AI-Aided Advancements in Autonomous Underwater Vehicle Navigation

TL;DR

This paper reviews recent AI-driven advancements in autonomous underwater vehicle navigation, emphasizing sensor fusion and learning approaches to overcome environmental challenges for precise deep-sea exploration.

Contribution

It highlights novel AI-based sensor fusion architectures and learning methods that improve AUV positioning accuracy beyond traditional filtering techniques.

Findings

AI-enhanced sensor fusion improves navigation accuracy.

Learning approaches outperform traditional model-based filtering.

The chapter provides a comprehensive roadmap for high-precision AUV navigation.

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.