Autonomous Underwater Vehicle Technology Advances in Ocean Observation

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: closed (25 February 2023) | Viewed by 31769

Special Issue Editor

Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA
Interests: embedded systems; robotics; autonomous underwater vehicles; artificial intelligence techniques for autonomous vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, there is an ongoing pressing need for a sustained, persistent, and affordable presence in the oceans that will help us to understand and monitor key issues such as climate change, ocean acidification, unsustainable fishing, and pollution. Autonomous underwater vehicles (AUVs) play a valuable role in the maintenance and observation of our oceans to ensure safety to our most valuable natural resources. AUVs have emerged as a key enabling technology for addressing complex and challenging missions in ocean observation, which offer solutions that cannot be achieved through conventional methods. Characterized by rapid maneuverability, long operational range, and high personnel safety, AUVs with suitable sensory modules have great potential for ocean observation.

The use and deployment of AUVs in ocean observation have presented challenges in navigation, communication, and control. This Special Issue is dedicated to recent advances in AUV technology in ocean observation. We seek to publish the latest research in the following areas:

  • Vehicle design;
  • Vehicle navigation;
  • Vehicle control;
  • Mission planning and control;
  • Power management and control;
  • Communications management;
  • Multivehicle systems;
  • Vehicle modeling and simulation;
  • Policy and regulation.

Your contributions can address current and emerging research and development issues, approaches, techniques, or applications; community, state, and/or international initiatives; and other topics related to regional and global ocean observation, impacts, adaptation, and policy. 

Prof. Dr. Shuo Pang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AUV design
  • underwater navigation
  • AUV control
  • mission planning and control
  • power management
  • underwater communications
  • multivehicle systems
  • vehicle modeling and simulation
  • policy and regulation

Published Papers (18 papers)

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Research

16 pages, 36565 KiB  
Article
A Task Allocation Method for Multi-AUV Search and Rescue with Possible Target Area
by Chang Cai , Jianfeng Chen, Muhammad Saad Ayub and Fen Liu
J. Mar. Sci. Eng. 2023, 11(4), 804; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse11040804 - 10 Apr 2023
Cited by 3 | Viewed by 1256
Abstract
Task allocation is crucial for autonomous underwater vehicle (AUV) collaboration in multi-AUV maritime search and rescue missions. In real projects, there are possible target areas existing in task areas, which are not expected to be divided. Motivated by such a special situation, this [...] Read more.
Task allocation is crucial for autonomous underwater vehicle (AUV) collaboration in multi-AUV maritime search and rescue missions. In real projects, there are possible target areas existing in task areas, which are not expected to be divided. Motivated by such a special situation, this paper proposes an area partitioning method to allocate the task to multiple AUVs and maintain the possible target area as a whole. First, the spatial structure of the task area is defined by the spiked Morse decomposition, which divides the task area according to a set of angles. Then, we perform a variational transformation to determine the optimal angles using the AUV order. Next, a customized backtracking method is introduced to determine the optimal AUV order which divides the task area among the multiple AUVs without disturbing the possible target areas. The proposed methodology is validated under various challenging scenarios using a different number of AUVs. The empirical results show that the divided possible target areas and workload variance were superior to the comparison methods. This indicates that the proposed method can generate stable solutions that effectively reduce the segmentation of possible target areas and keep the workload of the multiple AUVs balanced. Full article
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19 pages, 14876 KiB  
Article
A Storage-Saving Quadtree-Based Multibeam Bathymetry Map Representation Method
by Zheng Cong, Teng Ma, Ye Li, Mingxiao Yuan, Yu Ling, Haohan Du, Chi Qi, Ziyuan Li, Shuo Xu and Qiang Zhang
J. Mar. Sci. Eng. 2023, 11(4), 709; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse11040709 - 25 Mar 2023
Cited by 1 | Viewed by 1279
Abstract
With the rapid advancement of the simultaneous localization and mapping (SLAM) technology, the collaboration of several autonomous underwater vehicles (AUVs) in large-scale seafloor imaging has become a trending topic. Electromagnetic waves are difficult to transfer underwater, the only viable method of communication is [...] Read more.
