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Autonomous underwater vehicles play a crucial role in marine and underwater exploration, as well as in monitoring aquatic species. To achieve AUV autonomy and enable free movement, obstacle detection, collision avoidance, and path-planning technologies are employed. This article primarily focuses on recent state-of-the-art methods for AUVs, providing a critical analysis to inform researchers in their endeavors in this marine field. The main objective of this article is to provide a comprehensive comparison and analysis of the output characteristics, state-space models, constraints, and breakthroughs in AUV obstacle detection, collision avoidance, and path planning. Finally, this paper concludes with suggestions for future research directions to guide further investigation.

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Innovative State-of-the-Art Reinforcement Learning Methods for Obstacle Management in Underwater Vehicles

  • 会议论文

  • 发表日期：

    2025年

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  • V. Baranidharan,
  • V. M. Nandhakumar,
  • M. K. Sanmathi,
  • S. Jesu Antony Raj,
  • K. R. Raam Kumar,
  • S. K. Visvaa

摘要

Autonomous underwater vehicles play a crucial role in marine and underwater exploration, as well as in monitoring aquatic species. To achieve AUV autonomy and enable free movement, obstacle detection, collision avoidance, and path-planning technologies are employed. This article primarily focuses on recent state-of-the-art methods for AUVs, providing a critical analysis to inform researchers in their endeavors in this marine field. The main objective of this article is to provide a comprehensive comparison and analysis of the output characteristics, state-space models, constraints, and breakthroughs in AUV obstacle detection, collision avoidance, and path planning. Finally, this paper concludes with suggestions for future research directions to guide further investigation.