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Open AccessArticle

A Hybrid Excitation Model Based Lightweight Siamese Network for Underwater Vehicle Object Tracking Missions

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J. Mar. Sci. Eng. 2023, 11(6), 1127; https://doi.org/10.3390/jmse11061127 - 26 May 2023

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Abstract

Performing object tracking tasks and efficiently perceiving the underwater environment in real time for underwater vehicles is a challenging task due to the complex nature of the underwater environment. A hybrid excitation model based lightweight Siamese network is proposed to solve the mismatch [...] Read more.

Performing object tracking tasks and efficiently perceiving the underwater environment in real time for underwater vehicles is a challenging task due to the complex nature of the underwater environment. A hybrid excitation model based lightweight Siamese network is proposed to solve the mismatch between underwater objects with limited characteristics and complex deep learning models. The lightweight neural network is applied to the residual network in the Siamese network to reduce the computational complexity and cost of the model while constructing a deeper network. In addition, to deal with the changeable complex underwater environment and consider the timing of video tracking, the global excitation model (HE module) is introduced. The model adopts the excitation methods of space, channel, and motion to improve the accuracy of the algorithm. Based on the designed underwater vehicle, the underwater target tracking and target grabbing experiments are carried out, and the experimental results show that the proposed tracking algorithm has a high tracking success rate. Full article

(This article belongs to the Special Issue Advances in Underwater Robots for Intervention)

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Open AccessArticle

Model Predictive Controller Design Based on Residual Model Trained by Gaussian Process for Robots

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Changjie Wu

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Xiaolong Tang

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Xiaoyan Xu

J. Mar. Sci. Eng. 2023, 11(5), 893; https://doi.org/10.3390/jmse11050893 - 22 Apr 2023

Viewed by 450

Abstract

Model mismatch is inevitable in robot control due to the presence of unknown dynamics and unknown perturbations. Traditional model predictive control algorithms are usually based on constant value assumptions and are not able to overcome the degradation of controller performance due to model [...] Read more.

Model mismatch is inevitable in robot control due to the presence of unknown dynamics and unknown perturbations. Traditional model predictive control algorithms are usually based on constant value assumptions and are not able to overcome the degradation of controller performance due to model mismatch. In this paper, a model predictive control (MPC) algorithm based on Gaussian process regression (GPR) is proposed. Firstly, the kinematic equations of the mobile robot are established by the mechanistic analysis method; similarly, the dynamics of the mobile robot system are modeled using the second-class Lagrangian equations. Secondly, the problem of stability and reliability degradation due to model mismatch during the operation of mobile robot is considered. This paper uses a MPC algorithm with a main model plus residual model to solve the MPC closed-loop control strategy. The state at each moment is decomposed into a predicted state based on the first-principles model and a residual state. The residual state is learned by GPR in real-time and used to compensate for deviations between the real process model and the predicted model. The proposed method requires fewer data samples, enhancing the technique’s practicality. Finally, the simulation results show that the proposed algorithm is more stable and achieves the desired tracking faster. Compared with the MPC algorithm, the arrival time of the system is reduced by 28% and the speed error is controlled within 0.07. Full article

(This article belongs to the Special Issue Advances in Underwater Robots for Intervention)

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Open AccessArticle

Autonomous Heading Planning and Control Method of Unmanned Underwater Vehicles for Tunnel Detection

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J. Mar. Sci. Eng. 2023, 11(4), 740; https://doi.org/10.3390/jmse11040740 - 29 Mar 2023

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Abstract

To address the challenge of unmanned underwater vehicle (UUV) autonomous navigation in long-distance underwater tunnel detection tasks and improve the control performance of its heading control system, a method of autonomous heading planning and control based on sonar-ranging feedback control was proposed. This [...] Read more.

