Underwater Vehicle Research Articles (Page 1)

Remotely Operated Underwater Vehicles (ROUVs) are increasingly important for high-resolution surveying of narrow shipping lanes. This paper presents 3Clifs, a LiDAR- and AI-enhanced ROUV designed for near-real-time topographic mapping and navigation support in shallow, constrained waterways, demonstrated at three cliff sites in the Suez Canal. The system integrates three-dimensional Light Detection and Ranging (LiDAR) scanning, an Inertial Measurement Unit (IMU), and an onboard processor running the Robot Operating System (ROS) for Simultaneous Localization and Mapping (SLAM). To address data loss from underwater LiDAR (caused by scattering and reflection), we introduce an AI-driven optimisation module that reconstructs missing point cloud data and improves SLAM continuity. We also report propulsion and propeller design changes (propeller v05_1) that reduce flow turbulence and improve scan stability. We compare our approach to sonar-only ROUV mapping and to recent ROUV/LiDAR studies using metrics including point-cloud completeness, SLAM continuity, and navigation-path deviation. The main contributions are: (i) an integrated LiDAR, ROS and AI pipeline for underwater SLAM with missing-point recovery; (ii) a propulsion configuration optimized for LiDAR scanning stability; and (iii) a real-world Suez Canal case study demonstrating practical benefits for narrow-lane navigation.

A small-scale three-degree-of-freedom decompression launch experiment method is used to study the flow characteristics in a ventilated cavity at different transverse velocities. The study subjects are three typical head-shaped underwater vehicles: hemispherical, ellipsoidal, and conical. The evolution mechanism of the ventilated shoulder cavity in a vehicle under transverse velocity is investigated, and the effects of transverse velocity and vehicle head shape changes on the cavity are summarized. Research results show that the hemispherical-headed vehicle’s ventilated cavity is prone to cavity pre-positioning, thereby affecting the distribution of the confronted stream surface (CSS) cavity. As the transverse velocity increases, the cavity pre-positioning point disappears, and the degree of deflection in the vehicle’s trajectory increases. The difference between the opposing stream surface (OSS) and the CSS cavities decreases as the cavities shed. The drag effect of the shedding air mass causes a change in the cavity closure angle. At high transverse velocity (vt = 0.6 m/s), the cavity difference between the OSS and CSS of the ellipsoidal vehicle is the largest, and the amount of gas shed at the cavity’s end is the smallest. The initial angle of the closure angle at the cavity end is related to the ability of the air mass near the tube (AMNT) to be drawn in by the head shape of the vehicle. Under the influence of transverse velocity, the shedding cavity deflects toward the OSS. The interaction patterns between the shoulder and tail cavities on vehicles with different head shapes primarily include three modes.

Thruster-driven autonomous underwater vehicles (AUVs) are designed to maneuver at low speeds and carry out dynamic station keeping operations. In this paper, a dynamic model of a thruster-actuated axisymmetric AUV operating at low speed is presented along with a four-quadrant thruster model. The hydrodynamics of the AUV were estimated using Semi-empirical formulation and computational fluid dynamics simulations. To simulate realistic thruster forces, a four-quadrant thruster model is integrated into the AUV’s maneuvering dynamics. A robust control strategy based on the uncertainty and disturbance estimation (UDE) method is formulated for depth, pitch, and heading control of a thruster-driven AUV. UDE control consists of nominal feedback plus the estimator which utilizes a low-pass filter with a proper bandwidth to estimate the unmodeled dynamics and unknown external disturbances. We introduce an optimization framework for tuning the filter time constant value to improve the performance and energy efficiency. The effectiveness of the proposed control system is demonstrated through numerical simulations considering model uncertainty, thruster nonlinear model, input saturation, external disturbances, and measurement noises. The proposed UDE control design is compared with Time Delay Estimation (TDE) control and Sliding Mode Control (SMC), and implementation issues are addressed. The comparative analysis shows that the UDE control provides enhanced robustness and performance across various maneuvering scenarios, making it a viable solution for the AUV motion control. Experiments were conducted on a testbed AUV for trajectory tracking in depth and yaw degrees of freedom to demonstrate practical realization of the UDE control. The experimental results confirm the effectiveness of the proposed control strategy in presence of system uncertainties.

