• 제어로봇시스템학회논문지
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• 제22권7호
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• pp.562-569
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• 2016

This paper proposes a navigation system with a robust localization method for an underwater unmanned vehicle. For robust localization with IMU (Inertial Measurement Unit), a DVL (Doppler Velocity Log), and depth sensors, the EKF (Extended Kalman Filter) has been utilized to fuse multiple nonlinear data. Note that the GPS (Global Positioning System), which can obtain the absolute coordinates of the vehicle, cannot be used in the water. Additionally, the DVL has been used for measuring the relative velocity of the underwater vehicle. The DVL sensor measures the velocity of an object by using Doppler effects, which cause sound frequency changes from the relative velocity between a sound source and an observer. When the vehicle is moving, the motion trajectory to a target position can be recorded by the sensors attached to the vehicle. The performance of the proposed navigation system has been verified through real experiments in which an underwater unmanned vehicle reached a target position by using an IMU as a primary sensor and a DVL as the secondary sensor.

• 대한조선학회논문집
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• 제42권6호
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• pp.551-558
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• 2005

In this paper, horizontal manoeuvrability of an underwater vehicle near free surface was investigated. Planar Motion Mechanism(PMM) tests were performed at the shallow depth within 4.5 times of vehicle's diameter. Hydrodynamic coefficients related to the horizontal movement were estimated from the measured data using Least SQuare(LS) method and analyzed at each submerged depth. Furthermore, horizontal dynamic stability, trajectory of turning and zigzag test were investigated for the various depths. As underwater vehicle is positioned nearer to the free surface, forces increase and moment decreases. Tested model was found to be stable only at the depth 0.5 times of vehicle's diameter.

• 한국정밀공학회지
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• 제31권3호
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• pp.253-259
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• 2014

This study proposes a map-based control method to improve a vehicle's lateral stability, and the performance of the proposed method is compared with that of the conventional model-referenced control method. Model-referenced control uses the sliding mode method to determine the compensated yaw moment; in contrast, the proposed map-based control uses the compensated yaw moment map acquired by vehicle stability analysis. The vehicle stability region is calculated by a topological method based on the trajectory reversal method. The performances of model-referenced control and map-based control are compared under various road conditions and driving inputs. Model-referenced control uses a control input to satisfy the linear reference model, and it generates unnecessary tire lateral forces that may lead to worse performance than an uncontrolled vehicle with step steering input on a road with low friction coefficient. The simulation results show that map-based control provides better stability than model-referenced control.

• International Journal of Naval Architecture and Ocean Engineering
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• 제10권2호
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• pp.188-200
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• 2018

This paper proposes a single control strategy to solve the problem of trans-media vehicle difficult control. The proposed control strategy is just to control the vehicle's air navigation, but not to control the underwater navigation. The hydrodynamic model of a vehicle when entering water obliquely at low speed has been founded to analyze the motion characteristics. Two methods have been used to simulate the vehicle entering water in the same condition: numerical simulation method and theoretical model solving method. And the results of the two methods can validate the hydrodynamic model founded in this paper. The entering water motion in the conditions of different velocity, different angle, and different attack angle has been simulated by this hydrodynamic model and the simulation has been analyzed. And the change rule of the vehicle's gestures and position when entering water has been obtained by analysis. This entering water rule will guide the follow-up of a series of research, such as the underwater navigation, the exiting water process and so on.

• 한국항공우주학회지
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• 제46권1호
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• pp.68-77
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• 2018

달 궤도선의 전반적인 특성을 해석하기 위해 일별 발사가 가능한 Variable Coast 방식을 3.5 위상전이궤적에 적용하였다. 발사장 및 발사체를 선정하여 발사에서부터 달 궤도 진입까지의 전 과정에 대한 임무 시나리오를 구성 및 해석을 수행하였다. 특히 지구-달 회전좌표계에서 정의한 SEM(Satellite-Earth-Moon) 각도는 3.5 위상전이궤적을 전반적으로 검토할 수 있는 중요한 구속조건이다. SEM 각도를 이용한 시뮬레이션 결과를 지구-달 전이궤적 및 달 궤도 진입에서의 발사 시각, 관성비행 기간, 근지점 고도 및 ${\Delta}V$등 다양한 관점으로 분석하고 최적의 SEM 각도를 제안하였다. 이 결과는 향후 Fixed Coast 분석결과와 비교함으로써 발사체 선정에 따른 3.5 위상전이궤적의 특성을 평가하는데 큰 도움을 줄 것으로 예상된다.

• 한국항행학회논문지
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• 제24권5호
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• pp.382-392
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• 2020

드론기술은 민수용과 군사용 양 분야 에서 전도유망한 기술이나, 규정과 관련법의 미성숙으로 불법드론이 오남용 되고, 사회안전에 심각한 위협이 되고 있다. 본고에서는 PSO (particle swarm optimization)에 기반을 둔 군집드론 궤적계획기를 개발하여, 군집정찰드론들에게 최적화된 3차원 궤적탐지기술을 제공한다. 나아가서, 에너지소비도, 비행위험도 및 SAP (surveillance area priority)와 부합하는 군집 목적물 최적화 함수를 제시하고 평가한다. 군집 비행 시뮬레이션 결과는, 제안한 궤적계획기로 생성한 궤적은 에너지 소비도 및 비행위험도를 최소화 하며 탐색한다는 것을 입증해준다.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1997년도 춘계학술대회 논문집
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• pp.68-75
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• 1997

This paper presents a new approach to the design of neural-fuzzy controller for the speed and azimuth control of a track vehicle. The proposed control scheme uses a Gaussian function as a unit function in the frzzy-neural network, and back propagaton algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a track vehicle driven by two independent wheels.

• 로봇학회논문지
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• 제7권2호
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• pp.113-119
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• 2012

In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.

• 한국지능시스템학회논문지
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• 제14권2호
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• pp.239-246
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• 2004

In this paper, we develop and implement a high integrity GNC(Guidance, Navigation, and Control) system, based on the combined use of the Global Positioning System (GPS) and an Inertial Measurement Unit (IMU), for autonomous land vehicle applications. This paper highlights guidance for the predetermined trajectory and navigation with detection of possible faults during the fusion process in order to enhance the integrity of the navigation loop. The implementation of the GNC system to the autonomous land vehicle presented with fault detection methodology considers high frequency faults from the GPS receiver caused by shadowing and multipath error The implementation, based on a low-cost, strapdown INS aided by standard GPS technology, is described. The results of the field test in the urban environment are presented and showed effectiveness of the GNC system.

• 제어로봇시스템학회논문지
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• 제16권8호
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• pp.810-815
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• 2010

This paper proposes a vision/DR integrated navigation system using distance between wheels of the vehicle and a gyroscope. In order to show the validity of the proposed vision/DR integrated navigation system, experiments were performed for a trajectory of a mobile robot. Experimental results show that the proposed vision/DR integrated navigation system gives better navigation performance than a vision/DR integrated navigation system using only distance between wheels of the vehicle.