• International Journal of Naval Architecture and Ocean Engineering
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• 제8권3호
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• pp.243-251
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• 2016

Autonomous Underwater Vehicles (AUVs) generally work in complex marine environments. Any fault in AUVs may cause significant losses. Thus, system reliability and automatic fault diagnosis are important. To address the actuator failure of AUVs, a fault diagnosis method based on the Gaussian particle filter is proposed in this study. Six free-space motion equation mathematical models are established in accordance with the actuator configuration of AUVs. The value of the control (moment) loss parameter is adopted on the basis of these models to represent underwater vehicle malfunction, and an actuator failure model is established. An improved Gaussian particle filtering algorithm is proposed and is used to estimate the AUV failure model and motion state. Bayes algorithm is employed to perform robot fault detection. The sliding window method is adopted for fault magnitude estimation. The feasibility and validity of the proposed method are verified through simulation experiments and experimental data.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.513-517
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• 2006

Autonomous Underwater Vehicles plays specific role in underwater investigation. Generally, this kind of vehicles will move along a planned path for sea bottom or underwater installations inspections, search for mineral deposits along shelves, seeking lost items including bottom mines or for hydrographic measurements. A crucial barrier for it remains the possibility of precise determination of their underwater position. Commonly used radionavigation systems do not work in such circumstances or do not guarantee the required accuracies. In the paper some new solution is proposed on the assumption that it is possible to increase the precision by certain processing of a combination of measurements conducted by means of different techniques. Objective of the paper is the idea of navigation of AUV which consists of two phases: firstly a trip of AUV along pre-planned route and after that postprocessed transformation of collected data in post-processing mode. During the processing of collected data the modern adjustment methods have been applied, mainly estimation by means of least squares and M-estimation. Application of these methods should be associated with the measuring and geometric conditions of navigational tasks and thus suited for specific scientific and technical problems of underwater navigation. The first results of computer aided investigation will be presented and the basic scope of these application and possible development directions will be indicated also. The paper is prepared as an partial results of the works carried out within a framework of the research Project: 'Improvement of the Precise Underwater Vehicle Navigation Methods' financed by the Polish Ministry of Education and Science (No 0 T00A 012 25).

• 제어로봇시스템학회논문지
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• 제13권5호
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• pp.406-413
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• 2007

This paper proposes a self-recurrent wavelet neural network(SRWNN) based adaptive backstepping control technique for the robust steering control of autonomous underwater vehicles(AUVs) with unknown model uncertainties and external disturbance. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the steering model of AUV. The adaptation laws for the weights of SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for the on-line control of AUV. Finally, simulation results for steering control of an AUV with unknown model uncertainties and external disturbance are included to illustrate the effectiveness of the proposed method.

• Journal of Positioning, Navigation, and Timing
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• 제5권2호
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• pp.97-107
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• 2016

Military Autonomous Underwater Vehicle (AUV) is utilized to search a mine under the sea. This paper presents design and performance verification of real-time hybrid navigation system for AUV. The navigation system uses Doppler Velocity Log (DVL) integration method to correct INS error in underwater. When the AUV is floated on the water, the accumulated error of navigation algorithm is corrected using position/velocity of GPS. The navigation algorithm is verified using 6 Degree Of Freedom (DOF) simulation, Program In the Loop Simulation (PILS). Finally, the experiments are performed in real sea environment to prove the reliability of real-time hybrid navigation algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권1호
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• pp.82-86
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• 2008

The article presents ARTMAP and Fuzzy BK-Product approach underwater obstacle avoidance for the Autonomous underwater Vehicles (AUV). The AUV moves an unstructured area of underwater and could be met with obstacles in its way. The AUVs are equipped with complex sensorial systems like camera, aquatic sonar system, and transducers. A Neural integrated Fuzzy BK-Product controller, which integrates Fuzzy logic representation of the human thinking procedure with the learning capabilities of neural-networks (ARTMAP), is developed for obstacle avoidance in the case of unstructured areas. In this paper, ARTMAP-Fuzzy BK-Product controller architecture comprises of two distinct elements, are 1) Fuzzy Logic Membership Function and 2) Feed-Forward ART component. Feed-Forward ART component is used to understanding the unstructured underwater environment and Fuzzy BK-Product interpolates the Fuzzy rule set and after the defuzzyfication, the output is used to take the decision for safety direction to go for avoiding the obstacle collision with the AUV. An on-line reinforcement learning method is introduced which adapts the performance of the fuzzy units continuously to any changes in the environment and make decision for the optimal path from source to destination.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2002년도 추계학술대회 논문집
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• pp.142-148
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• 2002