As the economy and society continue to develop, the range of underwater vehicles is expanding and technology is constantly being upgraded. Consequently, it is becoming increasingly difficult to classify and identify them, and the traditional classification method based on signal characteristics can no longer meet the urgent need for the accurate identification of underwater targets. This paper therefore proposes multiple convolutional neural network recognition methods based on enhanced Gramian Angular Field (GAF) images. Firstly, the radiated noise signals of underwater targets are converted into enhanced images using the GAF method. Then, the converted image dataset is used as input for the convolutional neural network. The input dataset is modified accordingly for each convolutional neural network. Finally, the significant advantages of convolutional neural networks in image processing are leveraged to achieve precise classification of underwater target radiated noise. In order to propose a convolutional neural network method that matches the enhanced image method, this paper compares the calculation results of multiple convolutional neural network models. The experimental results show that the VGG-16 model achieves greater classification accuracy and efficiency, reaching 80.67%.

Abstract In underwater applications, the mechanical noise has become one of the key limiting factors affecting the acoustic stealth performance of underwater vehicles. Among them, the main power unit noise and propeller noise have been effectively controlled. However, the hydraulic pipeline system noise still needs to be solved. In this paper, a piezoelectric actuated attenuator for underwater pipeline pulsation suppression is developed. The attenuator changes the local liquid volume in the secondary channel through the output displacement of the piezoelectric stack, causing regular changes in the pressure in the area, thereby generating secondary pressure pulsations to offset the pressure pulsations in the main path. The system identification is conducted based on a customized fabricated prototype and the built experimental set. Then an active noise control (ANC) technology based on the developed attenuator using enhanced variable step-size filtered-x least mean square (VSS-FxLMS) algorithm is proposed to suppress low frequency vibration and underwater noise in the pipelines. The ANC is tested and compared with existing algorithms to show the effectiveness. Results show that the proposed VSS-FxLMS can suppress the initial pulsation fluctuations fast by converging within 1 second and maintaining a low residual error in the steady state stage. In terms of quantitative comparison of the noise attenuation, PID and phase shift controllers achieved attenuation effects of –7.62 dB and –8.04 dB respectively, while the FxLMS algorithm improved to –10.34 dB. As for the proposed VSS-FxLMS, it shows better control effects in each stage, and finally achieves a noise attenuation of –11.83 dB. The developed piezo-actuated attenuator as well as its ANC algorithm can provide a new structure and control scheme for noise attenuation of underwater vehicle pipeline systems.

ABSTRACT In the underwater target detection missions, such as locating crashed aircraft, autonomous underwater vehicles (AUVs) often adopt a region tracking methodology. This approach only requires the AUVs’ actual trajectory to remains within a bounded ‘desired region’ centered around the planned trajectory, rather than demanding high-precision trajectory tracking. To extend the single-dive duration of AUVs during such tasks, low-energy-consumption region tracking control is a critical research focus. This paper presents the first application of Tube Model Predictive Control (Tube-MPC) to the region tracking control of AUVs. However, direct implementation of Tube-MPC induces significant control output chattering, which in turn increases energy consumption. To address this issue, a region-dependent weighting Tube-MPC method (RDW-TMPC) is proposed, where the weight of the position error term in the cost function is dynamically adjusted based on the AUV’s real-time position within the desired region. To further enhance chattering suppression and energy efficiency, this paper proposes a region-dependent damping filtering region tracking control method (RDDF-RTC). The damping coefficient in RDDF-RTC is dynamically adjusted according to the AUV’s real-time position. Using the Falcon AUV as the simulation platform, the effectiveness of the proposed method is validated.

Abstract To address the challenges of autonomous underwater vehicle (AUV) formation control, obstacle avoidance, and reformation after obstacle avoidance, this paper proposes a formation control and collision avoidance method for an AUV swarm that integrates a virtual structure with an improved artificial potential field (APF) method. First, a leader-virtual follower formation control structure is rigorously defined. Subsequently, a formation point tracking control law is systematically derived by employing the sliding mode control algorithm. Then, the APF method is incorporated into formation control, where the virtual follower serves as the gravitational center to construct a gravitational potential field on the basis of dynamic velocity compensation. The obstacles are treated as repulsive points to establish a repulsive potential field via an adaptive boundary defined by radial basis functions. This approach effectively addresses the issues of dynamic target tracking lag and obstacle avoidance delays in formation control. Theoretical simulations and tests using "Haixiang-500XP" AUVs demonstrate that AUV formation can be rapidly established and effectively avoid obstacles under random initial position conditions. Furthermore, after obstacle avoidance, AUV formation can be autonomously reconfigured.

