Directions of Development of the Autonomous Unmanned Underwater Vehicles. A Review

In this paper, in order to overcome certain limitations of previously commercialized platforms, a new integrated unmanned surface vehicle (USV) and unmanned underwater vehicle (UUV) platform connected via underwater cable capable of acquiring real-time underwater data and long-time operation are studied. A catamaran-type USV was designed to overcome the limitations of an ocean environment and to play the role as the hub of power supply and communication for the integrated platform. Meanwhile, the UUV was designed as torpedo-shaped to minimize hydrodynamic resistance and its hardware design was focused on processing and sending the underwater camera and sonar data. The underwater cable driven by a winch system was installed to supply power from the USV to the UUV and to transmit acquired data form underwater sonar sensor or camera. Different from other previously studied cooperation system of USVs and autonomous underwater vehicles (AUVs), the merit of the proposed system is real-time motion coordination control between the USV and UUV while transmitting large amount of data using the tether cable. The main focus of the study is coordination of the UUV with respect to the global positioning system (GPS) attached at USV and verification of its performance throughout field tests. Waypoint tracking control algorithm was designed and implemented on USV and relative heading, distance control for USV–UUV coordination was implemented to UUV. To ensure the integrity of the coordination control of the integrated platform, a study on accurate measurement system of the relative position between the USV and the UUV by using the GPS and the ultrashort baseline (USBL) device was performed. Individual tests were conducted to verify the performance of USBL and AHRS, which provide the position and heading data of UUV among the sensors mounted on the actual platform, and the effectiveness of the obtained sensor data is presented. Using the accurate measurement system, a number of field tests were conducted to verify the performance of the integrated platform.

The use of unmanned aerial vehicles (UAVs) for different applications has increased tremendously during the past decade. The small size, high maneuverability, ability to fly at predetermined coordinates, simple construction, and affordable price have made UAVs a popular choice for diverse aerial applications. However, the small size and the ability to fly close to the terrain make the detection and tracking of UAVs challenging. Similarly, unmanned underwater vehicles (UUVs) have revolutionized underwater operations. UUVs can accomplish numerous tasks that were not possible with manned underwater vehicles. In this survey paper, we provide features and capabilities expected from current and future UAVs and UUVs, and review potential challenges and threats due to use of such UAVs/UUVs. We also overview the countermeasures against such threats, including approaches for the detection, tracking, and classification of UAVs and UUVs.

The proposed technology provides subsea autonomous solutions using artificial intelligence and communication software. These integrate wirelessly between Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs). This is a significant improvement on the current pre-programmed mode of AUVs and the subsea communications and operation of autonomous robotics. Furthermore, the technology allows underwater wireless communication between autonomous subsea robotics and introduces new operational opportunities using simultaneous multi-robotic subsea arrays. Underwater vehicles are used for a wide variety of operations that include – but are not limited to inspection/identification, oceanography, survey missions or samples picking. Underwater vehicles may be manned or unmanned. Among the unmanned vehicles, there are ROVs and AUVs. An Autonomous Underwater Vehicle (AUV) is a robot that travels underwater without requiring input from an operator. AUVs constitute part of a larger group of undersea systems known as unmanned underwater vehicles, a classification that includes the mentioned non-autonomous Remotely Operated underwater Vehicles (ROVs) – controlled and powered from the surface by an operator/pilot via an umbilical or using remote control. ROVs are unmanned underwater vehicles connected to a base station, which may be a ship. As mentioned ROVs are connected to the ship by means of cables; this implies that the maximum achievable distance between the ROV and the base station is limited by the length of the cable. AUVs are unmanned underwater vehicles, which are connected to a docking station by means of a wireless communication. Typically, AUVs are propelled through the energy stored in batteries housed in their body. This means that the operative range of an AUV is limited by the capacity of the battery. This type of underwater vehicles has recently become an attractive alternative for underwater search and exploration since they are cheaper than manned vehicles. Over the past years, there have been abundant attempts to develop underwater vehicles to meet the challenge of exploration and extraction programs in the oceans. Recently, researchers have focused on the development of AUVs for long-term data collection in oceanography and coastal management. The oil and gas industry uses AUVs to make detailed maps of the seafloor before they start building subsea infrastructure; pipelines and sub-sea completions can be installed in the most cost effective manner with minimum disruption to the environment. In addition, post-lay pipe surveys are now possible, which includes pipeline inspection. The use of AUVs for pipeline inspection and inspection of underwater man-made structures is becoming more common. With the adoption of AUV technology becoming more widespread, the limitations of the 5 technology are being explored and addressed. The average AUV charge lasts about 24- hours on an underwater AUV, but sometimes it is necessary to deploy them for the kinds of several day missions that some unmanned systems are equipped to undertake. Like most robots, the unmanned mechanisms contain batteries that require regular recharging. Docking stations that communicate directly with underwater vehicles, guiding them to where they can recharge and transfer data have been developed. Any data the AUV has gathered, such as images of the seabed, could be uploaded to the docking station and transmitted to home base, which could direct new instructions to the robot any underwater vehicle requiring the need of a wireless communication with the docking station faces at least the problem of the limitations for wireless communications in water

