AI-Aided Advancements in Autonomous Underwater Vehicle Navigation
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866909017223200768 |
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| author | Damari, Guy Yampolsky, Zeev Cohen, Nadav Sahoo, Arup Kumar Danial, Jeryes Silva, Felipe O. Klein, Itzik |
| author_facet | Damari, Guy Yampolsky, Zeev Cohen, Nadav Sahoo, Arup Kumar Danial, Jeryes Silva, Felipe O. Klein, Itzik |
| contents | Autonomous underwater vehicles (AUVs) have become indispensable for deep-sea exploration, spanning critical scientific research and commercial applications. The rapid attenuation of electromagnetic waves renders satellite radio signals unavailable, while the dynamic unpredictability of the marine environment presents formidable navigation challenges. This chapter explores recent advancements in AI-aided AUV positioning, specifically focusing on advanced sensor fusion architectures that integrate inertial navigation systems with Doppler velocity logs and cameras. Beyond traditional model-based filtering, we examine the transformative emergence of AI-driven learning approaches in enhancing inertial dead-reckoning tasks and adaptive fusion algorithms. By addressing these recent milestones, this chapter provides a comprehensive roadmap for achieving the high-precision navigation essential for autonomous underwater missions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04672 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI-Aided Advancements in Autonomous Underwater Vehicle Navigation Damari, Guy Yampolsky, Zeev Cohen, Nadav Sahoo, Arup Kumar Danial, Jeryes Silva, Felipe O. Klein, Itzik Robotics Autonomous underwater vehicles (AUVs) have become indispensable for deep-sea exploration, spanning critical scientific research and commercial applications. The rapid attenuation of electromagnetic waves renders satellite radio signals unavailable, while the dynamic unpredictability of the marine environment presents formidable navigation challenges. This chapter explores recent advancements in AI-aided AUV positioning, specifically focusing on advanced sensor fusion architectures that integrate inertial navigation systems with Doppler velocity logs and cameras. Beyond traditional model-based filtering, we examine the transformative emergence of AI-driven learning approaches in enhancing inertial dead-reckoning tasks and adaptive fusion algorithms. By addressing these recent milestones, this chapter provides a comprehensive roadmap for achieving the high-precision navigation essential for autonomous underwater missions. |
| title | AI-Aided Advancements in Autonomous Underwater Vehicle Navigation |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.04672 |