Autonomous Underwater Vehicle (AUV)

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Optimal Control Of Autonomous Underwater Vehicle (AUV) Using Genetic Algorithms

Optimal Control Of Autonomous Underwater Vehicle (AUV) Using Genetic Algorithms

Autonomous Underwater Vehicle (AUV) is a type of underwater robotic device which can drive through the underwater propulsion system without any human controls. It is self-piloted where it is using the feedback received from the surrounding in order to determine its actions and movement during operation.

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Robust-save energy controller on an autonomous underwater vehicle with obstacles avoidance

Robust-save energy controller on an autonomous underwater vehicle with obstacles avoidance

Autonomous Underwater Vehicle (AUV) is developed with the aim to diminish the complexity of undersea project due to the health issue of human involved in a long term undersea mission 1 . A trajectory-tracking, such as to detect a damage pipe, is an example on the use of an AUV. Developing an advance robust controller for a trajectory-tracking mission is a challenging task as the nonlinearity of underwater environment 2 . Beside unpredictable perturbations which enable to move the AUV from desired position and increase the use of energy consumption, the problem comes out when some obstacles are appeared in the middle of prescribed trajectory 3 . Thus, it is a challenging task to develop an advance robust controller with a save energy control and obstacles avoidance technique.
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Autonomous Underwater Vehicle to Inspect Hydroelectric Dams

Autonomous Underwater Vehicle to Inspect Hydroelectric Dams

of an autonomous underwater vehicle (AUV) with capacity of au- tonomous locomotion, to perform monitoring of the physical struc- tures of dams and capture information about the local biota based on mechatronic project using dimensioning structural elements and machinery and elaborating the sensory part, which includes naviga- tion sensors and sensors of conditions, as well as its computational vision system to detect and measure cracks on hydroelectric dams. This paper is organized as follows: Section 2 presents an overview of the physical structure of the vehicle. Details of the electronic systems, instrumentation and hardware systems are shown on sec- tion 3. The description of the Robot Vision module is presented on Section 4. The experiments performed and results are discussed and analyzed in Section 5. Finally, conclusions and future proposals are fulfilled in Section 6.
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Three-Dimensional Path Tracking Control of Autonomous Underwater Vehicle Based on Deep Reinforcement Learning

Three-Dimensional Path Tracking Control of Autonomous Underwater Vehicle Based on Deep Reinforcement Learning

Abstract: In this paper, the three-dimensional (3D) path tracking control of an autonomous underwater vehicle (AUV) under the action of sea currents was researched. A novel reward function was proposed to improve learning ability and a disturbance observer was developed to observe the disturbance caused by currents. Based on existing models, the dynamic and kinematic models of the AUV were established. Deep Deterministic Policy Gradient, a deep reinforcement learning, was employed for designing the path tracking controller. Compared with the backstepping sliding mode controller, the controller proposed in this article showed excellent performance, at least in the particular study developed in this article. The improved reward function and the disturbance observer were also found to work well with improving path tracking performance.
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Resource investigation for Kichiji rockfish by autonomous underwater vehicle in Kitami-Yamato bank off Northern Japan

Resource investigation for Kichiji rockfish by autonomous underwater vehicle in Kitami-Yamato bank off Northern Japan

investigation method by the trawl for the fish is difficult to survey on rough terrain and need for big support of the ship. This paper proposes resource investigation method for kichiji rockfish using autonomous underwater vehicle (AUV) Tuna-Sand, and image processing method for precise measurement of the fish length. The AUV Tuna-Sand was developed for survey of material and energy resources in deep-sea such, and can observe natural seafloor automatically using only mounted sensors and devices. Our image processing makes a photograph possible to measure accurately the fish length by color correction for removing the unevenness of the brightness and distortion correction.
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Study of underwater thruster (UT) front cover of msi300 
		Autonomous 
		Underwater Vehicle (AUV) using finite element analysis (FEA)

Study of underwater thruster (UT) front cover of msi300 Autonomous Underwater Vehicle (AUV) using finite element analysis (FEA)

The Ocean’s living resources are a treasure for current and future generations of humankind. To sustain the valuable resources, the scientists start to develop unmanned underwater vehicles such as Remotely Operated Vehicle (ROV) and Autonomous Underwater Vehicle (AUV) to seabed mapping and sampling. This underwater vehicle propelled by underwater thrusters (UT), which consists of electric motor and propeller fix at the shaft. However, most of the available UT is not specifically meet the requirement such as the size and the power output. A new UT for an AUV has been designed to suit in. The study focused on new design front cover which is one of most important component in UT and using Aluminum 6061-T6 as material. Finite element analysis on the front cover of the UT reveals that it can withstand the pressure up to 1000 meter operating depth. Another crucial part need to be investigated is the gap between shaft and front cover. It was found that the gap needs to be increased from preliminary design 0.005 mm to 0.008 due to deflection occurred in most critical area is 0.0073 mm. It is important to determine this gap in order to avoid the water leak into the thruster if the gap too big or the shaft contacted the casing if the gap too small.
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Design And Development Of An Autonomous Underwater Vehicle (AUV)