With the rapid advancement of the simultaneous localization and mapping (SLAM) technology, the collaboration of several autonomous underwater vehicles (AUVs) in large-scale seafloor imaging has become a trending topic. Electromagnetic waves are difficult to transfer underwater, the only viable method of communication is acoustic transmission, but its bandwidth is limited. Therefore, how to compress and process multibeam bathymetry maps so that AUVs can acquire maps gathered by other AUVs has become an important topic of study. This study presents a representation approach for multibeam bathymetry maps based on a quadtree structure. In comparison to the girding approach, the sparse pseudo-input Gaussian processes (SPGPs) method, and the octree-based method, the quadtree-based method suggested in this study preserves precision while compressing storage space. Experiments utilizing field data validate the performance of the proposed technique, and the method’s ability to compress storage space towards an AUV cooperative SLAM’s scenario. Full article
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18 pages, 2268 KiB  
Article
Self-Supervised Pre-Training Joint Framework: Assisting Lightweight Detection Network for Underwater Object Detection
by Zhuo Wang, Haojie Chen, Hongde Qin and Qin Chen
J. Mar. Sci. Eng. 2023, 11(3), 604; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse11030604 - 13 Mar 2023
Cited by 4 | Viewed by 1668
Abstract
In the computer vision field, underwater object detection has been a challenging task. Due to the attenuation of light in a medium and the scattering of light by suspended particles in water, underwater optical images often face the problems of color distortion and [...] Read more.
In the computer vision field, underwater object detection has been a challenging task. Due to the attenuation of light in a medium and the scattering of light by suspended particles in water, underwater optical images often face the problems of color distortion and target feature blurring, which greatly affect the detection accuracy of underwater object detection. Although deep learning-based algorithms have achieved state-of-the-art results in the field of object detection, most of them cannot be applied to practice because of the limited computing capacity of a low-power processor embedded in unmanned underwater vehicles. This paper proposes a lightweight underwater object detection network based on the YOLOX model called LUO-YOLOX. A novel weighted ghost-CSPDarknet and simplified PANet were used in LUO-YOLOX to reduce the parameters of the whole model. Moreover, aiming to solve the problems of color distortion and unclear features of targets in underwater images, this paper proposes an efficient self-supervised pre-training joint framework based on underwater auto-encoder transformation (UAET). After the end-to-end pre-training process with the self-supervised pre-training joint framework, the backbone of the object detection network can extract more essential and robust features from degradation images when retrained on underwater datasets. Numerous experiments on the URPC2021 and detecting underwater objects (DUO) datasets verify the performance of our proposed method. Our work can assist unmanned underwater vehicles to perform underwater object detection tasks more accurately. Full article
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17 pages, 3099 KiB  
Article
Discerning Discretization for Unmanned Underwater Vehicles DC Motor Control
by Jovan Menezes and Timothy Sands
J. Mar. Sci. Eng. 2023, 11(2), 436; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse11020436 - 16 Feb 2023
Cited by 6 | Viewed by 2128
Abstract
Discretization is the process of converting a continuous function or model or equation into discrete steps. In this work, learning and adaptive techniques are implemented to control DC motors that are used for actuating control surfaces of unmanned underwater vehicles. Adaptive control is [...] Read more.
Discretization is the process of converting a continuous function or model or equation into discrete steps. In this work, learning and adaptive techniques are implemented to control DC motors that are used for actuating control surfaces of unmanned underwater vehicles. Adaptive control is a strategy wherein the controller is designed to adapt the system with parameters that vary or are uncertain. Parameter estimation is the process of computing the parameters of a system using a model and measured data. Adaptive methods have been used in conjunction with different parameter estimation techniques. As opposed to the ubiquitous stochastic artificial intelligence approaches, very recently proposed deterministic artificial intelligence, a learning-based approach that uses the physics-defined process dynamics, is also applied to control the output of the DC motor to track a specified trajectory. This work goes further to evaluate the performance of the adaptive and learning techniques based on different discretization methods. The results are evaluated based on the absolute error mean between the output and the reference trajectory and the standard deviation of the error. The first-order hold method of discretization and surprisingly large sample time of seven-tenths of a second yields greater than sixty percent improvement over the results presented in the prequel literature. Full article
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20 pages, 3800 KiB  
Article
Autonomous Underwater Vehicle Based Chemical Plume Tracing via Deep Reinforcement Learning Methods
by Lingxiao Wang and Shuo Pang
J. Mar. Sci. Eng. 2023, 11(2), 366; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse11020366 - 06 Feb 2023
Viewed by 1185
Abstract
This article presents two new chemical plume tracing (CPT) algorithms for using on autonomous underwater vehicles (AUVs) to locate hydrothermal vents. We aim to design effective CPT navigation algorithms that direct AUVs to trace emitted hydrothermal plumes to the hydrothermal vent. Traditional CPT [...] Read more.
