• 한국군사과학기술학회지
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• 제17권6호
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• pp.751-763
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• 2014

The paper is written to determine the optimal search pattern through search effects assessment on underwater targets. 5 types of search patterns are introduced such as, M-type pattern, W-type pattern, rectangular pattern, 4-type pattern and square pattern, In addition, Operational effectiveness analysis model is developed to obtain the optimum search pattern. The algorithms and mathematical models are also suggested to analyze the required search times, AUV's movement patterns, moving distances, overlapping areas and so on.

• 로봇학회논문지
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• 제9권4호
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• pp.206-215
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• 2014

Acoustic signal is crucial for the autonomous navigation of underwater vehicles. For this purpose, this paper presents a method of acoustic source localization. The proposed method is based on the probabilistic estimation of time delay of acoustic signals received by two hydrophones. Using Bayesian update process, the proposed method can provide reliable estimation of direction angle of the acoustic source. The acquired direction information is used to estimate the location of the acoustic source. By accumulating direction information from various vehicle locations, the acoustic source localization is achieved using extended Kalman filter. The proposed method can provide a reliable estimation of the direction and location of the acoustic source, even under for a noisy acoustic signal. Experimental results demonstrate the performance of the proposed acoustic source localization method in a real sea environment.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2001년도 추계학술대회 논문집
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• pp.91-96
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• 2001

This paper presents a neural net based nonlinear adaptive controller for an autonomous underwater vehicle (AUV). AUV's dynamics are highly nonlinear and their hydrodynamic coefficients vary with different operational conditions, so it is necessary for the high performance control system of an AUV to have the capacities of learning and adapting to the change of the AUV's dynamics. In this paper a linearly parameterized neural network is used to approximate the uncertainties of the AUV's dynamics, and a sliding mode control is introduced to attenuate the effects of the neural network's reconstruction errors and the disturbances of AUV's dynamics. The presented controller is consist of three parallel schemes; linear feedback control, sliding mode control and neural network. Lyapunov theory is used to guarantee the asymptotic convergence of trajectory tracking errors and the neural network's weights errors. Numerical simulations for motion control of an AUV are performed to illustrate to effectiveness of the proposed techniques.

• Journal of Advanced Marine Engineering and Technology
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• 제36권4호
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• pp.451-458
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• 2012

수중의 작업 목적에 따라 ROV와 AUV의 두 가지 용도로 사용할 수 있는 새로운 구조의 변신 6자유도 수중로봇의 설계연구를 하였다. CROV의 ROV모드와 AUV모드에 대한 각각의 구조 설계연구를 수행하였으며 각각의 모드에 대한 제어시스템을 설계하고 AUV의 경우에는 추력에 따른 항해속도에 대한 해석을 수행하였다. ROV나 AUV의 정확한 위치 및 속도를 추정할 수 있도록 다양한 센서신호를 퓨전하여 처리하는 센서퓨전보드를 제작하고 확장 칼만필터를 포함하는 전체 제어시스템을 설계하고 제작하였다.

• 한국해양공학회지
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• 제19권2호
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• pp.74-81
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• 2005

A 3-D localization method of an autonomous underwater vehicle (AUV) has been developed, which can solve the limitations oj the conventional localization, such as LBL or SBL that reduces the flexibility and availability of the AUV. The system is composed of a mother ship (small unmanned marine prober) on the surface of the water and an unmanned underwater vehicle in the water. The mother ship is equipped with a digital compass and a GPS for position information, and an extended Kalman filter is used for position estimation. For the localization of the AUV, we used only non-inertial sensors, such as a digital compass, a pressure sensor, a clinometer, and ultrasonic sensors. From the orientation and velocity information, a priori position of the AUV is estimated by applying the dead reckoning method. Based on the extended Kalman filter algorithm, a posteriori position of the AUV is, then, updated by using the distance between the AUV and a mother ship on the surface of the water, together with the depth information from the pressure sensor.

