Underwater Tasks Research Articles

Research on layout optimization for propellers of a submarine operation vehicle

The submarine operation vehicle was an important equipment for exploring the marine environment and performing underwater tasks. In this paper, the calculation and analysis of hydrodynamic performance and the layout optimization of the propellers under different layouts were completed. Based on the left-right symmetrical layout of the four ducted propellers, the two ducted propellers were taken as the research object to optimize its vector layout, and an approximate model was constructed according to the numerical calculation results under different combinations of propeller spacing and deflection angle. The simulation calculation and verification are carried out, and the optimal layout of the four ducted propeller were determined considering the interference between the ducted propellers on both sides. Finally, an experimental platform for the thrust test of the two ducted propellers were built. It was found that the axial thrust ratio of the rear propeller and the front propeller in experimental results were in good agreement with the simulation results, which verified the feasibility of using the numerical simulation method to study the mutual interference between the ducted propellers.

An Integrated Navigation Method Aided by Position Correction Model and Velocity Model for AUVs.

When autonomous underwater vehicles (AUVs) perform underwater tasks, the absence of GPS position assistance can lead to a decrease in the accuracy of traditional navigation systems, such as the extended Kalman filter (EKF), due to the accumulation of errors. To enhance the navigation accuracy of AUVs in the absence of position assistance, this paper proposes an innovative navigation method that integrates a position correction model and a velocity model. Specifically, a velocity model is developed using a dynamic model and the Optimal Pruning Extreme Learning Machine (OP-ELM) method. This velocity model is trained online to provide velocity outputs during the intervals when the Doppler Velocity Log (DVL) is not updating, ensuring more consistent and reliable velocity estimation. Additionally, a position correction model (PCM) is constructed, based on a hybrid gated recurrent neural network (HGRNN). This model is specifically designed to correct the AUV's navigation position when GPS data are unavailable underwater. The HGRNN utilizes historical navigation data and patterns learned during training to predict and adjust the AUV's estimated position, thereby reducing the drift caused by the lack of real-time position updates. Experimental results demonstrate that the proposed VM-PCM-EKF algorithm can significantly improve the positioning accuracy of the navigation system, with a maximum accuracy improvement of 87.2% compared to conventional EKF algorithms. This method not only improves the reliability and accuracy of AUV missions but also opens up new possibilities for more complex and extended underwater operations.

Joint Luminance-Saliency Prior and Attention for Underwater Image Quality Assessment

Underwater images, as a crucial medium for storing ocean information in underwater sensors, play a vital role in various underwater tasks. However, they are prone to distortion due to the imaging environment, which leads to a decline in visual quality, which is an urgent issue for various marine vision systems to address. Therefore, it is necessary to develop underwater image enhancement (UIE) and corresponding quality assessment methods. At present, most underwater image quality assessment (UIQA) methods primarily rely on extracting handcrafted features that characterize degradation attributes, which struggle to measure complex mixed distortions and often exhibit discrepancies with human visual perception in practical applications. Furthermore, current UIQA methods lack the consideration of the perception perspective of enhanced effects. To this end, this paper employs luminance and saliency priors as critical visual information for the first time to measure the enhancement effect of global and local quality achieved by the UIE algorithms, named JLSAU. The proposed JLSAU is built upon an overall pyramid-structured backbone, supplemented by the Luminance Feature Extraction Module (LFEM) and Saliency Weight Learning Module (SWLM), which aim at obtaining perception features with luminance and saliency priors at multiple scales. The supplement of luminance priors aims to perceive visually sensitive global distortion of luminance, including histogram statistical features and grayscale features with positional information. The supplement of saliency priors aims to perceive visual information that reflects local quality variation both in spatial and channel domains. Finally, to effectively model the relationship among different levels of visual information contained in the multi-scale features, the Attention Feature Fusion Module (AFFM) is proposed. Experimental results on the public UIQE and UWIQA datasets demonstrate that the proposed JLSAU outperforms existing state-of-the-art UIQA methods.