This article presents two new chemical plume tracing (CPT) algorithms for using on autonomous underwater vehicles (AUVs) to locate hydrothermal vents. We aim to design effective CPT navigation algorithms that direct AUVs to trace emitted hydrothermal plumes to the hydrothermal vent. Traditional CPT algorithms can be grouped into two categories, including bio-inspired and engineering-based methods, but they are limited by either search inefficiency in turbulent flow environments or high computational costs. To approach this problem, we design a new CPT algorithm by fusing traditional CPT methods. Specifically, two deep reinforcement learning (RL) algorithms, including double deep Q-network (DDQN) and deep deterministic policy gradient (DDPG), are employed to train a customized deep neural network that dynamically combines two traditional CPT algorithms during the search process. Simulation experiments show that both DDQN- and DDPG-based CPT algorithms achieve a high success rate (>90%) in either laminar or turbulent flow environments. Moreover, compared to traditional moth-inspired method, the averaged search time is improved by 67% for the DDQN- and 44% for the DDPG-based CPT algorithms in turbulent flow environments. Full article
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26 pages, 4077 KiB  
Article
Date-Driven Tracking Control via Fuzzy-State Observer for AUV under Uncertain Disturbance and Time-Delay
by Chengxi Wu, Yuewei Dai, Liang Shan and Zhiyu Zhu
J. Mar. Sci. Eng. 2023, 11(1), 207; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse11010207 - 12 Jan 2023
Cited by 2 | Viewed by 1223
Abstract
This paper focuses on developing a data-driven trajectory tracking control approach for autonomous underwater vehicles (AUV) under uncertain external disturbance and time-delay. A novel model-free adaptive predictive control (MFAPC) approach based on a fuzzy state observer (FSO) was designed to achieve high precision. [...] Read more.
This paper focuses on developing a data-driven trajectory tracking control approach for autonomous underwater vehicles (AUV) under uncertain external disturbance and time-delay. A novel model-free adaptive predictive control (MFAPC) approach based on a fuzzy state observer (FSO) was designed to achieve high precision. Concretely, the mathematical model of AUV motion was analyzed, and simplified via model decoupling, thus providing the model basis with an explicit physical explanation for the controller. Second, the MFAPC scheme for a multiple-inputs and multiple-outputs (MIMO) discrete time system was derived, that estimates system external disturbance. The controller can online estimate and predictive time-varying parameter pseudo-Jacobian matrix (PJM) to establish equivalent state space data-model for AUV motion system. Third, the Takagi–Sugeno (T–S) fuzzy model based state observer was designed to combine with the MFAPC scheme for the first time, which was used to online decline the state error generated by system uncertain time-delay. In addition, the stability of the proposed control scheme was analyzed. Finally, two trajectory tracking scenarios were designed to verify the effectiveness and robustness of the proposed FMFAPC scheme, and the simulations are implemented using the realistic parameters of T-SEA AUV. Full article
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29 pages, 7281 KiB  
Article
APSO-MPC and NTSMC Cascade Control of Fully-Actuated Autonomous Underwater Vehicle Trajectory Tracking Based on RBF-NN Compensator
by Han Bao, Haitao Zhu, Xinfei Li and Jing Liu
J. Mar. Sci. Eng. 2022, 10(12), 1867; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10121867 - 02 Dec 2022
Cited by 4 | Viewed by 1415
Abstract
In this paper, a model predictive control (MPC) method optimized by an adaptive particle swarm optimization (APSO) algorithm is proposed. Combined with non-singular terminal sliding mode control (NTSMC), the inner and outer double-closed-loop control system is constructed to solve the fully actuated autonomous [...] Read more.
