Hongwu Huang

Disc-shaped Autonomous Underwater Helicopters (AUHs) offer superior maneuverability but suffer from high hydrodynamic drag, which limits their operational endurance. To address this challenge, this study proposes a robust optimization framework for a novel sea-turtle-inspired AUH. A parametric hull, governed by two dimensionless shape factors based on modified Myring equations, was established to facilitate systematic exploration. To reduce the high computational cost of direct CFD evaluations, a high-precision Gaussian Process Regression (GPR) surrogate model was constructed from a small dataset of 24 samples. The core methodological innovation is T-NSGA-II, an algorithm featuring hyperparameters that are systematically optimized by the Optuna framework. In comparative evaluations, the T-NSGA-II-generated Pareto front demonstrated clear superiority over the standard NSGA-II, identifying designs with significantly lower drag for an equivalent vertical force. A key scientific contribution of this research is the identification of a distinct performance gap on the Pareto front. This phenomenon is interpreted not as an algorithmic artifact but as a ‘natural gap’, reflecting a deep physical trade-off, with potential underlying causes including a critical transition in flow physics or a topological shift in the optimal hull geometries. This work not only delivers a suite of optimized, practical AUH designs but also presents a powerful, intelligent optimization methodology that is capable of revealing fundamental physical trade-offs in complex engineering problems.

In complex underwater environments, the stability of navigation for autonomous underwater vehicles (AUVs) is critical for mission success. To enhance the reliability of the AUV-integrated navigation system, fault detection technology was investigated. Initially, the causes and classifications of faults within the integrated navigation system were analyzed in detail, and these faults were categorized as either abrupt or gradual, based on variations in sensor output characteristics under fault conditions. To overcome the limitations of the residual chi-square method in detecting gradual faults, a cumulative residual detection approach with error coefficient amplification was proposed. The algorithm enhances gradual fault detection by using eigenvalue analysis and constructing fault-frequency-based error amplification coefficients with non-parametric techniques. Furthermore, to improve the detection of gradual faults, artificial intelligence-based fault detection methods were also explored. Specifically, the particle swarm optimization (PSO) algorithm was employed to optimize the hyperparameters of a long short-term memory (LSTM) neural network, leading to the development of a PSO-LSTM fault detection model. In this model, the fault detection function was formulated by comparing the predictions generated by the PSO-LSTM model with those derived from the Kalman filter. The experimental results demonstrated that the fault detection function formulated by PSO-LSTM exhibited enhanced sensitivity to gradual faults and enabled the timely isolation of faulty sensors. In unfamiliar marine regions, the PSO-LSTM method demonstrates greater stability and avoids the need to recalibrate detection thresholds for each sea area—an important advantage for AUV autonomous navigation in complex environments.

Advancements in underwater robotic fish have generated new requirements for diverse underwater scenarios, presenting challenges in attaining efficient and precise control, particularly in the realm of classical trajectory tracking. In response to the inherently nonlinear and underactuated characteristics of underwater robot control design, this study introduces a trajectory tracking backstepping control method for the planar motion of underactuated underwater robotic systems. The method is grounded in dung beetle optimization (DBO) backstepping control. Firstly, a dynamic model of a single-node tail-actuated robotic fish is introduced, and the model is averaged. Based on the averaged model and Lyapunov functions, the design of the backstepping control scheme is derived to ensure the stability of the control system. Subsequently, the derived backstepping control is further optimized through the application of the DBO optimization algorithm, then the optimal backstepping control (OBC) approach is presented. Finally, the proposed control scheme is applied to the simulation experiments with the robotic fish. The simulation results for straight-line tracking indicate that OBC is superior to the PID method in terms of overshoot performance, reducing the average overshoot from 0.23 to 0.02. Additionally, OBC reduces the average velocity error from 0.043 m/s (backstepping control) to 0.035 m/s, which is lower than that of the PID method, with an average velocity error of 0.054 m/s. In turn tracking, the simulation results reveal that OBC reduces the average velocity error from 0.067 m/s (backstepping control) to 0.055 m/s and demonstrates better performance than the PID method, with an average velocity error of 0.066 m/s. Under various disturbance conditions, the simulations reveal that OBC exhibits superior performance when compared to other control methods.

This paper presents a novel underwater image enhancement method addressing the challenges of low contrast, color distortion, and detail loss prevalent in underwater photography. Unlike existing methods that may introduce color bias or blur during enhancement, our approach leverages a two-pronged strategy. First, an Efficient Fusion Edge Detection (EFED) module preserves crucial edge information, ensuring detail clarity even in challenging turbidity and illumination conditions. Second, a Multi-scale Color Parallel Frequency-division Attention (MCPFA) module integrates multi-color space data with edge information. This module dynamically weights features based on their frequency domain positions, prioritizing high-frequency details and areas affected by light attenuation. Our method further incorporates a dual multi-color space structural loss function, optimizing the performance of the network across RGB, Lab, and HSV color spaces. This approach enhances structural alignment and minimizes color distortion, edge artifacts, and detail loss often observed in existing techniques. Comprehensive quantitative and qualitative evaluations using both full-reference and no-reference image quality metrics demonstrate that our proposed method effectively suppresses scattering noise, corrects color deviations, and significantly enhances image details. In terms of objective evaluation metrics, our method achieves the best performance in the test dataset of EUVP with a PSNR of 23.45, SSIM of 0.821, and UIQM of 3.211, indicating that it outperforms state-of-the-art methods in improving image quality.