Accurately predicting the long-term behavior of complex dynamical systems is a central challenge for safety-critical applications like autonomous navigation. Mechanistic models are often brittle, relying on difficult-to-measure parameters, while standard deep learning models are black boxes that fail to generalize, producing physically inconsistent predictions. Here, we introduce a physics-informed framework that learns the continuous-time dynamics of an Autonomous Underwater Vehicle (AUV) by discovering its underlying energy landscape. We embed the structure of Port-Hamiltonian mechanics into a neural ordinary differential equation (NODE) architecture, learning not to imitate trajectories but rather to identify the system’s Hamiltonian and its constituent physical matrices from observational data. Geometric consistency is enforced by representing rotational dynamics on the SE(3) manifold, preventing numerical error accumulation. Experimental validation reveals a stark performance divide. While a state-of-the-art black-box model matches our accuracy in simple, interpolative maneuvers, its predictions fail catastrophically under complex controls. Quantitatively, our physics-informed model maintained a mean 10 s position error of a mere 3.3 cm, whereas the black-box model’s error diverged to 5.4 m—an over 160-fold performance gap. This work establishes that the key to robust, generalizable models lies not in bigger data or deeper networks but in the principled integration of physical laws, providing a clear path to overcoming the brittleness of black-box models in critical engineering simulations.

Enhancing the performance of biomimetic underwater vehicles requires a deeper understanding of the fluid dynamics of fish-like undulation kinematics. This study numerically investigates the hydrodynamic performance and wake structures of a two-dimensional (2D) foil undergoing asymmetric (outward vs retract) traveling wave undulation. A kinematic model integrating quarter-specific asymmetry (parameterized by ζ) with a foil of constant midline length is used to systematically explore the effects of Reynolds number (Re=250–2000), Strouhal number (St=0.1–1.0), and motion asymmetry (ζ=0.6–1.4). The results show that asymmetric motion with a slow-outward/fast-retract stroke (ζ>1) improves thrust generation and shifts the drag-thrust boundary to lower values of Re and St, although at the expense of reduced efficiency compared to symmetric motion (ζ=1). We propose a generalized thrust model for the 2D undulating foil, C¯T=c1Rec2St3+C¯T0, which is valid within the studied parameter space. The coefficients c1 and c2 exhibit a strong dependence on ζ. Furthermore, four distinct wake structures are identified, showing transitions governed by Re, St, and ζ. While hook-shaped vortices (ζ>1) enhance thrust and tadpole-shaped vortices (ζ<1) impair it, both are linked to low propulsive efficiency due to power dissipation from lateral motions. The direction of wake deflection can be altered by vortex interactions and can be determined from the distribution of the dimensionless cycle-averaged velocity and pressure, along with their relative magnitudes on the upper and lower surfaces of the foil. These findings may help provide insights into the flow physics of fish-like propulsion and offer practical implications for the design and control of bio-inspired underwater vehicles.

Hydrodynamic characteristics of lateral movement in underwater vehicles with separated undulating fins

In engineering applications, non-cooperative signals lack well-defined analytical models, and existing synthetic array methodological frameworks based on single-hydrophone exhibit fundamental limitations due to their inability to resolve ambiguous signal characteristics. Moreover, traditional broadband beamforming techniques-including the incoherent subspace method and coherent subspace method-suffer from practical limitations in robustness and directional precision. To systematically address the two aforementioned challenges in a decoupled manner, this work leverages the Fourier transform's properties to achieve "time alignment" of virtual array elements in the frequency domain. In addition, the Incoherent conventional beamforming-coherent minimum variance distortionless response (ICBF-CMVDR) algorithm is proposed. The algorithm ensures excellent directivity and significantly enhances robustness. Simulation results demonstrate that the direction-of-arrival (DOA) estimation error of the proposed algorithm is merely 3° at a signal-to-noise ratio (SNR) of -20 dB, and the effective DOA estimation lower limit is extended to an SNR of -24 dB. Validation using sea trial data reveals that, when applied to the same underwater unmanned vehicle, the ICBF-CMVDR algorithm extends the duration of effective DOA estimation by more than 100 s compared to the currently implemented complex sound intensity-frequency histogram method.

An ANN-based force allocation scheme of an unmanned underwater vehicle and thrust control by system identification method

Fault-Tolerant Control for Autonomous Underwater Vehicles With Prescribed Tracking Accuracy

An integrated water-air cross-domain controller for seamless trajectory tracking of the micro quadrotor hybrid aerial underwater vehicle

Robust hybrid visual servoing for hovering control of autonomous underwater vehicles in unstructured environments

Flexible and safe navigation of autonomous underwater vehicles with input-dynamics move blocking

SIO-UV: Rapid and Robust Sonar Intertial Odometry for Underwater Vehicles

Research on the wave energy capture performance of an underwater vehicle with hydrofoils

An intelligent positioning method for underwater vehicle under hydroacoustic noises

Fault Detection for Autonomous Underwater Vehicles Based on Zonotopic Set-Membership Estimation