In this paper, a model predictive control (MPC) method optimized by an adaptive particle swarm optimization (APSO) algorithm is proposed. Combined with non-singular terminal sliding mode control (NTSMC), the inner and outer double-closed-loop control system is constructed to solve the fully actuated autonomous underwater vehicle (AUV) dynamic trajectory tracking control problem. First, the outer loop controller generates the expected optimal velocity commands and passes them to the inner loop velocity controller, which generates the available control inputs to ensure the entire closed-loop trajectory tracking. In the controller design stage, system input and state constraints are effectively considered. After that, a compensator based on an adaptive radial basis function (RBF) neural network (NN) is designed to compensate for the model error and external sea state disturbances and to improve the control accuracy of the system. Then, the stability of the proposed controller is proved based on Lyapunov analysis. Finally, the dynamic trajectory tracking performance of an AUV with different sea state disturbances is verified by simulation, and the simulation results are compared with double-closed-loop PD control and cascade control of standard MPC based on PSO and SMC. The results show that the designed controller is effective and robust. Full article
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20 pages, 4479 KiB  
Article
Taking into Account the Eddy Density on Analysis of Underwater Glider Motion
by Shufeng Li, Xiu Cao, Wei Ma, Yan Liang and Dongyang Xue
J. Mar. Sci. Eng. 2022, 10(11), 1638; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10111638 - 03 Nov 2022
Cited by 2 | Viewed by 1095
Abstract
Mesoscale eddies play an important role in regulating the global ocean ecosystem and climate variability. However, few studies have been found to focus on the survey of the underwater gliders (UGs) motion performance inside mesoscale eddies. The dynamic model of an UG considering [...] Read more.
Mesoscale eddies play an important role in regulating the global ocean ecosystem and climate variability. However, few studies have been found to focus on the survey of the underwater gliders (UGs) motion performance inside mesoscale eddies. The dynamic model of an UG considering the eddy density is established to predict its motion performance inside an eddy. Ignoring the effect of vertical velocity inside the eddy on the motion of UG, the simulation results and experimental data are compared to verify the derived model. From the analysis of the motion performance, the vertical velocity is larger at 400∼940 m depth than that at a depth of 0∼400 m in the ascent. Considering the vertical structures of parameters within eddies, the climbing profiles are chosen as the available samples to capture an eddy better. The larger error caused by the eddy density mainly occurs near the depth of the thermocline. Moreover, there is a stronger influence of eddy density on the motion performance of the UG in the ascent than that in the descent. The results show the differences in the effect of the mesoscale eddy density on the motion performance of “Petrel II” UG in the descent and ascent, and they provide a sampling suggestion for the application of UGs in the mesoscale eddy observation. Full article
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15 pages, 2177 KiB  
Article
Dynamic Target Tracking of Autonomous Underwater Vehicle Based on Deep Reinforcement Learning
by Jiaxiang Shi, Jianer Fang, Qizhong Zhang, Qiuxuan Wu, Botao Zhang and Farong Gao
J. Mar. Sci. Eng. 2022, 10(10), 1406; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10101406 - 01 Oct 2022
Cited by 7 | Viewed by 1744
Abstract
Due to the unknown motion model and the complexity of the environment, the problem of target tracking for autonomous underwater vehicles (AUVs) became one of the major difficulties in model-based controllers. Therefore, the target tracking task of AUV is modeled as a Markov [...] Read more.
Due to the unknown motion model and the complexity of the environment, the problem of target tracking for autonomous underwater vehicles (AUVs) became one of the major difficulties in model-based controllers. Therefore, the target tracking task of AUV is modeled as a Markov decision process (MDP) with unknown state transition probabilities. Based on actor–critic framework and experience replay technique, a model-free reinforcement learning algorithm is proposed to realize the dynamic target tracking of AUVs. In order to improve the performance of the algorithm, an adaptive experience replay scheme is further proposed. Specifically, the proposed algorithm utilizes the experience replay buffer to store and disrupt the samples, so that the time series samples can be used for training the neural network. Then, the sample priority is arranged according to the temporal difference error, while the adaptive parameters are introduced in the sample priority calculation, thus improving the experience replay rules. The results confirm the quick and stable learning of the proposed algorithm, when tracking the dynamic targets in various motion states. Additionally, the results also demonstrate good control performance regarding both stability and computational complexity, thus indicating the effectiveness of the proposed algorithm in target tracking tasks. Full article
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17 pages, 3559 KiB  
Article
Robust Optimization Design for Path Planning of Bionic Robotic Fish in the Presence of Ocean Currents
by Qunhong Tian, Tao Wang, Yunxia Wang, Changjiang Li and Bing Liu
J. Mar. Sci. Eng. 2022, 10(8), 1109; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10081109 - 12 Aug 2022
Cited by 3 | Viewed by 1316
Abstract
The bionic robotic fish is one of the special autonomous underwater vehicles (AUV), whose path planning is crucial for many applications including underwater environment detection, archaeology, pipeline leak detection, and so on. However, the uncertain ocean currents increase the difficulty of path planning [...] Read more.