A novel underwater object detection enhanced algorithm based on YOLOv5‐MH

AbstractUnderwater object detection is an important application of underwater vehicles. However, traditional underwater object detection algorithms have several shortcomings in underwater settings. These include imprecise feature extraction, slow detection speeds, and a lack of robustness. To address these shortcomings, a novel approach based on YOLOv5‐MH (Multi‐Head) is proposed in this paper. Firstly, an image enhancement technique is utilised. This technique uses adaptive linear mapping to adjust contrast and improve the quality of underwater images. Secondly, the C2f module for feature extraction is employed to enable more effective capture of object characteristics. Subsequently, the multi‐head self‐attention and coordinate attention are integrated into the network's backbone. This integration increases the attention given to input data, thereby enhancing the network's performance in handling complex tasks. Furthermore, a bidirectional feature pyramid is implemented to adeptly handle objects of varying scales and sizes, and elevate model performance. Finally, through comprehensive testing on the 2018 URPC dataset and deep plastic dataset, this method demonstrates superior performance. This performance is compared to the original YOLOv5 and other similar networks. It holds immense promise for practical applications across a wide spectrum of underwater tasks.

Enhanced three-dimensional trajectory tracking control for AUVs in variable operating conditions using FMPC-FTTSMC

In addressing the variable operating conditions encountered in complex underwater tasks, an enhanced three-dimensional tracking control method is proposed for autonomous underwater vehicle (AUV) based on fuzzy model predictive control-finite time terminal sliding mode control (FMPC-FTTSMC). Firstly, to enhance adaptability to tasks and environments, fuzzy model predictive control method is designed to achieve precise three-dimensional trajectory tracking. Additionally, a fuzzy weight allocator is employed to enable AUV to autonomously adjust optimization strategies based on real-time states such as task type, speed, and distance from the seabed, thus better coping with changing conditions and improving the universality of the control method. Secondly, a dynamic controller is designed using the finite-time terminal sliding mode control method, combined with a finite-time radial basis function neural network (FTRBFNN) disturbance observer to accurately estimate disturbances. Finally, simulation results demonstrate that the proposed method effectively reduces optimization solving time compared to traditional model predictive control, achieves trajectory tracking control under various conditions, meets preset state constraints, and demonstrates excellent tracking accuracy and robustness.

Depth control of bionic robotic fish based on fuzzy PID algorithm

Abstract This paper proposes a staged depth control method based on fuzzy PID control to improve the execution of various underwater tasks by robotic fish in pectoral and caudal fin co-propulsion modes in complex underwater environments. The method obtains appropriate PID parameters and regulates the corresponding CPG models by introducing a fuzzy controller into the PID control framework. The method follows the fuzzification, fuzzy rule setting, and defuzzification steps. During the approach phase, the robotic fish adjusts the amplitude and offset of its pectoral fin. In the cruise phase, it adjusts the angle of attack of the pectoral fin to achieve staged depth control. Simulation and analysis results indicate that the staged fuzzy PID control is more effective than pure fuzzy control and PID control. It reduces delay time by 38.5% and 59.4%, optimizes rise time by 31.1% and 44.1%, reduces peak time by 49.7% and 44.6%, and reduces steady-state error by 42.4% and 76.6%. Therefore, the staged fuzzy PID control provides a faster response and greater stability, making it an effective method for depth control of underwater robots.

Development and 3-D Path-Following Control of an Agile Robotic Manta With Flexible Pectoral Fins.

The broad and powerful pectoral fins of manta rays are crucial to their efficient and maneuverable swimming. However, very little is currently known about the pectoral-fin-driven 3-D locomotion of manta-inspired robots. This study is focused on the development and 3-D path-following control of an agile robotic manta. First, a novel robotic manta with 3-D mobility is constructed, of which the distinctive pectoral fins provide the only propulsion. Specifically, the unique pitching mechanism is detailed in which the time-coupled coordination movement of the pectoral fins is applied. Second, based on a 6-axis force measuring platform, the propulsion characteristics of the flexible pectoral fins are analyzed. Then, the force-data-driven 3-D dynamic model is further established. Third, a control scheme combined with a line-of-sight (LOS) guidance system and a sliding-mode fuzzy controller is conceived, addressing the 3-D path-following task. Finally, various simulated and aquatic experiments are conducted, demonstrating the superior performance of our prototype and the effectiveness of the proposed path-following scheme. This study will hopefully generate fresh insights into the updated design and control of agile bioinspired robots performing underwater tasks in dynamic environments.

