

Main Results:

  • Simulation experiments validated the algorithm's effectiveness.
  • The proposed algorithm demonstrated accurate TAN localization results.
  • Achieved good maneuvering performance for AUVs using the developed path planning strategy.

Conclusions:

  • The integrated algorithm offers a robust solution for AUV navigation in challenging terrains.
  • Considering topographic complexity significantly improves TAN accuracy and AUV operational efficiency.
  • This approach provides a foundation for more reliable autonomous underwater exploration and operations.