The bionic robotic fish is one of the special autonomous underwater vehicles (AUV), whose path planning is crucial for many applications including underwater environment detection, archaeology, pipeline leak detection, and so on. However, the uncertain ocean currents increase the difficulty of path planning for bionic robotic fish in practice. In this paper, the path energy consumption is selected as the objective function for path planning, path safety factor, and smoothness are considered as the constraint conditions. The kinematic model is established for bionic robotic fish and, considering the uncertainty of ocean currents, a “min-max” robust optimization problem is proposed in the light of the normal optimization model of path planning for bionic robotic fish. The co-evolutionary genetic algorithm is presented to solve the robust optimization problem with two populations; one population represents the solutions and the other represents the uncertain ocean currents. The objective of the proposed algorithm is to find a robust solution that has the best worst-case performance over a set of possible ocean currents. Multiple experiments indicate that the proposed algorithm is very effective for path planning for bionic robotic fish with ocean currents. Full article
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15 pages, 6762 KiB  
Article
Occupancy Grid-Based AUV SLAM Method with Forward-Looking Sonar
by Xiaokai Mu, Guan Yue, Nan Zhou and Congcong Chen
J. Mar. Sci. Eng. 2022, 10(8), 1056; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10081056 - 31 Jul 2022
Cited by 9 | Viewed by 2518
Abstract
Simultaneous localization and mapping (SLAM) is an active localization method for Autonomous Underwater Vehicle (AUV), and it can mainly be used in unknown and complex areas such as coastal water, harbors, and wharfs. This paper presents a practical occupancy grid-based method based on [...] Read more.
Simultaneous localization and mapping (SLAM) is an active localization method for Autonomous Underwater Vehicle (AUV), and it can mainly be used in unknown and complex areas such as coastal water, harbors, and wharfs. This paper presents a practical occupancy grid-based method based on forward-looking sonar for AUV. The algorithm uses an extended Kalman filter (EKF) to estimate the AUV motion states. First, the SLAM method fuses the data coming from the navigation sensors to predict the motion states. Subsequently, a novel particle swarm optimization genetic algorithm (PSO-GA) scan matching method is employed for matching the sonar scan data and grid map, and the matching pose would be used to correct the prediction states. Lastly, the estimated motion states and sonar scan data would be used to update the grid map. The experimental results based on the field data have validated that the proposed SLAM algorithm is adaptable to underwater conditions, and accurate enough to use for ocean engineering practical applications. Full article
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27 pages, 17887 KiB  
Article
Research on AUV Energy Saving 3D Path Planning with Mobility Constraints
by Guocheng Zhang, Jixiao Liu, Yushan Sun, Xiangrui Ran and Puxin Chai
J. Mar. Sci. Eng. 2022, 10(6), 821; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10060821 - 15 Jun 2022
Cited by 6 | Viewed by 2318
Abstract
This paper aims to focus on the path planning problem of AUV in the marine environment. As well as considering the path length and safe obstacle avoidance, ocean currents should not be ignored as the main factor affecting the navigation energy consumption of [...] Read more.
This paper aims to focus on the path planning problem of AUV in the marine environment. As well as considering the path length and safe obstacle avoidance, ocean currents should not be ignored as the main factor affecting the navigation energy consumption of AUV. At the same time, the path should satisfy the mobility constraint of AUV; otherwise, the path is inaccessible to AUV. For the above problems, this paper presents a path planning algorithm based on an improved particle swarm (EPA-PSO); the fitness function is designed based on path length, energy consumption, and mobility constraints. The updated law of particle velocity and the initialization law of particles are improved, and the possible optimal solutions are stored in the feasible solution set; finally, the optimal solutions are obtained by comparison. The local jumping ability is given to the particle swarm so that the particles can jump out of the local optimal solution. The path planning simulation experiment is compared with the traditional PSO algorithm. The results show that the EPA-PSO algorithm proposed in this paper can be used in the AUV three-dimensional path planning process. It can effectively save energy and make the navigation path of AUV satisfy the requirements of maneuverability. The field experiment was completed in Shanghai, China, and the experiment proved that it was feasible to obtain a path satisfying the maneuverability constraints with optimal energy consumption for the problems studied in this paper. Full article
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20 pages, 4509 KiB  
Article
Prediction-Based Region Tracking Control Scheme for Autonomous Underwater Vehicle
by Tu Lv, Mingjun Zhang and Yujia Wang
J. Mar. Sci. Eng. 2022, 10(6), 775; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10060775 - 03 Jun 2022
Cited by 2 | Viewed by 1145
Abstract
This paper addresses the region tracking control problem for an autonomous underwater vehicle (AUV) and proposes a prediction-based region tracking control (PRTC) scheme for AUV. In the PRTC scheme, the idea of prediction is adopted to solve the problems of overshoot and high [...] Read more.