DyFish-DETR: Underwater Fish Image Recognition Based on Detection Transformer

Due to the complexity of underwater environments and the lack of training samples, the application of target detection algorithms to the underwater environment has yet to provide satisfactory results. It is crucial to design specialized underwater target recognition algorithms for different underwater tasks. In order to achieve this goal, we created a dataset of freshwater fish captured from multiple angles and lighting conditions, aiming to improve underwater target detection of freshwater fish in natural environments. We propose a method suitable for underwater target detection, called DyFish-DETR (Dynamic Fish Detection with Transformers). In DyFish-DETR, we propose a DyFishNet (Dynamic Fish Net) to better extract fish body texture features. A Slim Hybrid Encoder is designed to fuse fish body feature information. The results of ablation experiments show that DyFishNet can effectively improve the mean Average Precision (mAP) of model detection. The Slim Hybrid Encoder can effectively improve Frame Per Second (FPS). Both DyFishNet and the Slim Hybrid Encoder can reduce model parameters and Floating Point Operations (FLOPs). In our proposed freshwater fish dataset, DyFish-DETR achieved a mAP of 96.6%. The benchmarking experimental results show that the Average Precision (AP) and Average Recall (AR) of DyFish-DETR are higher than several state-of-the-art methods. Additionally, DyFish-DETR, respectively, achieved 99%, 98.8%, and 83.2% mAP in other underwater datasets.

Enhancing Autonomous Underwater Vehicle Decision Making through Intelligent Task Planning and Behavior Tree Optimization

The expansion of underwater scenarios and missions highlights the crucial need for autonomous underwater vehicles (AUVs) to make informed decisions. Therefore, developing an efficient decision-making framework is vital to enhance productivity in executing complex tasks within tight time constraints. This paper delves into task planning and reconstruction within the AUV control decision system to enable intelligent completion of intricate underwater tasks. Behavior trees (BTs) offer a structured approach to organizing the switching structure of a hybrid dynamical system (HDS), originally introduced in the computer game programming community. In this research, an intelligent search algorithm, MCTS-QPSO (Monte Carlo tree search and quantum particle swarm optimization), is proposed to bolster the AUV’s capacity in planning complex task decision control systems. This algorithm tackles the issue of the time-consuming manual design of control systems by effectively integrating BTs. By assessing a predefined set of subtasks and actions in tandem with the complex task scenario, a reward function is formulated for MCTS to pinpoint the optimal subtree set. The QPSO algorithm is then leveraged for subtree integration, treating it as an optimal path search problem from the root node to the leaf node. This process optimizes the search subtree, thereby enhancing the robustness and security of the control architecture. To expedite search speed and algorithm convergence, this paper recommends reducing the search space by pre-grouping conditions and states within the behavior tree. The efficacy and superiority of the proposed algorithm are validated through security and timeliness evaluations of the BT, along with comparisons with other algorithms for automatic AUV decision control behavior tree design. Ultimately, the effectiveness and superiority of the proposed algorithm are corroborated through simulations on a multi-AUV complex task platform, showcasing its practical applicability and efficiency in real-world underwater scenarios.

U-ATSS: A lightweight and accurate one-stage underwater object detection network

Due to the harsh and unknown marine environment and the limited diving ability of human beings, underwater robots become an important role in ocean exploration and development. However, the performance of underwater robots is limited by blurred images, low contrast and color deviation, which are resulted from complex underwater imaging environments. The existing mainstream object detection networks perform poorly when applied directly to underwater tasks. Although using a cascaded detector network can get high accuracy, the inference speed is too slow to apply to actual tasks. To address the above problems, this paper proposes a lightweight and accurate one-stage underwater object detection network, called U-ATSS. Firstly, we compressed the backbone of ATSS to significantly reduce the number of network parameters and improve the inference speed without losing the detection accuracy, to achieve lightweight and real-time performance of the underwater object detection network. Then, we propose a plug-and-play receptive field module F-ASPP, which can obtain larger receptive fields and richer spatial information, and optimize the learning rate strategy as well as classification loss function to significantly improve the detection accuracy and convergence speed. We evaluated and compared U-ATSS with other methods on the Kesci Underwater Object Detection Algorithm Competition dataset containing a variety of marine organisms. The experimental results show that U-ATSS not only has obvious lightweight characteristics, but also shows excellent performance and competitiveness in terms of detection accuracy.