The complete modeling and simulation of an unmanned vehicle with combined aerial and underwater ca- pabilities, called Hybrid Unmanned Aerial Underwater Vehicle (HUAUV), is presented in this paper. The best architecture for this kind of vehicle was evaluated based on the adaptation of typical platforms for aerial and underwater vehicles, to allow the navigation in both environments. The model selected was based on a quadrotor-like aerial platform, adapted to dive and move underwater. Kinematic and dynamic models are presented here, and the parameters for a small dimension prototype was estimated and simulated. Finally, controllers were used and validated in realistic simulation, including air and water navigation, and the environment transition problem. To the best of our knowledge, it is the first vehicle that is able to navigate in both environment without mechanical adaptation during the medium transitions. I. INTRODUCTION Nowadays, unmanned autonomous vehicles have been the focus of many development efforts, with a large range of applications. The amount of resources applied has improved their capabilities, especially in the military field. Remotely operated or autonomous Unmanned Aerial Vehicles (UAVs), for example, were used in recent military operations around the world (1). But they were also used in non-military activities, like agriculture (2) and surveillance (3). Another important robotic platform are the Unmanned Underwater Vehicles (UUVs), whose the most known are the Remotely Operated Vehicles (ROVs). This kind of vehicles can also be applied in several commercial field operations (4), e.g. oil and gas extraction in ultra deep waters (5). Both kind of vehicles are well adapted to work in their own environment (air and water, respectively), but some situations may require a single vehicle capable of working in both environment. Such requirement commonly appears when is necessary to perform maintenance on partially or fully submersing structures, as ship hull or risers. A typical approach includes using auxiliary vessels to transport ROVs that will make the inspection of offshore target regions. This problem is harder in partially submersed structures. In such situations, where the usage of auxiliary ships is difficult and expensive, underwater robots equipped with wheels or tracks are recommended.

In order to ensure the safety of the underwater unmanned vehicle (UUV) and reduce the risk of damage or loss during operation, the safety system will be equipped on the UUV. It can accurately locate the fault when the submersible encounters dangerous situations, such as equipment failure and cabin flooding, assess the dangerous situation and make emergency measures, so as to help the underwater unmanned submersible realize self-rescue. Fault diagnosis expert system is a knowledge-based fault diagnosis method, which is suitable for complex systems with incomplete knowledge. However, because the working environment of the underwater unmanned underwater vehicle is very complex and the fault types are diverse and change in real time, the traditional fault diagnosis expert system may not meet the response time requirements of the safety system due to the long response time required. Therefore, the finite state machine technology is applied to the diagnostic expert system to improve the response speed of the diagnostic expert system, and at the same time make the system more flexible and convenient to expand its functions. In addition, the model-based design method was applied to establish the model of the security system in stateflow, and the model verification was carried out. With the help of PLC Coder tool, the code of the target controller was generated and the algorithm was deployed.

This paper addresses the problem of developing an autonomous cooperative multi-vehicle system composed of one simple low-cost unmanned underwater vehicle (UUV) guided by a more capable, sensor-equipped, surface or underwater vehicle. Specifically, in this work we consider a system composed of an autonomous surface vehicle (ASV) able to localize a small-sized UUV via a multibeam sonar and, then, to guide it via an acoustic link to reach a target. The UUV is equipped with low-cost navigation sensors (a compass and a depth sensor), and to estimate its own position relies on the data received by the ASV (range and bearing from the ASV and ASV position). This paradigm was applied in the domain of mine countermeasures (MCM) to realize the NATO CMRE Autonomous Mine Neutralization System. In this system the low-cost feature of the UUV is of the most importance since the UUV is supposed to be expendable. In this approach, however, there may be cases in which the underwater vehicle cannot be detected by the sonar for extended time periods causing drifts in the UUV position estimate, potentially compromising the mission. This paper presents a set of behaviors to address this situation. The behaviors coordinate ASV circular search patterns together with sending to the UUV purposely generated navigation updates. The aim is to limit the distance between the two vehicles, increasing the probability of sonar reacquisition, and at the same time ensuring that the UUV progresses toward the target. Results from sea trials held in Elba island (Italy) during ANT12 experiments (in June 2012) are reported, and demonstrate the approach is effective and can push our system toward full autonomy.

