Development Of Autonomous Underwater Vehicle Research Articles

FPGA-Based Real-Time Synchronous Parallel System for Underwater Acoustic Positioning and Navigation

With the development of autonomous underwater vehicles, the research of underwater acoustic positioning and navigation technology is becoming increasingly important. However, most underwater positioning systems use the traditional acoustic response synchronization method, which leads to insufficient positioning real-time performance and refresh rate. To solve this problem, this paper proposes a real-time global synchronization mechanism with high-precision atomic clocks, which can realize one positioning through one-way propagation, so that the positioning efficiency is at least doubled. To further improve the positioning accuracy, an M-sequence-based phase offset modulation method is proposed to facilitate beacon identification. Consequently, a normalized signal matching capture algorithm against Doppler effect is designed, and the positioning solution is fused with the real-time sound ray bending compensation mechanism. Based on the above content, we developed an FPGA-based real-time synchronous parallel underwater positioning and navigation (RSPUPN) system, which uses FPGA+MCU architecture to accelerate signal processing operations in a low-power and parallel way. The test results in the South China Sea show that the operating range of the system can reach 8 km, the positioning refresh rate can reach 0.2 Hz, and the relative average position error is 0.25%, confirming the effectiveness of the designed system in industrial applications.

Identifying Key Issues in Integration of Autonomous Ships in Container Ports: A Machine-Learning-Based Systematic Literature Review

Background: Autonomous ships have the potential to increase operational efficiency and reduce carbon footprints through technology and innovation. However, there is no comprehensive literature review of all the different types of papers related to autonomous ships, especially with regard to their integration with ports. This paper takes a systematic review approach to extract and summarize the main topics related to autonomous ships in the fields of container shipping and port management. Methods: A machine learning method is used to extract the main topics from more than 2000 journal publications indexed in WoS and Scopus. Results: The research findings highlight key issues related to technology, cybersecurity, data governance, regulations, and legal frameworks, providing a different perspective compared to human manual reviews of papers. Conclusions: Our search results confirm several recommendations. First, from a technological perspective, it is advised to increase support for the research and development of autonomous underwater vehicles and unmanned aerial vehicles, establish safety standards, mandate testing of wave model evaluation systems, and promote international standardization. Second, from a cyber–physical systems perspective, efforts should be made to strengthen logistics and supply chains for autonomous ships, establish data governance protocols, enforce strict control over IoT device data, and strengthen cybersecurity measures. Third, from an environmental perspective, measures should be implemented to address the environmental impact of autonomous ships. This can be achieved by promoting international agreements from a global societal standpoint and clarifying the legal framework regarding liability in the event of accidents.

Autonomous underwater vehicle motion state recognition and control pattern mining

Self-awareness of its own state during autonomous operation is critical for Autonomous Underwater Vehicles (AUVs) to execute tasks and monitor their health. Automated data analysis methods appear to be the most appropriate tool for attempting to understand and elucidate the motion states of AUVs. However, research scholars often focus on the state recognition of small AUVs used for shallow water detection, and the relevant conclusions cannot be directly applied to deep-sea AUVs. As a representative deep-sea AUV, Qianlong-3 is studied in this paper. In this study, a data-driven approach was employed to recognize the motion states and control modes of the Qianlong-3 AUV while retaining the original data noise. First, the class imbalance problem in the data was addressed, and then the data features were enhanced through a sliding window statistical method. By comparing the differences in the accuracy of motion state recognition after class imbalance processing using different supervised learning methods, the Random Forest (RF) algorithm was found to be the most suitable classification algorithm. In addition, the Toeplitz Inverse Covariance-Based Clustering (TICC) algorithm was used to recognize and mine the motion states and control modes of the AUV. The TICC algorithm was effective in extracting latent information from the actual data. Finally, through correlation analysis, the three motion states of the AUV were discussed, and the control modes for each of these states were determined. Overall, this study demonstrates the feasibility of relevant algorithms in practical deep-sea applications for state recognition and control mode mining. This provides valuable reference and experience for the development of future deep-sea AUV.