Dual branch Transformer-CNN parametric filtering network for underwater image enhancement

Due to the absorption and scattering of light on the water surface, underwater images often face challenges such as low contrast, color deviation, insufficient exposure, and blurred details, exacerbating the difficulty of underwater tasks. In recent years, underwater image enhancement has become increasingly crucial in marine applications. Among existing underwater enhancement methods, the focus has often been on pixel-level learning, which may lead to image noise and an inability to finely adjust the image. In this paper, we propose a dual-branch Transformer-CNN Parameter Filtering network for underwater image enhancement, referred to as DTCPF. Specifically, to better aggregate window information, we introduce an overlapping window self-attention module to enhance interaction between windows. Additionally, we employ an improved Transformer encoder and decoder, utilizing long-distance attention and reversible neural networks to extract low-frequency and high-frequency information from the image. Moreover, we introduce a regression parameter filtering group for regression prediction, using the predicted parameters to enhance the image and obtain a reliable underwater enhancement model. Our approach undergoes qualitative and quantitative analyses on four real underwater datasets, demonstrating outstanding performance.

A laser field synchronous scanning imaging system for underwater long-range detection

A novel underwater optical imaging system is proposed to improve underwater image degradation, which is severely deteriorated due to absorption and scattering under artificial illumination. The light source employs a line laser with 520 nm wavelength and a mirror galvanometer that controls the one-dimension scanning of the laser beam to form a planar illumination light field. The camera adopts an sCMOS image sensor with an electronic rolling shutter exposure pattern, compressing the camera’s instantaneous field of view(IFOV) into a narrow strip by reducing the exposure time. Meanwhile, the system controls the light source to irradiate the IFOV of the camera only synchronously, which significantly compressed the overlapping volume of the light path between the camera and the light source so that the backscattered light captured by the camera was dramatically reduced. Experimental results at different turbidity and distances in pool environments show that the laser field synchronous scanning imaging system obtains excellent performance in contrast ratio (CR) and contrast signal-to-noise ratio (CSNR). In the turbidity experiment, the peak CR of the target image improved by a factor of 3.88, and the peak CSNR improved by a factor of 2.33 in 19.6 FTU. In the distance experiment, the peak CR of the target image improved by a factor of 5.57, and the peak CSNR improved by a factor of 1.67 at a distance of 13 m, achieving a clear imaging range of 15 m. This system substantially diminishes the impact of light backscattering on imaging quality, which effectively facilitates further development of the underwater task and renders the system highly applicable in underwater imaging.

Imitation learning from imperfect demonstrations for AUV path tracking and obstacle avoidance

Autonomous underwater vehicle (AUV) is widely used for complex underwater tasks such as seafloor exploration. In recent years, deep reinforcement learning (DRL) has been introduced to the AUV control due to its capability to improve the autonomy of AUV. However, it is usually very difficult to design an effective reward function for the DRL methods. Generative adversarial imitation learning (GAIL) can allow AUVs to learn control policies from expert demonstrations instead of pre-defined reward functions, but suffers from the deficiency of requiring optimal expert demonstrations and not surpassing the provided demonstrations. This paper builds upon the GAIL algorithm for AUV learning control policies from expert demonstrations. We proposed an importance reweighting generative adversarial imitation learning (WGAIL) algorithm by using confidence scores to indicate the optimality of the demonstrated trajectories, which can facilitate AUVs to learn control policies from expert demonstrations of different levels. Our experimental results on a simulated AUV system modeling Sailfish 210 of our lab in the Gazebo simulation environment show that an AUV trained via WGAIL can achieve a better performance than the one trained via GAIL with different levels of expert sub-optimal demonstrations. Moreover, control policies trained via WGAIL in simple tasks can generalize better to complex tasks than those trained via GAIL, greatly extending the applicability of the AUV learning from expert demonstrations.

