AI-Aided Advancements in Autonomous Underwater Vehicle Navigation
For researchers and engineers in autonomous underwater navigation, this provides a comprehensive roadmap of recent AI techniques to address the challenge of high-precision positioning in GPS-denied environments.
This chapter reviews recent AI-aided advancements in AUV navigation, focusing on sensor fusion and AI-driven learning approaches to enhance inertial dead-reckoning and adaptive fusion for high-precision underwater positioning.
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.