Adaptive Scheduling MAC Protocol in Underwater Acoustic Broadcast Communications for AUV Formation

With the rapid development of autonomous underwater vehicles (AUVs) and the continuous improvement of marine exploration requirements, AUV formation has emerged as a promising technique for performing underwater tasks with greater flexibility, adaptability, and scalability. To achieve AUV formation control, underwater acoustic communication networks (UACNs) are widely used to support information exchange among AUVs. Considering broadcast communications in fully connected mobile UACNs, existing medium access control (MAC) protocols face severe packet collision risk or low reuse efficiency. To improve the timeliness and reliability of the information exchange among AUVs, we develop an efficient scheduling-based adaptive broadcasting MAC (AB-MAC) protocol. The AB-MAC protocol is incompletely centralized, which can be reflected in two aspects. First, to ensure the consistency of the transmission schedule in the network, the AB-MAC protocol selects a control node for each frame to schedule the transmission of all nodes according to the updated network topology. The slot lengths and transmission sequences are changeable in each frame to avoid packet collisions and minimize frame lengths. Second, to further improve the efficiency and robustness of the protocol, all nodes adaptively adjust their transmission time by integrating the information contained in their previously received control packets and ordinary packets. The transmission scheduling problem is formulated as a combinatorial optimization problem, which can be solved by our proposed improved Genetic Algorithm. Numerical results demonstrate that the AB-MAC protocol not only performs well in terms of network throughput and average update interval but also has a high level of robustness even in low-quality underwater acoustic channels.

Trajectory Tracking Control of Autonomous Underwater Vehicle Called PAST

The increasing development of Autonomous Underwater Vehicles (AUVs) with varying levels of autonomy can be seen globally. The paper is devoted to the new AUV called PAST being developed and built within the Polish development project entitled „Development of the underwater autonomous transport system” (no. POIR.01.01.01-00-0893/20). Mainly, the paper focuses on high-level control of PAST AUV devoted to controlling along the desired trajectory. It demands a set of tuned controllers of advanced velocity, course, depth, and trim. The trajectory tracking algorithm should be implemented and verified for different trajectories set in practice. At the beginning of the article, an introduction to the research is inserted. Then, the following scientific issues are presented: the mathematical model of AUV motion, high-level control structure, and the trajectory tracking algorithm. Next, the results of numerical research in the MATLAB environment are presented. In the end, the conclusions for future research are formulated.

DEVELOPMENT OF AUTONOMOUS UNDERWATER VEHICLE (AUV) BASED ON ROBOTIC OPERATING SYSTEM FOR FOLLOWING UNDERWATER CABLE

In addition to satellites, the Marine Cable Communication Channel (SKKL) which is located under the sea is also one of the backbones of the communication network to connect from one island to another. However, there are often irresponsible parties who damage or commit acts of vandalism. This action resulted in the disruption of the communication process on the submarine cable network. So, it is necessary to periodically check the condition of the underwater communication network cables. Regular checking of underwater cables is very risky, so we propose an underwater robot to handle it. This paper presents the development of Autonomous Underwater Vehicle (AUV) based on Robotic Operating System (ROS) for Following Underwater Cable to monitor the condition of the cables automatically. The AUV is equipped with a camera, Nvidia Jetson Nano, Arduino, Flight Controller, ESC, and brushless DC motors that used to assist the tracking process on the cable. The camera is used as the main visual sensor. Visual image processing methods are carried out using thresholding and contours detection methods, then the obtained data are processed to drive the motors on the AUV so that they can move on the direction of the cable. The experiment results show that the object detection method can be used under conditions with light intensity more than 25 lux. It works optimally at speeds of 0.27 m/s to 0.42 m/s. In the horizontal motion control test, the overshoot parameter value is ±60%, rise time is 2s, settling time is 16s, and steady state error is ±20%. The AUV can track on a straight and winding path of 2 meters with a bright light intensity of ±493 lux, a dim light of ±107 lux and a dark light intensity of ±25 lux with the help of an LED beam with a light intensity of ±773 lux. The percentage of success of scoping experiments on a straight track and a winding track with three trials is 75%. This performance shows that the developed AUV works well to follow underwater cable.