Dual-Loop Integral Sliding Mode Control-Based Path Tracking with Reaction Torque for Autonomous Underwater Vehicle

Path tracking control is an important method for a Six-Degree-Of-Freedom Autonomous Underwater Vehicle to perform specific underwater tasks. Therefore, this paper investigates a dual-loop integral sliding mode control (DLISMC)-based tracking controller for an AUV with model uncertainties and external disturbances, and introduces a new reaction torque model for static compensation in order to improve the attitude control capability for AUVs when performing path tracking. In addition, the stability of tracking control law based on DLISMC is demonstrated using the Lyapunov function. Finally, numerical simulations are carried out on MATLAB 2016/Simulink and compared with the Proportion–Integral–Differential (PID) control commonly used in industry as well as the dual-loop Proportion–Integral–Differential (DLPID). Simulation results show that the DLISMC has a smaller tracking error, faster convergence speed, and more robustness against external disturbances and reaction torque.

Modelling and characteristics analysis of an electro-hydraulic proportional valve with a discrete pilot stage for underwater hydraulic manipulators

As the harshness of the underwater operating environment and complexity of underwater tasks gradually increase, underwater hydraulic manipulators with high reliability and precision attract more research attention. To improve the reliability and dynamic performance of the traditional electro-hydraulic proportional valve (EHPV) used in the underwater hydraulic manipulator, an improved electro-hydraulic proportional valve with a discrete pilot stage (DEHPV) is proposed. In the pilot stage, two 2-position 3-way normally open high speed switching valves (HSVs) are used, and the initial oil spring stiffness in the control chamber is large enough to maintain the dynamic performance of the DEHPV. First, the working principle and configuration of the DEHPV are both given, and the coupled electric-magnetic-mechanical-fluid model of the DEHPV is presented. Moreover, a dual-valve hybrid pulse width modulation (DVHPWM) control strategy is proposed to improve the position tracking accuracy of the DEHPV. Furthermore, a co-simulation model of the DEHPV considering the multi-field coupling law is established to study the dynamic characteristics. Extensive experiments are carried out to verify the effectiveness of the proposed structure as well as its good dynamic performance and control accuracy.

Collaborative Path Planning of Multiple AUVs Based on Adaptive Multi-Population PSO

Collaborative operations of multiple AUVs have been becoming increasingly popular and efficient in underwater tasks of marine applications. Autonomous navigation capability and cooperative control stability of multiple AUVs are crucial and challenging issues in underwater environments. To address the collaborative problem of path planning for multiple AUVs, this paper proposes an adaptive multi-population particle swarm optimization (AMP-PSO). In AMP-PSO, we design a grouping strategy of multi-population and an exchanging mechanism of particles between groups. We separate particles into one leader population and various follower populations according to their fitness. Firstly, in the grouping strategy, particles within the leader population are updated by both the leader population and follower populations so as to keep global optimization, while particles within the follower population are updated by their own group so as to keep local priority. Secondly, in the exchanging mechanism, particles are exchanged between the leader population and follower populations so as to improve multi-population diversity. To accommodate multi-population characteristics, an adaptive parameter configuration is also included to enhance the global search capability, convergence speed, and complex environment adaptability of AMP-PSO. In numerical experiments, we simulate various scenarios of collaborative path planning of multiple AUVs in an underwater environment. The simulation results convincingly demonstrate that AMP-PSO can obtain feasible and optimal path solutions compared to classic PSO and other improved PSO, which enable multiple AUVs to effectively achieve objectives under the conditions of collision avoidance and navigation constraint.

Target positioning of dual forward looking sonars based on orthogonal detection

Accurate positioning of underwater targets is an important basis for many underwater tasks. As an imagery equipment, forward-looking sonar (FLS) can overcome the problem of poor brightness in underwater environment, and has a longer detection distance than the camera, which plays an important role in oceanic target positioning. However, there remain two challenging problems: (1) due to the complex underwater environment and the propagation characteristics of sound waves in the water, there are a lot of noises in the FLS images, which impair the accuracy of positioning; (2) FLS can only obtain two-dimensional information, which is insufficient for target positioning. Aiming at these two problems, a three-dimensional underwater target positioning algorithm based on dual FLSs is proposed in this paper. Firstly, a Threshold-Mask-Guided Filter (TMGF) image denoising algorithm is designed to deal with the obvious background noises and same-pitch noises in FLS images, which greatly improves the denoising effect compared with the original guided filter algorithm. Secondly, a target positioning algorithm based on orthogonal vertical placement of dual sonars is proposed to calculate the three-dimensional position of the target. Finally, pool experiments and sea trial tests are carried out respectively to verify the effectiveness of TMGF algorithm in denoising and the accuracy and stability of positioning algorithm.