• 한국해양공학회지
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• 제16권1호
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• pp.8-15
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• 2002

This paper presents a neural net based nonlinear adaptive controller for an autonomous underwater vehicle (AUV). AUV's dynamics are highly nonlinear and their hydrodynamic coefficients vary with different operational conditions, so it is necessary for the high performance control system of an AUV to have the capacities of learning and adapting to the change of the AUV's dynamics. In this paper a linearly parameterized neural network is used to approximate the uncertainties of the AUV's dynamic, and the basis function vector of network is constructed according to th AUV's physical properties. A sliding mode control scheme is introduced to attenuate the effect of the neural network's reconstruction errors and the disturbances in AUV's dynamics. Using Lyapunov theory, the stability of the presented control system is guaranteed as well as the uniformly boundedness of tracking errors and neural network's weights estimation errors. Finally, numerical simulations for motion control of an AUV are performed to illustrate the effectiveness of the proposed techniques.

• 한국해양공학회지
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• 제28권3호
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• pp.199-208
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• 2014

In this study, propeller open water characteristics ($K_P$, $K_T$ and ${\eta}_o$) were compared for three different ducted propellers using a Computational Fluid Dynamics (CFD) analysis, as well as an experimental test at a basin. The best shape of the duct was selected from the three types of specially designed ducts based on the CFD analysis results. The same propeller model (Kaplan type propeller) was used inside all three duct models, and the propeller open water characteristics were compared, predominantly at the design speed for an underwater vehicle. Finally, the results of the CFD test simulations for the selected duct case were verified by experimental open water tests in a towing tank.

• International Journal of Naval Architecture and Ocean Engineering
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• 제11권1호
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• pp.211-224
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• 2019

This paper deals with a nonlinear controller based on saturation functions with variable parameters for set-point regulation and trajectory tracking control of an Autonomous Underwater Vehicle (AUV). In many cases, saturation functions with constant parameters are used to limit the input signals generated by a classical PD (Proportional-Derivative) controller to avoid damaging the actuators; however this abrupt bounded harms the performance of the controller. We, therefore, propose to replace the conventional saturation function, with constant parameters, by a saturation function with variable parameters to limit the signals of a PD controller, which is the base of the nonlinear PD with gravitational/buoyancy compensation and the nonlinear PD + controllers that we propose in this paper. Consequently, the mathematical model is obtained, considering the featuring operation of the underwater vehicle LIRMIA 2, to do the stability analysis of the closed-loop system with the proposed nonlinear controllers using the Lyapunov arguments. The experimental results show the performance of an AUV (LIRMIA 2) for the depth control problems in the case of set-point regulation and trajectory tracking control.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2012년도 전기공동학술대회 논문집
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• pp.214-214
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• 2012

본 논문 만타형 무인잠수정을 이용한 실해역 자율주행 성능시험에 관한 내용을 다룬다. 만타형의 개발을 위해서 6자유도 운동방정식에 대한 시뮬레이션을 실시하였으며, 시뮬레이션 결과를 바탕으로 만타형 무인잠수정을 개발하여 실해역에서 성능시험을 실시하였다. 실해역 자율주행 시험은 가시선 방법(LOS, Line of Sight)방법을 이용하였으며, 제어에는 PD 제어기를 사용하였다. 목표반경을 10m로 하였을 때, 설정된 좌표로 이동하는 것을 확인하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2000년도 추계학술발표논문집 (하)
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• pp.1441-1444
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• 2000

실시간 정보가 알려지지 않은 해저환경에서 자율수중 운동체(AUV, Autonomous Underwater Vehicle)가 성공적인 임무 수행을 완료하기 위해서는 주어진 목표지점까지의 안전하고 효율적인 경로설정이 선행되어야 한다. 이를 위해 평가함수(evaluation function)에 기반한 휴리스틱 탐색(heuristic search)이 사용되는데 대부분의 평가함수는 목표점까지의 거리, 소모되는 연료로 구성된다[1]. 본 논문에서는 영역전문가가 보유한 장애물회피 관련 경험적 정보(heuristic information)를 반영하여 보다 효율적인 평가함수를 고안하며 후보노드들간의 관계성을 고려한 퍼지관계곱(Fuzzy Relational Products) 기반 휴리스틱 탐색기법을 제안한다. 제안한 탐색기법의 성능을 검증하기 위해 수행시간(cpu time), 경로의 최적화(optimization)정도, 사용 메모리 관점에서 시뮬레이션을 통해 $A^*$ 탐색기법과 비교한다.