Review of Collision Avoidance and Path Planning Algorithms Used in Autonomous Underwater Vehicles

The rapid technological development of computing power and system operations today allows for increasingly advanced algorithm implementation, as well as path planning in real time. The objective of this article is to provide a structured review of simulations and practical implementations of collision-avoidance and path-planning algorithms in autonomous underwater vehicles (AUVs). The novelty of the review paper is to consider not only the results of numerical research but also the newest results of verifying collision-avoidance and path-planning algorithms in real applications together with a comparison of the difficulties encountered during simulations and their practical implementation. Analysing the last 20 years of AUV development, it can be seen that experiments in a real environment are dominated by classical methods. In the case of simulation studies, artificial intelligence (AI) methods are used as often as classical methods. In simulation studies, the APF approach is most often used among classical methods, whereas among AI algorithms reinforcement learning and fuzzy logic methods are used. For real applications, the most used approach is reactive behaviors, and AI algorithms are rarely used in real implementations. Finally, this article provides a general summary, future works, and a discussion of the limitations that inhibit the further development in this field.

Development of Autonomous Underwater Vehicle for Small-size Jellyfish Extermination and its Evaluation

Development of Autonomous Underwater Vehicle for Small-size Jellyfish Extermination and its Evaluation

THE BIOMIMETIC FIN PERFORMANCE EVALUATION FOR AN EFFICIENT AUTONOMOUS UNDERWATER VEHICLE (AUV) TO SUPPORT THE REVOLUTION IN MILITARY AFFAIRS (RMA) IN UNDERWATER DEFENSE

<div><p class="Els-history-head">The world is facing the global trend of Industry 4.0 in the manufacturing sectors. The concept of the Internet of Underwater Thing (IoUT) presents the trends in the underwater region. The Autonomous Underwater Vehicle (AUV) as a data collector and transmitter becomes the central component in the connected system of the IoUT. The efficiency of the platform is crucial to lengthening the range and duration of the mission. The biomimetic method of utilizing the caudal fin propulsion could enhance the efficiency of the small and low-speed AUV. From the defense perspective, there is a concept of Revolution in Military Affairs (RMA) that supports technological modernization for defense purposes. Technically, this study aims to combine the technical aspects of mechanical engineering and the defense concept of RMA in the technological advancement of AUV for the advanced and efficient defense strategy in the underwater region. The evaluations involved numerical simulation with Computational Fluid Dynamics (CFD) method. The simulation results show that the fully tapered flexible fin enhances the efficiency by 25%, while the narrow flexible fin enhances the efficiency by 30%. These results indicate that a flexible tapered fin should be the primary consideration in designing the high-efficiency biomimetic fin. The visualization of the force vectors shows that the flexibility of the fin acts as a thrust vectoring factor that directs more force vectors in the thrust direction. This study supports the RMA concept implementation by technological modernization, such as efficient biomimetic AUV development, to develop doctrines in the State's defense in the underwater region.</p></div>

The future of autonomous underwater vehicle control

The future of autonomous underwater vehicle control

A Multi-AUV Path Planning System Based on the Omni-Directional Sensing Ability

Following the development of autonomous underwater vehicles (AUVs), multiple trajectory-based submarine target information collection constitutes one of the key technologies that significantly influence underwater information collection ability and deployment efficiency. In this paper, we propose an underwater information collection AUV, O-AUV, that can perceive the omnidirectional area and could detect a larger area than the traditional AUV. A 3D sensing model for the O-AUV is proposed to describe the complex underwater information collection spaces. Thereafter, a cube-based environment model involving candidate observation point calculation methods are suggested to adapt the O-AUV model. A voyage cost map is also built according to the multi-AUV path planning for a common submarine mission that must traverse numerous mission targets in complex environments through the R-Dijkstra algorithm. Specifically, the voyage planning problem is solved through a critical algorithm called ANSGA (accelerated NSGA-II algorithm), which in turn, is developed by modifying the non-dominated sorting genetic algorithm (NSGA-II) to accelerate the optimization rate for the Pareto solution. Experiments are carried out in MATLAB, and the results verify the validity of the proposed O-AUV+ANSGA algorithm framework.