Underwater natural gas pipelines constitute critical infrastructure for energy transportation. Any damage or leakage in these pipelines poses serious security risks, directly threatening marine and lake ecosystems, and potentially causing operational issues and economic losses in the energy supply chain. However, current methods for detecting deterioration and regularly inspecting these submerged pipelines remain limited, as they rely heavily on divers, which is both costly and inefficient. Due to these challenges, the use of unmanned underwater vehicles (UUVs) becomes crucial in this field, offering a more effective and reliable solution for pipeline monitoring and maintenance. In this study, we conducted an underwater pipeline tracking and damage detection experiment using a remote-controlled unmanned underwater vehicle (UUV) with autonomous features. The primary objective of this research is to demonstrate that UUV systems provide a more cost-effective, efficient, and practical alternative to traditional, more expensive methods for inspecting submerged natural gas pipelines. The experimental method included vehicle (UUV) setup, pre-test calibration, pipeline tracking mechanism, 3D navigation control, damage detection, data processing, and analysis. During the tracking of the underwater pipeline, damages were identified, and their locations were determined. The navigation information of the underwater vehicle, including orientation in the x, y, and z axes (roll, pitch, yaw) from a gyroscope integrated with a magnetic compass, speed and position information in three axes from an accelerometer, and the distance to the water surface from a pressure sensor, was integrated into the vehicle. Pre-tests determined the necessary pulse width modulation values for the vehicle’s thrusters, enabling autonomous operation by providing these values as input to the thruster motors. In this study, 3D movement was achieved by activating the vehicle’s vertical thruster to maintain a specific depth and applying equal force to the right and left thrusters for forward movement, while differential force was used to induce deviation angles. In pool experiments, the unmanned underwater vehicle autonomously tracked the pipeline as intended, identifying damages on the pipeline using images captured by the vehicle’s camera. The images for damage assessment were processed using a convolutional neural network (CNN) algorithm, a deep learning method. The position of the damage relative to the vehicle was estimated from the pixel dimensions of the identified damage. The location of the damage relative to its starting point was obtained by combining these two positional pieces of information from the vehicle’s navigation system. The damages in the underwater pipeline were successfully detected using the CNN algorithm. The training accuracy and validation accuracy of the CNN algorithm in detecting underwater pipeline damages were 94.4% and 92.87%, respectively. The autonomous underwater vehicle also followed the designated underwater pipeline route with high precision. The experiments showed that the underwater vehicle followed the pipeline path with an error of 0.072 m on the x-axis and 0.037 m on the y-axis. Object recognition and the automation of the unmanned underwater vehicle were implemented in the Python environment.

With the development of unmanned systems, automated launch and recovery of an Unmanned Underwater Vehicle (UUV) has become a bottleneck technology. Using an Unmanned Surface Vehicle (USV) to recover an UUV has become one of the direction of the future development of marine unmanned systems. At present, some research institutions have proposed the program using an USV recovering an UUV, but the maturity is not good. In order to achieve an USV autonomous recovering an UUV, this paper designs a recovery device for the USV to recover an UUV. The device is connected by rope to a winch fixed to the USV, with a V-shaped wing below for maintaining the stability of the device. By this device, the USV can launch and recovery the UUV autonomously. This paper first introduces the state of the art for the autonomous recovery, and then analyzes the design background, the structure and stability of recovery device was given. Finally, the dynamic process of an UUV underwater docking was simulated by using hydrodynamic software CFX, and the change of resistance when the recovery device was docked under different working conditions was obtained. The numerical simulation shows that when the UUV moves closer to the recovery device, the viscous resistance of the UUV gradually becomes larger. When the UUV approaches the recovery device, the pressure resistance rapidly decreases. However, the total resistance change of the UUV first increases and then decreases. In addition, the recovery device designed in this paper has good stability and has a certain guiding significance for the dynamic recovery of an UUV in the future.

In today’s times, unmanned and manned underwater vehicles are required to efficiently carry out shallow and deep-water ocean exploration, localization, inspection and intervention applications. Given the complexity and extreme challenges of deep underwater and real-time applications, unmanned underwater vehicles such as Autonomous Underwater Vehicles (AUV), Remotely Operated Vehicles (ROV) would be required to carry out the tasks [1]. However, AUVs and ROVs are extremely expensive. Hence, it is imperative to carry out extensive simulation-based analysis first [2]. In this paper, the performance of the underwater vehicle is analyzed by integrating Gazebo and ROS (Robot Operating System). The performance is analyzed over different trajectories such as circular, helical, and shortest path; and the position error and heading error are calculated for all trajectories.