Unmanned surface vehicles (USVs) in nearshore areas are prone to environmental occlusion and electromagnetic interference, which can lead to the failure of traditional satellite-positioning methods. This paper utilizes a visual simultaneous localization and mapping (vSLAM) method to achieve USV positioning in nearshore environments. To address the issues of uneven feature distribution, erroneous depth information, and frequent viewpoint jitter in the visual data of USVs operating in nearshore environments, we propose a stereo vision SLAM system tailored for nearshore conditions: SP-SLAM (Segmentation Point-SLAM). This method is based on ORB-SLAM2 and incorporates a distance segmentation module, which filters feature points from different regions and adaptively adjusts the impact of outliers on iterative optimization, reducing the influence of erroneous depth information on motion scale estimation in open environments. Additionally, our method uses the Sum of Absolute Differences (SAD) for matching image blocks and quadric interpolation to obtain more accurate depth information, constructing a complete map. The experimental results on the USVInland dataset show that SP-SLAM solves the scaling constraint failure problem in nearshore environments and significantly improves the robustness of the stereo SLAM system in such environments.

Autonomous underwater vehicles (AUVs) are susceptible to non-line-of-sight (NLOS) errors and noise bias at receiving stations during the application of hydroacoustic localization systems, leading to a degradation in positioning accuracy. To address this problem, this paper optimizes the Chan-Taylor algorithm. Initially, we propose the Weighted Modified Chan-Taylor (WMChan-Talor) algorithm, which introduces dynamic weights into the Chan algorithm to correct noise variance at measurement stations, thereby improving the accuracy of AUV positioning. Computer simulations validate the effectiveness of the WMChan-Taylor algorithm in enhancing positioning accuracy. To further address the accuracy degradation caused by noise deviations across different receiving stations, we introduce an error-corrected WMChan-Taylor algorithm. This algorithm utilizes a standard residual function to eliminate significant delays caused by large errors at receiving stations and applies standard residual weighting to improve the combined positioning solution. The performance of the error-corrected WMChan-Taylor algorithm is demonstrated through both computer and semi-physical simulation experiments, confirming its capability to isolate noisier stations and thus enhance overall positioning accuracy.

Thornfish (Terapon jarbua) is a significantly commercial species inhabiting the shallow coastal waters of the Indo-Pacific Ocean. To achieve effective underwater acoustic (UWA) monitoring on the abundance and population dynamics of this species, the comprehensive target strength (TS) characteristics should be investigated and understood. In this study, the Kirchhoff-ray mode (KRM) model was adopted to evaluate and analyze the acoustic TS of T. jarbua and its variations with the sound wave frequency, pitch angle distributions as well as morphological characteristics in the South China Sea. A total of 19 samples were captured and evaluated at four types of frequencies of 38 kHz, 70 kHz, 120 kHz, and 200 kHz. The results demonstrated that the TS of T. jarbua varied with the pitch angle shifts, and the number of secondary TS peaks increased as the increasing frequency accordingly. Two classic pitch angle distributions that included N[−5°, 15°] and N[0°, 10°] were adopted to calculate the average TS of T. jarbua. The fitted TS-L regression formulations and the standard b₂₀ form equations were determined at different pitch angle distributions as well as frequencies. These results could support the accurate and reliable UWA abundance estimation in the South China Sea to facilitate a better understanding of the abundance and population dynamics of T. jarbua.

With its superiorities of low cost, high flexibility and deployment convenience, small-size autonomous underwater vehicles (AUVs) have been extensively applied to perform a variety of undersea missions. While underwater acoustic (UWA) communication provides a practical way to establish a wireless link, it still poses a significant challenge due to the strict limitations of a small-size AUV platform in terms of load capacity, energy supply and cost. Orthogonal frequency division multiplexing (OFDM) has drawn extensive attention due to its high data rate capability and relative robustness to multipath, the performance of which is unfortunately sensitive to the widespread Doppler effect. While efficient Doppler compensation is significantly crucial for UWA OFDM mobile communication, most of the conventional approaches are conducted using software resampling, thus rendering a huge burden on memory and calculation capability as well as a considerable processing delay. In this paper, from the perspective of hardware completion an UWA OFDM communication payload based on STM32F407 processor is designed and implemented to facilitate agile Doppler compensation with low computational overhead. In particular, after estimating Doppler by calculating the time compression or extension of the preamble signal, Doppler compensation is performed by directly adjusting the direct digital frequency synthesizer (DDS)-driven sampling rate of the analog-to-digital converter (ADC). As the Doppler is compensated parallel to the ADC acquisition, processing delay and memory requirement can be avoided. Finally, hardware-in-loop (HIP) simulation is performed to demonstrate the effectiveness and superiority of the proposed system. The results show that the designed system has the potential to achieve an effective communication rate of 3.19 kbps with the admissible implementation overhead. Future work will entail the integration and performance evaluation of the proposed UWA OFDM communication payload on a practical small-size AUV platform.