Autonomous underwater vehicle challenge: design and construction of a medium-sized, AI-enabled low-cost prototype

The design of an autonomous underwater vehicle (AUV) with physical dimensions of 1100 mm × 700 mm × 330 mm, and weight of 55 kg, is introduced herein. This paper describes the design, materials, hydrodynamics, and system architecture of an AUV prototype named Synoris, developed as a low-cost and medium-scale testbed platform. Synoris moves via six brushless motors, can reach up to 200 m depth, has an autonomy estimated around 6 hours and a modular design for multiple payload options. Stability control, autonomous movement, obstacle avoidance temperature/pressure sensing, and video/image capturing are simultaneously performed by exploiting a set of onboard computers that are described briefly in Section 4. The whole platform is built on top of the open source software called ROS (robotic operating system) that provides a flexible framework for writing robot software by providing services such as low-level device control, message parsing, data fusion, and system integration. Synoris is ideal for underwater applications and missions, involving machine learning and computer vision features. AUV development in general meets high-cost solutions due to the complexity and harshness of the operational environment. Even the most cost-effective solutions demand plentiful resources. This paper describes the entire process of development and how a relatively low-cost approach can provide a reliable AUV for many underwater applications, involving AI and machine-learning capabilities.

Study on battery thermal management of autonomous underwater vehicle by bionic wave channels with liquid cooling

As lithium-ion batteries (LIBs) are used in autonomous underwater vehicles (AUVs) power systems, their thermal safety and reliability have seriously inhibited the development of AUVs toward high speed and long voyage. However, the related research on AUVs' battery thermal management is very limited. In this article, a novel and efficient liquid cooling scheme is proposed, for battery thermal management of AUV, which adopts bionic wave cooling channels. A three-dimensional heat dissipation model of the battery was developed, which considered the influence of battery internal resistance and state of charge on battery heat generation. The effects of discharge rate, channel shape, coolant mass flow rate, coolant inlet direction, and dynamic operation strategy on the battery maximum temperature and temperature variation were investigated during discharging process. It was found that the battery with bionic wave channels was superior to the battery with straight channels, regarding cooling efficiency. The maximum temperature and temperature difference of the former decreased by 12.8 and 5.3 K compared with the latter at 5C current discharging rate. Based on the bionic wave channels, increasing the mass flow rate of coolant was the most efficient route to reducing the maximum temperature and improving the uniformity of the battery temperature. A mass flow rate equal to 2.5 × 10−4 kg s−1 is reasonably recommended by considering the cooling efficiency comprehensively. The direction of the coolant inlet had little effect on the maximum temperature of the battery, but it significantly improved the uniformity of the battery temperature. Under the premise of meeting the cruising requirements of AUVs, alternating low-rate and high-rate discharge was more conducive to improving the temperature behavior of the battery. The conclusions of this article can provide reference for the battery thermal management system of AUVs.

A Survey of Autonomous Underwater Vehicle Formation: Performance, Formation Control, and Communication Capability

Autonomous underwater vehicles (AUVs) are submersible underwater vehicles controlled by onboard computers. AUV formation is a cooperative control which focuses on controlling multiple AUVs to move in a group while executing tasks. In contrast to a single AUV, multi-AUV formation represents higher efficiency and better stability for many applications, such as oil and gas industries, hydrographic surveys, and military missions, etc. To achieve better formation, there are several key factors, including AUV performance, formation control, and communication capability. However, most studies in the field of AUV formation mainly focus on formation control methods. We observe that the research of communication capability and AUV performance of multiple AUV formation is still in an early stage. It is beneficial to researchers to present a comprehensive survey of the state of the art of AUV formation research and development. In this paper, we study AUV, formation control, and underwater acoustic communication capability in detail. We propose a classification framework with three dimensions, including AUV performance, formation control, and communication capability. This framework provides a comprehensive classification method for future AUV formation research. It also can be used to compare different methods and help engineers choose suitable formation methods for various applications. Moreover, our survey discusses formation architecture with communication constraints and we identify some common misconceptions and questionable research for formation control related to communication.

Basic Design for the development of Autonomous Underwater Vehicle

The artificial intelligence (AI) is used to automate the operation by taking a decision based on the situation or conditions. It is expected that around 2025, Robotics can move on earth similar to a human and support human society. Artificial Intelligence (AI) is in every field to automate the decision making intelligence to execute the desired operation. Understanding the need and importance of AUV for underwater operation, the authors have identified the need an AUV for the monitor, search and rescue operation. During the development unmanned vehicle, it was identified that the major task and key functionalities to be known to the researchers who want to contribute in this AI domain. The functionalities of AUV is decided by features which can be grouped into six categories as (i) Design of AUV shape (ii) Functionalities (iii) Design of Mechanical and Electrical system (iv) Control system (v) Navigation system (vi) Embedded system/software programming and (vii) Propulsion system. As a researcher, understanding each task or goal is very much needed and useful. Keeping the new researchers need, each task has been studied and highlighted the critical issue in each task which was faced in our development process. This paper can encourage many new researchers to do research in the upcoming domain of AUV.