Neural Network Model-Based Reinforcement Learning Control for AUV 3-D Path Following

Autonomous underwater vehicles (AUVs) have become important tools in the ocean exploration and have drawn considerable attention. Precise control for AUVs is the prerequisite to effectively execute underwater tasks. However, the classical control methods such as model predictive control (MPC) rely heavily on the dynamics model of the controlled system which is difficult to obtain for AUVs. To address this issue, a new reinforcement learning (RL) framework for AUV path-following control is proposed in this paper. Specifically, we propose a novel actor-model-critic (AMC) architecture integrating a neural network model with the traditional actor-critic architecture. The neural network model is designed to learn the state transition function to explore the spatio-temporal change patterns of the AUV as well as the surrounding environment. Based on the AMC architecture, a RL-based controller agent named ModelPPO is constructed to control the AUV. With the required sailing speed achieved by a traditional proportional-integral (PI) controller, ModelPPO can control the rudder and elevator fins so that the AUV follows the desired path. Finally, a simulation platform is built to evaluate the performance of the proposed method that is compared with MPC and other RL-based methods. The obtained results demonstrate that the proposed method can achieve better performance than other methods, which demonstrate the great potential of the advanced artificial intelligence methods in solving the traditional motion control problems for intelligent vehicles.

Dynamic modeling and rotation control for flexible single-link underwater manipulator considering flowing water environment based on modified morison equation

Flexible single-link underwater manipulator (FSLUM) can be installed in autonomous underwater vehicles, it can carry out underwater tasks such as offshore oil and gas exploration, underwater cable maintenance as well as fishery farming and fishing. The design and development of the FSLUM are of great significance for ocean exploration and development. The FSLUM is affected by the underwater interference torque during underwater tasks in the flowing water environment, which will greatly affect the operational accuracy. In addition, many nonlinear factors such as the flexibility of the transmission system and the two-dimensional deformation of flexible links will aggravate the control difficulty of the FSLUM. An improved sliding mode control (SMC) strategy based on neural network identification is proposed to enhance the FSLUM's control accuracy. Neural networks are proposed to identify uncertain components including underwater interference and flexible nonlinear terms, so as to advance the control law design accuracy. Firstly, the FSLUM dynamic equations coupled with underwater interference torque considering multiple nonlinear factors are established according to the assumed mode method (AMM). A modified Morison equation is proposed to abbreviate the underwater interference torque in flowing water environments. Next, the control law of improved neural network sliding mode control (NNSMC) strategies are proposed according to the Lyapunov stability theorem, so as to ensure closed-loop stability of the FSLUM. Finally, the results of simulation and prototype tracking control experiments show that the improved NNSMC strategy has high tracking accuracy.

Adaptive neural network fixed-time sliding mode control for trajectory tracking of underwater vehicle

The effectiveness of Autonomous Underwater Vehicles (AUVs) in diverse underwater tasks is heavily reliant on their ability to perform accurate trajectory tracking. However, due to uncertainties in AUVs modeling and the complex underwater environment disturbances, designing effective trajectory tracking controllers and disturbance observers for AUVs is still a major challenge. To address these uncertainties and enable faster convergence of tracking errors, a trajectory-tracking controller based on fixed-time sliding mode control (FTSMC) and a Radial Basis function neural network (RBFNN) observer are used in this paper. In most cases, the AUV platform carries limited computational resources. In most cases, AUV platforms carry limited computational resources, which restricts the practical use of online neural network methods, and it is particularly important to reduce the complexity of computational neural networks and enhance the real-time performance of the observer. Therefore, we adopted a fast online weight update strategy based on a single parameter. Considering actuator faults and input saturation, passive fault-tolerant control (PFTC) is used in this scheme to further reduce the computational burden. Furthermore, the Lyapunov method is used to demonstrate the fixed-time stability of the individual signals of the system. Finally, simulation results and theoretical analysis demonstrate the superiority and effectiveness of the proposed method.