AI-Aided Advancements in Autonomous Underwater Vehicle Navigation
Guy Damari, Zeev Yampolsky, Nadav Cohen, Arup Kumar Sahoo, Jeryes Danial + 2 more
TLDR
This paper reviews AI-aided advancements in AUV navigation, focusing on sensor fusion architectures and AI-driven learning for high-precision positioning.
Key contributions
- Explores advanced sensor fusion architectures for AUVs, integrating INS, DVLs, and cameras.
- Examines AI-driven learning approaches to enhance inertial dead-reckoning tasks in AUVs.
- Discusses the emergence of adaptive fusion algorithms for improved AUV navigation.
Why it matters
AUV navigation is critical for deep-sea exploration but faces challenges from signal loss and dynamic environments. This paper provides a roadmap for high-precision navigation by leveraging AI-aided sensor fusion and learning approaches. It addresses key milestones for robust underwater autonomy.
Original Abstract
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.
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