We have designed and constructed a Real-Time Tracking Gradiometer (RTG) to be placed in an autonomous unmanned underwater vehicle (UUV) to detect and locate buried underwater mines. This system has been assembled and is now being tested by the United States Naval Surface Warfare Center in Panama City, FL (NSWC-PC). The UUV-RTG consists of fluxgate magnetic sensors that measure the magnetic field gradients caused by the presence of the target in the Earth's magnetic field. These magnetic gradients can then be used to detect and locate a target with the UUV in motion. The UUV-RTG control electronics is fully digital and is computer controlled. The data acquisition is also fully digital and provides 36 channels of data at 20-bit resolution per channel. The system is controlled by an embedded low-power processor platform. We will describe the system hardware, software, and provide details of the interface with the embedded platform. The integration of the RTG with the UUV and UUV-RTG performance details will be provided by NSWC-PC in a separate paper also to be presented at this conference (Allen et al., 2005).

An unmanned underwater vehicle (UUV) or autonomous underwater vehicle (AUV) is a marine robot [1] used for a wide range of oceanographic and military tasks including underwater surveys, inspection of submerged structures, tracking oceanographic features, undersea mapping, laying undersea cable, searching for downed aircraft, and finding naval sea mines, to name but a few. An unmanned surface vehicle (USV) is also categorized as a marine robot; however, most of this book is concerned with UUVs. In terms of physical shape, UUVs can be the traditional torpedo-shaped bodies as shown in Fig. 1 or an underwater glider such as that seen in Fig. 2. At the lowest level of control, UUVs operate with closed-loop control for basic actions such as maintaining depth, pitch, roll, and heading. As an extension to this capability, UUVs can also line-follow between a series of waypoints while logging data from mission sensors, as well as sensors that monitor the UUV status and functionality. Such basic actions can be grouped into behaviours.

Ocean thermal energy application technologies for unmanned underwater vehicles: A comprehensive review

Traditionally, unmanned underwater vehicles are operated at low angles of inclination (pitch and roll). However, there are tasks that require high maneuverability and controllability of the underwater vehicle in the entire range of orientation angles. At the same time, traditional control systems use the Euler angles and have limitations associated with the degeneration of the kinematic equations at a picth angle of ± 90 °, the problem of the non-uniqueness of the Euler angles, and the deterioration of the system performance at large inclinations of the vehicle. Thus, the question of developing a synthesis method for a control system for highly maneuverable unmanned underwater vehicles arises. The paper provides a review and comparative analysis of existing approaches to attitude control, on the basis of which an approach using quaternions is chosen. The law of attitude control based on quaternions was approved during field tests on a hybrid unmanned underwater vehicle "Iznos", developed at Bauman Moscow State Technical University. The results obtained during the tests are given in the article. The quaternion-based approach has better performance in comparison with the traditional control system. In addition, control system based on quaternion approach has a simple structure and can be used to increase the maneuverability of unmanned underwater vehicles with average requirements for positioning accuracy.

In this paper, an intelligent navigation system for an unmanned underwater vehicle powered by renewable energy and designed for shadow water inspection in missions of a long duration is proposed. The system is composed of an underwater vehicle, which tows a surface vehicle. The surface vehicle is a small boat with photovoltaic panels, a methanol fuel cell and communication equipment, which provides energy and communication to the underwater vehicle. The underwater vehicle has sensors to monitor the underwater environment such as sidescan sonar and a video camera in a flexible configuration and sensors to measure the physical and chemical parameters of water quality on predefined paths for long distances. The underwater vehicle implements a biologically inspired neural architecture for autonomous intelligent navigation. Navigation is carried out by integrating a kinematic adaptive neuro-controller for trajectory tracking and an obstacle avoidance adaptive neuro- controller. The autonomous underwater vehicle is capable of operating during long periods of observation and monitoring. This autonomous vehicle is a good tool for observing large areas of sea, since it operates for long periods of time due to the contribution of renewable energy. It correlates all sensor data for time and geodetic position. This vehicle has been used for monitoring the Mar Menor lagoon.

To satisfy the requirements of position control accuracy and allow unmanned underwater vehicle to maintain in the target zone with energy consumption as little as possible, this article proposes a flexible dynamic positioning strategy and control method, so as to extend the operating time of unmanned underwater vehicle dynamic positioning. Taking the distance between unmanned underwater vehicle and target point as the judgment condition for control method switching, this article delimits the working zone of different control levels for unmanned underwater vehicle and designs corresponding dynamic control methods for different working conditions. When unmanned underwater vehicle is far away from the target point, cuckoo search optimization method is proposed to plan the optimal motion scheme of energy consumption for the process of unmanned underwater vehicle reaching the target point. When unmanned underwater vehicle is close to the target positioning point, a flexible model predictive control method is proposed to reduce the energy consumption of unmanned underwater vehicle in the process of dynamic position control. The simulation case is designed to verify the flexible dynamic control capability of monomer unmanned underwater vehicle. The experimental results show that the strategy and control method proposed in this article, compared with the traditional proportion integration differentiation control method, could obtain the same control effect and reduce energy consumption, to achieve the purpose of prolonging the unmanned underwater vehicle operating time.