Underwater bio-inspired robots have emerged as a promising alternative to conventional propeller-driven autonomous underwater vehicles and remotely operated vehicles because of their potential for high propulsive efficiency, superior maneuverability, reduced acoustic signatures, and enhanced environmental adaptability. Unlike rigid propellers operating under approximately steady inflow conditions, bio-inspired propulsion relies on strongly unsteady hydrodynamic mechanisms, including vortex generation and shedding, added-mass effects, boundary-layer evolution, and flexible fluid–structure interaction (FSI). These processes fundamentally govern thrust production, energy conversion, and maneuvering performance, yet a systematic synthesis connecting hydrodynamic mechanisms with engineering implementation remains limited. This review addresses that gap from a hydrodynamic perspective. First, the major propulsion modes of aquatic organisms, including body and caudal fin (BCF), median and paired fin (MPF), and jet propulsion, are summarized together with their characteristic wake structures. Key unsteady flow mechanisms are then discussed, including reverse Kármán vortex streets, leading-edge vortex dynamics, dynamic stall, boundary-layer behavior, wake instabilities, and biomimetic drag-reduction strategies. Particular attention is given to flexible FSI, including modeling frameworks, passive deformation–active actuation coupling, stiffness and morphology effects, and energy-transfer pathways. Representative studies report propulsive efficiencies of approximately 50–70% for optimized flexible flapping foils and above 70% for phase-tuned dual-foil systems, while biomimetic surface designs have achieved approximately 5–10% drag reduction under specific flow conditions. However, these gains remain strongly condition-dependent, and their practical transfer is still limited by scale effects, propulsor interference, model uncertainty, material degradation, biofouling and insufficient marine validation. Future directions are proposed in real-environment hydrodynamics, multi-robot flow coordination, interdisciplinary modeling, and advanced materials. This review provides a mechanism-to-design framework for understanding, designing, and optimizing next-generation underwater bio-inspired robots.

Adaptive locomotion across continuous terrain transitions remains difficult for hexapod robots because contact timing, body attitude, support height, and load distribution change simultaneously along a route. This paper presents a unified proprioception-driven deep reinforcement learning and central pattern generator (DRL-CPG) framework for terrain-transition locomotion without visual terrain classification, explicit terrain labels, or terrain-specific controller switching. A high-level proximal policy optimization policy maps a 46-dimensional proprioceptive observation to a three-dimensional CPG modulation action comprising oscillation amplitude, swing-phase frequency, and turn modulation. A coupled six-node Hopf oscillator network then expands these modulated parameters into phase-coordinated rhythmic commands, which are mapped to the 18 joint targets of a JetHexa hexapod and executed by a low-level proportional-derivative controller. The observation space contains body linear velocity, body angular velocity, relative joint positions, relative joint velocities, the previous three-dimensional policy action, and inertial measurement unit (IMU)yaw/heading relative to the initial track direction. A continuous route consisting of flat ground, uphill stairs, irregular terrain, downhill stairs, and a recovery segment is defined to evaluate transition-aware locomotion using route completion, velocity-tracking error, lateral deviation, and roll/pitch fluctuation. Compared with the fixed-parameter CPG and end-to-end DRL baselines, the proposed method increased the full-distance success rate at 4.7 m from 9% and 20%, respectively, to 88%, while maintaining smoother velocity, lateral deviation, and roll/pitch responses. The framework preserves the rhythmic prior of CPG control while reducing the exploration burden of reinforcement learning, providing a compact formulation for adaptive hexapod locomotion across terrain transitions.

This study investigates the self-propulsion hydrodynamics of an underwater robot combining four oscillating-fin units with pulsed-jet actuation. A numerical framework couples three-dimensional unsteady computational fluid dynamics, dynamic meshing, and longitudinal self-propulsion dynamics for parallel (PS) and circular (CS) fin-motion arrangements. Fin-only propulsion provides the baseline for evaluating amplitude effects and jet assistance. Increasing the oscillation amplitude raises the stable swimming velocity U_stable in both arrangements, with CS maintaining a higher baseline speed. Under hybrid propulsion, the central jet entrains and stretches the fin wakes, reorganises vortical structures, and redistributes the pressure field. In CS mode, the mean swimming-speed enhancement was approximately 145.5%, 92.1%, and 65.3% at oscillation amplitudes of 20°, 25°, and 30°, respectively, demonstrating an amplitude-dependent benefit of pulsed-jet assistance. Averaged across the investigated amplitudes over the acceleration interval, positive streamwise impulse increases by approximately 116.9% and 117.6% in PS and CS modes, respectively, while propulsive impulse effectiveness increases by 2.9 and 1.6 percentage points. These impulse measures describe hydrodynamic loading rather than energetic efficiency. The results are consistent with direct jet momentum input accompanied by unsteady fin–jet interactions and provide a hydrodynamic basis for the configuration design and parameter selection of hybrid underwater propulsion systems.