To address the challenge of unmanned underwater vehicle (UUV) autonomous navigation in long-distance underwater tunnel detection tasks and improve the control performance of its heading control system, a method of autonomous heading planning and control based on sonar-ranging feedback control was proposed. This method combines UUV’s autonomous heading planning technology with the heading proportion-integral-derivative (PID) control algorithm, optimizing the acquisition method of controller input data, to impart specific adaptive characteristics to the controller. Using the ranging principle of ultrasonic spontaneous self-collection, it is possible to obtain the yaw direction and angle of the vehicle relative to the target heading in the tunnel and continuously adjust the control law to change the heading as the vehicle’s heading status changes during navigation. The effectiveness of the autonomous heading planning and control method is verified through pool experiments. The analysis and experimental results show that the proposed heading planning method achieves good control effect in UUV’s underwater tunnel detection heading control, and exhibits obvious advantages in long-distance closed tunnel environments. UUV can adaptively adjust the heading according to the tunnel environment and has a fast response and strong applicability in planning and controlling the heading. Full article

(This article belongs to the Special Issue Advances in Underwater Robots for Intervention)

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Open AccessArticle

Research on Underwater Image Restoration Technology Based on Multi-Domain Translation

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Teng Xue

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Tianchi Zhang

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Jing Zhang

J. Mar. Sci. Eng. 2023, 11(3), 674; https://doi.org/10.3390/jmse11030674 - 22 Mar 2023

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Abstract

Underwater images are crucial in various underwater applications, including marine engineering, underwater robotics, and subsea coral farming. However, obtaining paired data for these images is challenging due to factors such as light absorption and scattering, suspended particles in the water, and camera angles. [...] Read more.

Underwater images are crucial in various underwater applications, including marine engineering, underwater robotics, and subsea coral farming. However, obtaining paired data for these images is challenging due to factors such as light absorption and scattering, suspended particles in the water, and camera angles. Underwater image recovery algorithms typically use real unpaired dataset or synthetic paired dataset. However, they often encounter image quality issues and noise labeling problems that can affect algorithm performance. To address these challenges and further improve the quality of underwater image restoration, this work proposes a multi-domain translation method based on domain partitioning. Firstly, this paper proposes an improved confidence estimation algorithm, which uses the number of times a sample is correctly predicted in a continuous period as a confidence estimate. The confidence value estimates are sorted and compared with the real probability to continuously optimize the confidence estimation and improve the classification performance of the algorithm. Secondly, a U-net structure is used to construct the underwater image restoration network, which can learn the relationship between the two domains. The discriminator uses full convolution to improve the performance of the discriminator by outputting the true and false images along with the category to which the true image belongs. Finally, the improved confidence estimation algorithm is combined with the discriminator in the image restoration network to invert the labels for images with low confidence values in the clean domain as images in the degraded domain. The next step of image restoration is then performed based on the new dataset that is divided. In this way, the multi-domain conversion of underwater images is achieved, which helps in the recovery of underwater images. Experimental results show that the proposed method effectively improves the quality and quantity of the images. Full article

(This article belongs to the Special Issue Advances in Underwater Robots for Intervention)

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Open AccessArticle

Development and Control of an Innovative Underwater Vehicle Manipulator System

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Xinhui Zheng

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Qiyan Tian

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Qifeng Zhang

J. Mar. Sci. Eng. 2023, 11(3), 548; https://doi.org/10.3390/jmse11030548 - 03 Mar 2023

Cited by 1 | Viewed by 761

Abstract

Recently, as humans have become increasingly interested in ocean resources, underwater vehicle-manipulator systems (UVMSs) have played an increasingly important role in ocean exploitation. To realize precise operation in underwater narrow spaces, the fly arm underwater vehicle manipulator system (FAUVMS) is proposed with manipulators [...] Read more.

Recently, as humans have become increasingly interested in ocean resources, underwater vehicle-manipulator systems (UVMSs) have played an increasingly important role in ocean exploitation. To realize precise operation in underwater narrow spaces, the fly arm underwater vehicle manipulator system (FAUVMS) is proposed with manipulators as its core. However, this system suffers severe dynamic coupling effects due to the combination of small vehicle and big manipulators. To resolve this issue, we propose a robust adaptive controller that contains two parts. In the first part, the extended Kalman filter (EKF) is designed to estimate the system states and predicts external disturbances to achieve adaptive control. In the second part, a chattering-free sliding mode control (SMC) is designed to converge the tracking errors to zero, thus guaranteeing the robustness of the controller. We constructed the simulation platform based on the geometric model of FAUVMS, and various simulations are carried out under different situations. Compared to the traditional methods, the proposed method has a faster convergent speed, a better robustness and adaptiveness to external disturbances, and the tracking errors of positions of the vehicle and each end-effector are much smaller. Full article

(This article belongs to the Special Issue Advances in Underwater Robots for Intervention)

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Open AccessArticle

Modeling and Adaptive Boundary Robust Control of Active Heave Compensation Systems

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Rui Du

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Naige Wang

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Hangyu Rao

J. Mar. Sci. Eng. 2023, 11(3), 484; https://doi.org/10.3390/jmse11030484 - 23 Feb 2023

Viewed by 711

Abstract

Heave compensation systems are essential for operations’ safety, reliability, and efficiency in harsh offshore environments. This paper investigates the vibration suppression problem of a type of deep-sea robot with the length of time variation and harsh operating environments for active heave compensation systems, [...] Read more.

