As deep-sea exploration transitions from large-scale search to precision pinpoint operations, the inherent limitations of traditional “rigid-body and propeller” vehicles—specifically in low-speed maneuverability, environmental compliance, and acoustic stealth—are becoming increasingly apparent. Leveraging its unique integrated “gliding-flapping” locomotion and exceptional maneuverability, the manta ray serves as an ideal biological prototype for next-generation deep-sea operational platforms. From a systems engineering perspective, this paper provides a comprehensive review of the current research status and technical evolution of biomimetic manta ray submersibles. First, a technical pedigree centered on “operational depth” is established, illustrating how design paradigms transition from “mechanism replication” in shallow waters to “pressure adaptation” at full-ocean depths. Second, the mechanical challenges in structural design are explored, demonstrating that a “rigid-flexible” gradient distribution strategy is critical to resolving the conflict between pressure resistance and propulsive compliance. Regarding energy and propulsion, the synergistic effects of hybrid gliding-flapping drives and integrated structural batteries in enhancing long-range endurance and energy efficiency are analyzed. Finally, the evolution of motion control architectures—transitioning from spinal-cord-inspired Central Pattern Generator (CPG) rhythmic control to Deep Reinforcement Learning (DRL) featuring embodied intelligence—is outlined.

The intelligent decision-making systems of Autonomous Underwater Vehicle (AUV) are undergoing a significant transformation, shifting from traditional control theories to data-driven paradigms. Deep learning (DL) serves as the primary driving force behind this evolution; however, its application in complex and unstructured underwater en-vironments continues to present unique challenges. To systematically analyze the de-velopment, current obstacles, and future directions of DL-enhanced AUV deci-sion-making systems, this paper proposes an innovative "four-module" decomposition framework, which are information processing module, information understanding module, information judgment module and output module. This framework enables a structured review of the progression of deep learning technologies across each stage of the AUV decision-making information flow. To further bridge the gap between theo-retical advancements and practical implementation, we introduce a task complexity–environment uncertainty four-quadrant analytical matrix, offering strategic guidance for selecting appropriate DL architectures across diverse operational scenarios. Addi-tionally, this work identifies key challenges in the field as well as anticipates future developments to solve these challenges.This paper aims to provide researchers and engineers with a comprehensive and strategic overview to support the design and op-timization of next-generation AUV decision-making architectures.

The integration of artificial intelligence into experimental fluid mechanics promises to accelerate discovery, yet most AI applications remain narrowly focused on numerical studies. This work proposes an AI Fluid Scientist framework that autonomously executes the complete experimental workflow: hypothesis generation, experimental design, robotic execution, data analysis, and manuscript preparation. We validate this through investigation of vortex-induced vibration (VIV) and wake-induced vibration (WIV) in tandem cylinders. Our work has four key contributions: (1) A computer-controlled circulating water tunnel (CWT) with programmatic control of flow velocity, cylinder position, and forcing parameters (vibration frequency and amplitude) with data acquisition (displacement, force, and torque). (2) Automated experiments reproduce literature benchmarks (Khalak and Williamson [1999] and Assi et al. [2013, 2010]) with frequency lock-in within 4% and matching critical spacing trends. (3) The framework with Human-in-the-Loop (HIL) discovers more WIV amplitude response phenomena, and uses a neural network to fit physical laws from data, which is 31% higher than that of polynomial fitting. (4) The framework with multi-agent with virtual-real interaction system executes hundreds of experiments end-to-end, which automatically completes the entire process of scientific research from hypothesis generation, experimental design, experimental execution, data analysis, and manuscript preparation. It greatly liberates human researchers and improves study efficiency, providing new paradigm for the development and research of experimental fluid mechanics.

The intelligent decision-making systems of Autonomous Underwater Vehicles (AUVs) are undergoing a significant transformation, shifting from traditional control theories to data-driven paradigms. Deep learning (DL) serves as the primary driving force behind this evolution; however, its application in complex and unstructured underwater environments continues to present unique challenges. To systematically analyze the development, current obstacles, and future directions of DL-enhanced AUV decision-making systems, this paper proposes an innovative ‘four-module’ decomposition framework consisting of information processing, understanding, judgment, and output. This framework enables a structured review of the progression of DL technologies across each stage of the AUV decision-making information flow. To further bridge the gap between theoretical advancements and practical implementation, we introduce a task complexity–environment uncertainty four-quadrant analytical matrix, offering strategic guidance for selecting appropriate DL architectures across diverse operational scenarios. Additionally, this work identifies key challenges in the field as well as anticipates future developments to solve these challenges. This paper aims to provide researchers and engineers with a comprehensive and strategic overview to support the design and optimization of next-generation AUV decision-making architectures.