Autonomous underwater vehicles (AUVs) are unmanned underwater vessels to investigate sea environments, oceanography and deep-sea resources autonomously. Docking systems are required to increase the capability of the AUVs to recharge the batteries and to transmit data in real time for specific underwater works, such as repeated jobs at sea bed. This paper presents a visual servo control system for an AUV to dock into an underwater station with a camera mounted at the nose center of the AUV. To make the visual servo control system, this paper derives an optical flow model of a camera, where the projected motions of the image plane are described with the rotational and translational velocities of the AUV. This paper combines the optical flow equation of the camera with the AUVs equation of motion, and derives a state equation for the visual servoing AUV. This paper proposes a discrete-time MIMO controller minimizing a cost function. The control inputs of the AUV are automatically generated with the projected target position on the CCD plane of the camera and with the AUVs motion. To demonstrate the effectiveness of the modeling and the control law of the visual servoing AUV, simulations on docking the AUV to a target station are performed with the 6-dof nonlinear equations of REMUS AUV and a CCD camera.

• 한국해양공학회지
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• 제30권3호
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• pp.194-200
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• 2016

Recently, UUVs (unmanned underwater vehicles) have increasingly been applied in various science and engineering applications. In-water inspection, which used to be performed by human divers, is a potential application for UUVs. In particular, the operational safety and performance of in-water inspection missions can be greatly improved by using an underwater robotic vehicle. The capabilities of hovering maneuvers and automatic image mosaicking are essential for autonomous underwater visual inspection. This paper presents the development of a hover-capable autonomous underwater vehicle system for autonomous in-water inspection, which includes both a hardware platform and operational software algorithms. Some results from an experiment in a model basin are presented to demonstrate the feasibility of the developed system and algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1791-1796
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• 2004

Recently, great improvements have been made in developing autonomous underwater vehicles (AUVs) using stateof- the-art technologies for various kinds of sophisticated underwater missions. To meet increasing demands posed on AUVs, a powerful on-board computer system and an accurate sensor system with an well-organized control system architecture are needed. In this paper, a new control system architecture is proposed for AUV, ORCA (Oceanic Reinforced Cruising Agent) which is being currently developed by Korea Research Institute of Ships and Ocean Engineering (KRISO). The proposed architecture uses a hybrid architecture that combines a hierarchical architecture and a behavior based control architecture with an evaluator for coordinating between the architectures. This paper also proposed a sensor fusion structure based on the definition of 4 categories of sensors called grouping and 5-step data processing procedure. The development of the AUV, ORCA involving the system architecture, vehicle layout, and hardware configuration of on-board system are described.

• Ocean Systems Engineering
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• 제4권1호
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• pp.1-19
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• 2014

Hydrodynamic coefficients strongly affect the dynamic performance of autonomous underwater vehicles (AUVs). A novel kind of underwater vehicle (Heavier-than-water AUV) with higher density than water is presented, which is different from conventional ones. RANS method and overlapping grids are used to simulate the flow field around the vehicle. Lifts, drags and moments of different attack and drift angles in steady state are calculated. The hydrodynamic performances and how the forces change with the attitude are analyzed according to the flow field structure. The steady-state results using overlapping grid method are compared with those of software FLUENT and wind tunnel tests. The calculation results show that the overlapping grid method can well simulate the viscous flow field around the underwater vehicle. Overlapping grid skills have also been used to figure out the planar-motion-mechanism (PMM) problem of Heavier-than-water AUV and forecast its hydrodynamic performance, verifying its effectiveness in dealing with the dynamic problems, which would be quite helpful for design and control of Heavier-than-water AUV and other underwater vehicles.

• International Journal of Control, Automation, and Systems
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• 제2권3호
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• pp.374-383
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• 2004

This paper presents a neural network adaptive controller for autonomous diving control of an autonomous underwater vehicle (AUV) using adaptive backstepping method. In general, the dynamics of underwater robotics vehicles (URVs) are highly nonlinear and the hydrodynamic coefficients of vehicles are difficult to be accurately determined a priori because of variations of these coefficients with different operating conditions. In this paper, the smooth unknown dynamics of a vehicle is approximated by a neural network, and the remaining unstructured uncertainties, such as disturbances and unmodeled dynamics, are assumed to be unbounded, although they still satisfy certain growth conditions characterized by 'bounding functions' composed of known functions multiplied by unknown constants. Under certain relaxed assumptions pertaining to the control gain functions, the proposed control scheme can guarantee that all the signals in the closed-loop system satisfy to be uniformly ultimately bounded (UUB). Simulation studies are included to illustrate the effectiveness of the proposed control scheme, and some practical features of the control laws are also discussed.