Design And Development Of An Autonomous Underwater Vehicle (AUV)

Generally, Unmanned Underwater Vehicles (UUV) have been designed and developed in various countries, including Malaysia for the past ten years in marine technology. It can be categorized into two forms which are Remotely Operated Vehicle (ROV) and Autonomous Underwater Vehicle (AUV) with the almost the same function as shown in Figure 1.1. Figure 1.2 shows the classification of UUV. AUV is basically an extension of the ROV's technology. The ROV is controlled by the human from the controller and needs navigation control on the surface of the core ship, whereas the AUV is controlled by its on-board controller guided by build-in pre-programmed instructions with free from a chain [1-2]. The common AUV has a submarine or torpedo shape with a minimum of one thruster for forward, backward and rudders adjust left and right direction.
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Fuzzy Logic Controller design for 
		autonomous underwater vehicle (AUV) yaw control

Fuzzy Logic Controller design for autonomous underwater vehicle (AUV) yaw control

Unmanned underwater vehicles (UUV) have been design and developed in many countries including Malaysia for the past ten years in marine technology. Generally, it’s categorized into two types which are Autonomous Underwater Vehicle (AUV) and Remotely Operated Vehicle (ROV) with the almost same function as shown in Figure-1. AUV is basically an extension of the ROV's technology. ROV is controlled by the human from the controller and needs steering control from the mother ship on the surface whereas the AUV is controlled by its on-board controller guided by build-in pre-programmed instructions with free from a chain [1, 2].
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Identification of an Autonomous Underwater Vehicle Dynamic Using Extended Kalman Filter with ARMA Noise Model

Identification of an Autonomous Underwater Vehicle Dynamic Using Extended Kalman Filter with ARMA Noise Model

In the procedure of designing an underwater vehicle or robot, its maneuverability and controllability must be simulated and tested, before the product is finalized for manufacturing. Since the hydrodynamic forces and moments highly affect the dynamic and maneuverability of the system, they must be estimated with a reasonable accuracy. In this study, hydrodynamic coefficients of an autonomous underwater vehicle (AUV) are identified using velocity and displacement measurements, and implementing an Extended Kalman Filter (EKF) estimator. The hydrodynamic coefficients are included in the augmented state vector of a six DOF nonlinear model. The accuracy and the speed of the convergence of the algorithm are improved by selecting a proper covariance matrix using the ARMA process model. This algorithm is used to estimate the hydrodynamic coefficients of two different sample AUVs: NPS AUV II and ISIMI. The comparison of the outputs of the identified models and the outputs of the real simulated models confirms the accuracy of the identification algorithm. This identification method can be used as an efficient tool for evaluating the hydrodynamic coefficients of underwater vehicles (robots), using the experimental data obtained from the test runs.
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Virtual planar motion mechanism tests of the autonomous underwater vehicle autosub

Virtual planar motion mechanism tests of the autonomous underwater vehicle autosub

A simple robust method, using unsteady RANS simulations is presented to numerically replicate the experimental PMM tests performed on a scale model of the Autonomous Underwater Vehicle (AUV) Autosub. The method uses a body fitted inner domain to capture the unsteady flow. This body fitted mesh moves relative to a fixed outer domain via stretching/compressing cells at the interface. Detailed results for pure sway motion are presented and show good agreement for a relatively low computational cost. It is estimated that at the initial design stage a full set of manoeuvring derivatives could be found for an axis-symmetric AUV or submarine in under two days of simulation time using a desktop pc.
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Autonomous Underwater Vehicle Hull Geometry Optimization Using a Multi-objective Algorithm Approach

Autonomous Underwater Vehicle Hull Geometry Optimization Using a Multi-objective Algorithm Approach

In this paper, a new approach to optimize an Autonomous Underwater Vehicle (AUV) hull geometry is presented. Using this methode, the nose and tail of an underwater vehicle are designed, such that their length constraints due to the arrangement of different components in the AUV body are properly addressed. In the current study, an optimal design for the body profile of a torpedo-shaped AUV is conducted, and a multi-objective optimization scheme based on the optimization algorithm Non- dominated Sorting Genetic Algorithm-II (NSGA-II), as an evolutionary algorithm is employed. In addition, predefined geometrical constraints were considered so that equipment with the specific dimensions can be placed inside the AUV space without any effect on the AUV volume and the wetted surface. By optimizing the parameters of the newly presented profile, in addition to maximizing the volume and minimizing the wetted surface area, more diversed shapes can be achieved than with the ‘Myring’ profile. A CFD analysis of the final optimal design indicates that with the help of the proposed profile, the hydrodynamic parameters for the AUV hull were effectively improved.
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Optimal control and guidance of homing and docking tasks using an autonomous underwater vehicle