This paper addresses the region tracking control problem for an autonomous underwater vehicle (AUV) and proposes a prediction-based region tracking control (PRTC) scheme for AUV. In the PRTC scheme, the idea of prediction is adopted to solve the problems of overshoot and high energy consumption due to the lack of consideration of the large inertia of the AUV in the traditional scheme. The PRTC scheme predicts the future position of AUV through the past time-series position of AUV and the outer boundary of the desired region, and then designs the controller depending on the predicted results. Furthermore, the relationship between the desired region and the control output of the proposed PRTC scheme is studied. It is found that its control output amplitude is susceptible to the desired region range, resulting in output saturation. Therefore, this paper proposes a control law optimization scheme considering the desired region. This optimization scheme modifies the error signal in the control law of the PRTC scheme so that it is only related to the relative position of the desired region where the AUV is located. Finally, the proposed schemes are applied on the ODIN AUV, and the simulation results verify the feasibility of the proposed schemes. Full article
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21 pages, 4499 KiB  
Article
Coordinated Formation Control of Discrete-Time Autonomous Underwater Vehicles under Alterable Communication Topology with Time-Varying Delay
by Haomiao Yu, Zhenfang Zeng and Chen Guo
J. Mar. Sci. Eng. 2022, 10(6), 712; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10060712 - 24 May 2022
Cited by 7 | Viewed by 2332
Abstract
This paper is concerned with the coordinated formation control problem of multiple autonomous underwater vehicles (AUVs) under alterable communication topology and time-varying delay in discrete time domain. Firstly, the multi-AUV system is divided into one leader and multiple followers, and the communication topology [...] Read more.
This paper is concerned with the coordinated formation control problem of multiple autonomous underwater vehicles (AUVs) under alterable communication topology and time-varying delay in discrete time domain. Firstly, the multi-AUV system is divided into one leader and multiple followers, and the communication topology is divided into two parts. The coupled nonlinear AUV model is linearized into a second-order integral model using state feedback. Secondly, two types of coordinated controllers in discrete time are proposed: the controller for multi-AUV system without delay, the controller for multi-AUV system with time-varying delay. Then, the formation control issue for multiple AUVs with alterable topology is treated as the asymptotic stability of an error system. The stability analysis of the error system consisting of the state errors between each follower and the leader is performed, to obtain some novel sufficient conditions for achieving the formation control objective. Finally, some simulation results are presented to demonstrate the effectiveness of the theoretical results, and the comparisons describe the effects of communication topology and delay on the performance of the control system. Full article
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17 pages, 5746 KiB  
Article
Infrared and Visible Image Fusion Methods for Unmanned Surface Vessels with Marine Applications
by Renran Zhang, Yumin Su, Yifan Li, Lei Zhang and Jiaxiang Feng
J. Mar. Sci. Eng. 2022, 10(5), 588; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10050588 - 26 Apr 2022
Cited by 3 | Viewed by 1620
Abstract
Infrared and visible image fusion is a very effective way to solve the degradation of sea images for unmanned surface vessels (USVs). Fused images with more clarity and information are useful for the visual system of USVs, especially in harsh marine environments. In [...] Read more.