A numerical study of target flow field detection by autonomous underwater vehicles

ABSTRACT Sideline is a key component of fish sensory system, which has been found to affect many behaviours of fish, including manoeuvring obstacle avoidance, prey tracking and environmental mapping in complex fluid environment. In this paper, based on the sensing flow field mechanism of fish lateral line system, a sphere obstacle model is established by using the CFD method and FLUENT software, and the flow field around it is simulated and calculated. The experiment controls two variables: water flow velocity and sphere size. In this paper, the influence of velocity and obstacle size on pressure distribution is analysed, and the rectangular coordinate system is established with the spherical centre as the origin, and the characteristics of pressure distribution in X and Y directions are summarised. This research plays an important role in promoting the development of autonomous underwater vehicles.

Simulation Study for Obstacle Avoidance of Autonomous Underwater Vehicles

Li, Y. and Zhang, M., 2020. Simulation study for obstacle avoidance of autonomous underwater vehicles. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 104–108. Coconut Creek (Florida), ISSN 0749-0208.Fish can track their prey and detect their surroundings in a complex water environment because of their unique underwater perception advantage. In this paper, on the basis of the sensing flow-field environment mechanism of the fish lateral line system, by analyzing the sonar system of an autonomous underwater vehicle (AUV) and the sensing principle of the fish lateral line system, the obstacle model of two shapes is established by using ANSYS software with computational fluid dynamics (CFD) analysis method. Two characteristic variables of different flow velocity and obstacle size are combined to simulate the flow field. The flow field of the fluid through different shape obstacles is analyzed, and the variation of the flow velocity to the flow field around the obstacle is compared, with the aim to establish a method to actively perceive and analyze the surrounding flow-field information through the characteristic parameters (velocity, size, shape) of the underwater obstacle itself. This paper provides a novel idea and practical sensing means for underwater detection technology, which can fill the void left by sonar and vision systems in a chaotic water environment. Improving the adaptive ability of AUV flow field and providing the theoretical basis for realizing the bionic lateral line engineering in the future, this research has an important impetus to the development of AUVs.

Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions

Development of Autonomous Underwater Vehicles (AUVs) has permitted the automatization of many tasks originally achieved with manned vehicles in underwater environments. Teams of AUVs designed to work within a common mission are opening the possibilities for new and more complex applications. In underwater environments, communication, localization, and navigation of AUVs are considered challenges due to the impossibility of relying on radio communications and global positioning systems. For a long time, acoustic systems have been the main approach for solving these challenges. However, they present their own shortcomings, which are more relevant for AUV teams. As a result, researchers have explored different alternatives. To summarize and analyze these alternatives, a review of the literature is presented in this paper. Finally, a summary of collaborative AUV teams and missions is also included, with the aim of analyzing their applicability, advantages, and limitations.

Deep Learning for on-board AUV Automatic Target Recognition for Optical and Acoustic imagery

In the widespread field of underwater robotics applications, the demand for increasingly intelligent vehicles is leading to the development of Autonomous Underwater Vehi-cles(AUVs) with the capability of understanding and engaging the surrounding environment. Consequently, to push the boundaries of cutting-edge smart AUVs, the automatic recognition of targets is becoming one of the most investigated topics and Deep Learning-based strategies have shown astonishing results. In the context of this work, two different neural network architectures, based on the Single Shot Multibox Detector (SSD) and on the Faster Region-based Convolutional Neural Network (Faster R-CNN), have been trained and validated, respectively, on optical and acoustic datasets. In particular, the models have been trained with the images acquired by FeelHippo AUV during the European Robotics League (ERL) competition, which took place in La Spezia, Italy, in July 2018. The proposed ATR strategy has then been validated with FeelHippo AUV in an on-board post-processing stage by exploiting the images provided by both a 2D Forward Looking Sonar (FLS) as well as an IP camera mounted on-board on the vehicle.

Development of Autonomous Underwater Vehicle for benthos sampling

Development of Autonomous Underwater Vehicle for benthos sampling