Optimal control and guidance of homing and docking tasks using an autonomous underwater vehicle

Remotely Operated Vehicles (ROVs) and Unmanned Un- derwater Vehicles are useful for many underwater operations such as collecting biological and mineral resources, however there are some limitations for those vehicles during long- term operations. Consequently an Autonomous Underwater Vehicle (AUV) which is able to make decisions and take control actions more accurately and reliably without human intervention is an alternative to humans especially in long- term underwater complex tasks. Examples of such operations are seabed mapping and surveying, studying underwater and under-ice environments. Although studies have been made of AUVs over the past thirty years [1], still AUV technology limitations remain. Due to the current state of computa- tional capability and data storage capacity, the technology can marginally provide fair speed and efficiency including well- established software developments. Energy storage and power consumption are critical factors for all long-term operations. Its short operational periods limit scopes of each undersea exploration. In long-term experimentation, a vehicle should be able to operate continuously for 24 hours a day. However most underwater vehicles are typically capable of short-term operation. Vehicles require both software and hardware to be turned off before its batteries can be manually recharged or replaced. To overcome the limitations of the onboard battery, data transfer and sensor ranges, a floating docking platform is required to provide a large scope of potential missions. A focus on docking operations allows a vehicle to recharge its
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Depth Control of Autonomous Underwater Vehicle Using Discrete Time Sliding Mode Controller

Depth Control of Autonomous Underwater Vehicle Using Discrete Time Sliding Mode Controller

Autonomous Underwater Vehicle (AUV) has shown popularity for three decades due to its versatility and excellent performance which is increasingly being used in many industries [1]. Their solid small size with self-operated propulsion systems, capability carrying sensors such as depth sensors, video cameras, side-scan sonar and other oceanographic measuring devices makes AUV be well suited in dangerous mission. Futuristic element in AUVs prompts advantage to wider area such as surveillance, environmental monitoring, underwater inspection of harbor and pipeline, geological and biological survey, mine counter measures and so forth. However, an extremely unexpected ocean behavior created challenges to
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Vision Based Autonomous Underwater Vehicle for Pipeline Tracking

Vision Based Autonomous Underwater Vehicle for Pipeline Tracking

ABSTRACT: This paper discusses about the design and fabrication of vision based Autonomous Underwater Cable Tracking (AUCT) system and its working principle based on the propulsion technique. The AUCT system consists of three levels of controllers namely, higher level controller, Middle level controller and the Lower level controller to navigate the Autonomous Underwater Vehicle (AUV). A single CCD camera and an Ultrasonic distance sensor are used to determine the relative position of the underwater cable with respect to the Autonomous Underwater Vehicle (AUV) in a 3 dimensional space. Visual data provides two dimensions and ultrasonic distance sensor data provides the 3 rd dimension. An image filtering technique to reduce some undesirable features is underwater images, is used based on the morphological operator. A real-time algorithm is developed to determine the position of the cable in the image plane by cascading an estimator governed by AUV dynamics, with help of Hough transformation technique. The proposed system will perform very well other than in situations where the cable is covered by waterweeds for a long distance. This system can be used as a solution to the problem of underwater positioning especially in pre-determined areas.
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Development of a Vertical Self-balancing Experimental Autonomous Underwater Vehicle

Development of a Vertical Self-balancing Experimental Autonomous Underwater Vehicle

The unmanned underwater vehicle (UUV) covers both re motely operated vehicles (ROVs) and autonomous underwater vehicle (AUVs). ROVs have tethered umb ilical cable to enable re mote operator to control the operation of the vehicle. Tether influences the dynamics of vehicle, greatly reducing maneuverability. A UVs are tethered free, unmanned, powered by onboard energy sources such as fuel cells and batteries. AUVs performing man ipulation or inspection tasks need to be controlled in six degrees of freedom [1]. A UVs are also equipped with devices such as electronic co mpass, GPS, sonar sensor, laser ranger, pressure sensor, inclination sensor, roll sensor and contro lled by onboard computers to execute co mple x preprogra mmed missions.
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Thermal Analysis and Management for an Autonomous Underwater Vehicle

Thermal Analysis and Management for an Autonomous Underwater Vehicle

In this paper an attempt has been made to study and assess the heat and temperature variation of each sub system of Autonomous underwater vehicle, while functioning separately and as a whole system for various operating conditions through experimentation and thermal simulation using numerical and theoretical means. Methods for heat dissipation for maintaining the temperature inside the pressure hull within the permissible limits also has been studied and reported. Further, an attempt has been made to arrive at a simulation model for predicting the thermal behavior of the vehicle at different operating conditions.
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Teach and repeat path following for an autonomous underwater vehicle