Infrared and visible image fusion is a very effective way to solve the degradation of sea images for unmanned surface vessels (USVs). Fused images with more clarity and information are useful for the visual system of USVs, especially in harsh marine environments. In this work, three novel fusion strategies based on adaptive weight, cross bilateral filtering, and guided filtering are proposed to fuse the feature maps that are extracted from source images. First, the infrared and visible cameras equipped on the USV are calibrated using a self-designed calibration board. Then, pairs of images containing water scenes are aligned and used as experimental data. Finally, each proposed strategy is inserted into the neural network as a fusion layer to verify the improvements in quality of water surface images. Compared to existing methods, the proposed method based on adaptive weight provides a higher spatial resolution and, in most cases, less spectral distortion. The experimental results show that the visual quality of fused images obtained based on an adaptive weight strategy is superior compared to other strategies, while also providing an acceptable computational load. Full article
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13 pages, 2289 KiB  
Communication
Autonomous Water Sampler for Oil Spill Response
by Daniel Gomez-Ibanez, Amy L. Kukulya, Abhimanyu Belani, Robyn N. Conmy, Devi Sundaravadivelu and Lisa DiPinto
J. Mar. Sci. Eng. 2022, 10(4), 526; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10040526 - 11 Apr 2022
Cited by 3 | Viewed by 1902
Abstract
A newly developed water sampling system enables autonomous detection and sampling of underwater oil plumes. The Midwater Oil Sampler collects multiple 1-L samples of seawater when preset criteria are met. The sampler has a hydrocarbon-free sample path and can be configured with several [...] Read more.
A newly developed water sampling system enables autonomous detection and sampling of underwater oil plumes. The Midwater Oil Sampler collects multiple 1-L samples of seawater when preset criteria are met. The sampler has a hydrocarbon-free sample path and can be configured with several modules of six glass sample bottles. In August 2019, the sampler was deployed on an autonomous underwater vehicle and captured targeted water samples in natural oil seeps offshore Santa Barbara, CA, USA. Full article
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27 pages, 8255 KiB  
Article
Sliding Mode Motion Control for AUV with Dual-Observer Considering Thruster Uncertainty
by Yushan Sun, Puxin Chai, Guocheng Zhang, Tian Zhou and Haotian Zheng
J. Mar. Sci. Eng. 2022, 10(3), 349; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10030349 - 01 Mar 2022
Cited by 8 | Viewed by 1938
Abstract
In the motion control of AUVs, especially those driven by multiple thrusters, the thruster misalignment and thrust loss cause the actual force and moment applied to the AUV to deviate from that desired, making accurate and fast motion control difficult. This paper proposes [...] Read more.
In the motion control of AUVs, especially those driven by multiple thrusters, the thruster misalignment and thrust loss cause the actual force and moment applied to the AUV to deviate from that desired, making accurate and fast motion control difficult. This paper proposes a sliding mode control method with dual-observer estimation for the AUV 3D motion control problem in the presence of thruster misalignment uncertainty and thrust loss uncertainty. Firstly, this paper considers the force and moment deviation as disturbances that vary with the controller output, and proposes the TD disturbance observer to address the problem of deviation caused by uncertainty in thruster misalignment. Secondly, this paper introduces the dynamics equation of thrust loss and designs the gain disturbance observer to estimate the thrust loss uncertainty during AUV navigation. The designed controller, verified by simulation and field tests, ensures that the AUV maintains better motion control despite thruster misalignment and thrust loss. Full article
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20 pages, 6550 KiB  
Article
A Novel Method of Trajectory Optimization for Underwater Gliders Based on Dynamic Identification
by Ming Yang, Yanhui Wang, Yan Liang, Yu Song and Shaoqiong Yang
J. Mar. Sci. Eng. 2022, 10(3), 307; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse10030307 - 22 Feb 2022
Cited by 5 | Viewed by 1255
Abstract
For underwater gliders (UGs), high trajectory accuracy is an important factor in improving the observation of ocean phenomena. In this paper, a novel method of trajectory optimization is proposed to increase the trajectory accuracy of UGs, which is approximately based on the nonlinear [...] Read more.
For underwater gliders (UGs), high trajectory accuracy is an important factor in improving the observation of ocean phenomena. In this paper, a novel method of trajectory optimization is proposed to increase the trajectory accuracy of UGs, which is approximately based on the nonlinear dynamic model, rather than the linearization model. Firstly, a dynamic model of UGs is established to analyze the effect of the input parameters on the trajectory error, based on some approximate models that replaced the dynamic model due to its strong nonlinearity. Then, an identification strategy for the trajectory error is proposed, and the trajectory optimization strategy is analyzed while considering gliding range loss and observation distance loss. Finally, the identification strategy and trajectory optimization strategy proposed in this paper are verified by a sea trial of Petrel-L. The dynamic model, identification strategy, and optimization strategy are appropriate for other UGs. Full article
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