Heave compensation systems are essential for operations’ safety, reliability, and efficiency in harsh offshore environments. This paper investigates the vibration suppression problem of a type of deep-sea robot with the length of time variation and harsh operating environments for active heave compensation systems, where hydraulic heave compensators implement actuators with input nonlinearity, model coupling, and unknown nonlinear disturbances. A robust adaptive output feedback control scheme based on the backstepping control method is designed to eliminate deep-ocean robot vibration, where the adaptive law handles the system parameter uncertainty. Meanwhile, a nonlinear disturbance observer (NDO) is introduced to overcome the effects of random disturbances and model coupling. In addition, the stability of the whole system is proved according to Lyapunov’s theory, and the scheme is shown to be feasible by theoretical analysis. Finally, a comparative simulation study was conducted to validate the effectiveness of the proposed controller. Full article

(This article belongs to the Special Issue Advances in Underwater Robots for Intervention)

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Open AccessArticle

Underwater Optical Image Restoration Method for Natural/Artificial Light

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J. Mar. Sci. Eng. 2023, 11(3), 470; https://doi.org/10.3390/jmse11030470 - 22 Feb 2023

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Abstract

This paper investigates the underwater optical image restoration method under the background of underwater target detection based on optical vision in AUVs. The light source used for AUV detection is different when the AUV operates in different depths. The natural light source is [...] Read more.

This paper investigates the underwater optical image restoration method under the background of underwater target detection based on optical vision in AUVs. The light source used for AUV detection is different when the AUV operates in different depths. The natural light source is used in shallow water and the artificial light source is used in deep water. This paper investigates underwater optical image restoration in these two light conditions. Aiming at the problem of image blurring in underwater optical images, the traditional underwater image restoration method based on scattering model can obtain satisfactory image restoration performance in natural light conditions. However, it cannot obtain the same image restoration result in artificial light conditions. To solve this problem, this paper presents an improved underwater optical image restoration method based on the scattering model. The scattering model and power spectrum are used to solve the initial parameters of the filter, and the parameters are optimized based on an evaluation index. The index of image definition is introduced to evaluate the restoration performance and to achieve the satisfactory image restoration result in both natural light and artificial light conditions. The effectiveness of the presented method is verified by experiments. Full article

(This article belongs to the Special Issue Advances in Underwater Robots for Intervention)

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Open AccessArticle

Fixed-Time Average Consensus of a Dynamic Event-Triggered Mechanism in a System of Multiple Underactuated Autonomous Underwater Vehicles Based on a Body Frame Spherical Coordinate System

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J. Mar. Sci. Eng. 2023, 11(2), 385; https://doi.org/10.3390/jmse11020385 - 09 Feb 2023

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Abstract

This paper is concerned with the consensus of a system involving multiple underactuated autonomous underwater vehicles (AUVs). Combined with a dynamic event-triggered mechanism and a fixed-time stability theorem, the backstepping average consensus controllers are designed. Firstly, the new consensus control objective on the [...] Read more.

This paper is concerned with the consensus of a system involving multiple underactuated autonomous underwater vehicles (AUVs). Combined with a dynamic event-triggered mechanism and a fixed-time stability theorem, the backstepping average consensus controllers are designed. Firstly, the new consensus control objective on the system for multiple underactuated AUVs in a body frame (BF) spherical coordinate system is proposed, and the tracking error kinematic equations are established based on the kinematic characteristics of the underactuated AUV. The fixed-time consensus controller is designed by the backstepping method, and the average consensus theorem is proposed to improve the Lyapunov function. Furthermore, the dynamic event-triggered mechanism is adopted to reduce the communication requirements and energy consumption. This is the first solution to the problem of a consensus controller design for a system of multiple underactuated AUVs. Finally, numerical simulation results demonstrate that the proposed method has superior effectiveness over alternatives. Full article

(This article belongs to the Special Issue Advances in Underwater Robots for Intervention)

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