Toward Intelligent Ocean Exploration: Artificial Intelligence-Driven Innovations in AUV Systems and Future Trends
Year: 2025
Type: article
Abstract: The integration of artificial intelligence (AI) is revolutionizing intelligent ocean exploration, offering transformative solutions for next-generation autonomous underwater vehicles (AUVs). This paper systematically reviews current research on AUVs, identifying three critical challenges: limited underwater endurance due to energy constraints (limited battery capacity and inefficient recharge methods), insufficient adaptability of autonomous control systems in complex marine environments (dynamic obstacle avoidance, unpredictable current disturbances and poor algorithm generalization), and degraded visual perception caused by underwater turbidity and lighting variability (underwater turbidity, light reflection, illumination, mirrored targets, suspended particles, etc.). We analyze these technical bottlenecks through an AI-driven lens and propose targeted improvement strategies. Furthermore, emerging development trends are projected, emphasizing the synergistic advancement of four key areas: Wireless power transfer for AUVs, autonomous underwater vehicle–manipulator systems with artificial intelligence; advanced sensing, navigation and autonomy for AUVs; and collaborative operations (underwater robot swarm operations). The analysis highlights how these innovations will enable adaptive, energy-efficient and environmentally resilient AUV systems, ultimately accelerating sustainable ocean exploration and resource utilization.
Authors: Jianjun Song, Chee-Loon Siow
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411345272