Teach and repeat path following for an autonomous underwater vehicle

Tests were performed on two predefined reference paths: a short path for initial testing in 2013, repeated in 2014; and, a longer path only attempted in 2014. In each scenario the reference mission plan was traversed in teach mode and the resulting reference path then utilized for multiple repeat attempts. Figure 12 show the reference paths used in testing. On each repeat attempt the AUV conducted a prescribed mission which guaranteed it to both cross the reference path, and then pass alongside, whilst generating sonar images, in an attempt to make an initial match and localization to the reference set — the discovery phase. If a strong match was made to provide the initial localization, the TR system requested an interrupt to the ongoing mission, entered the repeat phase and attempted to follow the reference path to completion. The trials provided an opportunity to test all aspects of the TR system: image matching, localization, navigation and autonomous AUV control.
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Velocity Field Control Assistance for
Autonomous Underwater Vehicle Path Control

Velocity Field Control Assistance for Autonomous Underwater Vehicle Path Control

Abstract— To minimize the cost of inspecting the surface of a small battle ship, autonomous underwater vehicles (AUVs) can be used. However, it is extremely difficult to achieve both time and path tracking of an AUV for inspection. A robustness property of the velocity field control (VFC) is required to control an AUV while operating in an environment. The objective of this paper is to apply VFC-assisted linear quadratic regulation (LQR) for path trajectory to track an AUV while on a surveying mission. The results of the simulation and implementation were performed for circular path tracking.
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Studies on autonomous underwater vehicle systems

Studies on autonomous underwater vehicle systems

Remotely Operated Vehicles are typically remote controlled from an operator’s desk on a ship, to which they are connected via an umbilical cable (Valavanis et al., 2000). The umbilical cable supplies control commands to the motors of the ROV, transfers sensor data back to the operator, and usually also supplies power to the vehicle. The cable can also act as an emergency cable to retrieve the vehicle in case of mechanical faults. Typically, the cable is very robust, a few hundred or thousand metres long, and can easily weigh several hundred kilograms. The vehicle must have strong propulsion to counter the weight and the hydrodynamic drag of the cable (Zhang 2000). ROVs come in a large variety of shapes and sizes. The smallest ones can be as light as a few kilograms, and can only carry a camera or a small sensor suite. Due to the small size, they can only drag a short, thin cable, and are therefore typically operated close to the ship, or a larger submersible acting as a base platform. Most AUVs are scientific robots that are equipped with on board computing for navigation and mission planning, carry their own power supply and a range of sensors (depth sensors, inertial navigation, compass, temperature, often also sonar sensor and cameras) as shown in Figure 1. Once deployed, the AUV carries out the mission autonomously without human interaction and returns to the surface for pickup after completion (Buniyamin et al., 2011). Missions can last for several hours, or even several weeks in some special cases. There are two principally different designs for underwater vehicles. Most ROVs are an open frame design with multiple thrusters that allow decoupled multidimensional movement or even holonomic movement (6 degrees of freedom). AUVs may also have an open frame design, but
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Performance analysis and simulation of an autonomous underwater vehicle equipped with the collective and cyclic pitch propeller

Performance analysis and simulation of an autonomous underwater vehicle equipped with the collective and cyclic pitch propeller

There are a number of AUVs integrating different methods of locomotion in one system. The hybrid propulsion system have been extensively studied as a potential means for improvement of underwater vehicle performance. An excellent example of this can be seen on the Tethys-class AUV built by the Monterey Bay Aquarium Research Institute. The Tethys-class AUV has a unique ability to operate efficiently in three different operational modes with a range of actua- tors: traditional propeller for high speed, a moving internal mass (like a glider) for low speed, and variable buoyancy for drifting (like a float) (Hobson et al., 2012). The Guanay-II AUV uses the propulsion system comprises: a main engine, which provides the propulsion, two side thrusters, which monitor the direction of the vehicle and an internal pneumatic stainless cylin- der, which allows the vehicle to dive by taking in and ejecting water (Gomáriz et al., 2015). An- other hybrid AUV uses ducted propeller and rudder located at the aft for horizontal motion and internal mass shifter mechanism for vertical motion (Tran et al., 2015b) or the internal rolling mass mechanism for roll control (Hong and Chitre, 2015). The internal actuators are located inside the vehicle hull and hence are less subject to damage than the external thrusters are. How- ever, the use of moving internal actuators are limited due to the significant requirement for the inner hull space and high power consumption from multiple systems. Additionally, it is chal- lenging to design the control strategy for the integrated propulsion unit with different force generation methods.
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