• 대한조선학회논문집
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• 제60권4호
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• pp.256-265
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• 2023

This paper describes the results of the development of the submarine Free Running Model (FRM). First, the goal of development was set based on the test conditions and the test environment, and the system was obtained accordingly. The target submarine, Joubert BB2 submarine, was selected with a scale of 18.35 in accordance with the development goal. In order to conduct a submarine FRM test underwater, where communication is impossible, the FRM must operate at least semi-autonomously. For this purpose, an Extended Kalman Filter (EKF) based underwater integrated navigation system and control system using a sailplane and an X-shaped sternplane were designed respectively. In addition, a ballast system was designed to enable the model to float to the water surface in case of an emergency. To verify its propulsion, navigation, and control performance, the FRM tests were conducted in both indoor and outdoor basins. As a result, the relationship between propeller RPM and vehicle speed was derived, and it was confirmed that the navigation and control performance met the target value.

• 로봇학회논문지
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• 제18권4호
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• pp.427-435
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• 2023

This paper proposes a method of estimating the pose of a mobile robot by using a learning model. When estimating the pose of a mobile robot, wheel encoder and inertial measurement unit (IMU) data are generally utilized. However, depending on the condition of the ground surface, slip occurs due to interaction between the wheel and the floor. In this case, it is hard to predict pose accurately by using only encoder and IMU. Thus, in order to reduce pose error even in such conditions, this paper introduces a pose estimation method based on a learning model using data of the wheel encoder and IMU. As the learning model, long short-term memory (LSTM) network is adopted. The inputs to LSTM are velocity and acceleration data from the wheel encoder and IMU. Outputs from network are corrected linear and angular velocity. Estimated pose is calculated through numerically integrating output velocities. Dataset used as ground truth of learning model is collected in various ground conditions. Experimental results demonstrate that proposed learning model has higher accuracy of pose estimation than extended Kalman filter (EKF) and other learning models using the same data under various ground conditions.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.174-179
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• 2003

Through the simultaneous localization and map building (SLAM) technique, a robot can create maps about its unknown environment while it continuously localizes its position. Grid maps and feature maps have been widely used for SLAM together with application of probability methods and POMDP (partially observed Markov decision process). But this approach based on grid maps suffers from enormous computational burden. Topological maps, however, have drawn more attention these days because they are compact, provide natural interfaces, and are easily applicable to path planning in comparison with grid maps. Some topological SLAM techniques like GVG (generalized Voronoi diagram) were introduced, but it enables the robot to decide only whether the current position is part of GVG branch or not in the GVG algorithm. In this paper, therefore, to overcome these problems, we present a method for updating a global topological map from the local topological maps. These local topological maps are created through a labeled Voronoi diagram algorithm from the local grid map built based on the sensor information at the current robot position. And the nodes of a local topological map can be utilized as the features of the environment because it is robust in light of visibility problem. The geometric information of the feature is applied to the extended Kalman filter and the SLAM in the indoor environment is accomplished. A series of simulations have been conducted using a two-wheeled mobile robot equipped with a laser scanner. It is shown that the proposed scheme can be applied relatively well.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1664-1673
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• 2016

Lithium rechargeable cells are used in many industrial applications, because they have high energy density and high power density. For an effective use of these lithium cells, it is essential to build a reliable battery management system (BMS). Therefore, the state of charge (SOC) estimation is one of the most important techniques used in the BMS. An appropriate modeling of the battery characteristics and an accurate algorithm to correct the modeling errors in accordance with the simplified model are required for practical SOC estimation. In order to implement these issues, this approach presents the comparative analysis of the SOC estimation performance using equivalent electrical circuit modeling (EECM) and noise suppression technique (NST) in three representative $LiCoO_2/LiFePO_4/LiNiMnCoO_2$ cells extensively applied in electric vehicles (EVs), hybrid electric vehicles (HEVs) and energy storage system (ESS) applications. Depending on the difference between some EECMs according to the number of RC-ladders and NST, the SOC estimation performances based on the extended Kalman filter (EKF) algorithm are compared. Additionally, in order to increase the accuracy of the EECM of the $LiFePO_4$ cell, a minor loop trajectory for proper OCV parameterization is applied to the SOC estimation for the comparison of the performances among the compared to SOC estimation performance.

• Ocean and Polar Research
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• 제33권1호
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• pp.69-76
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• 2011

A test miner named MineRo was constructed for the purpose of shallow water test of mining performance. In June of 2009, the performance test was conducted in depth of 100 m, 5 km away from Hupo-port (Korean East Sea), to assess if the developed system is able to collect and lift manganese nodules from seafloor. In August of 2010, in-situ test of automatic path tracking control of MineRo was performed in depth of 120 m at the same site. For path tracking control, a localization algorithm determining MineRo's position on seabed is prerequisite. This study proposes an improved underwater navigation algorithm through estimation of MineRo's kinematic parameters. In general, the kinematic parameters such as track slips and slip angle are indirectly calculated using the position data from USBL (Ultra-Short Base Line) system and heading data from gyro sensors. However, the obtained data values are likely to be different from the real values, primarily due to the random noise of position data. The aim of this study is to enhance the reliability of the algorithm by measuring kinematic parameters, track slips and slip angle.

• 한국항공우주학회지
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• 제43권5호
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• pp.405-412
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• 2015

본 논문은 회전안정성을 갖는 탄도수정탄의 대기속도 추정을 위한 필터 설계에 대하여 설명한다. 대상 시스템은 운용상의 제약(공간, 파워)으로 인하여, 대기속도 측정을 위한 센서를 사용할 수 없다. 따라서 한정된 센서를 이용한 대기속도 추정이 필요하다. 따라서 본 연구에서는 IMU(가속도계, 자이로)에서 측정하는 3축 가속도와 각속도 데이터만 이용하여, 대기속도 추정을 위한 필터를 설계하였다. 대상 시스템의 경우, 넓은 속도, 고도의 운용범위를 커버하기 위한 추정 필터가 필요하므로 본 연구에서는 확장 칼만필터를 설계하여 기존의 연구와의 차별성을 두었다. 확장 칼만필터 설계를 위한 자코비안 행렬은 NRF(No-roll frame)에서의 간략화된 선형모델을 이용하여 구성하였다. 최종적으로 센서 오차와 바람 모델을 포함한 시뮬레이션을 통해 그 성능을 검토하였다. 이때, 시뮬레이션은 설계한 대기속도와 각속도 모델 오차의 영향을 분석하기 위하여 네 가지 경우의 프로세스 공분산 행렬 값에 대한 영향을 분석하였다.

• 한국항공우주학회지
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• 제45권12호
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• pp.1076-1083
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• 2017

본 논문은 발사체를 추적하는 단일 레이다 시스템에서 측정한 데이터에 포함된 오차를 추정하는 기법에 관한 내용을 다룬다. 레이다 시스템의 발사체 추적 데이터에는 발사체의 실제 위치, 방위각 혹은 고각 정보와 무작위 잡음, 그리고 전파굴절에 의한 바이어스가 포함되어져 있는 것으로 알려져 있다. 본 논문에서는 기존연구내용과는 달리, GPS와 같은 타 추적 데이터를 사용하지 않고 단일 레이다 시스템의 발사체 추적 데이터만을 사용해 레이다 추적 데이터에 포함된 바이어스를 정확하게 추정하는 기법을 소개한다. 제안된 기법을 실제 나로호(KSLV-I) 추적 데이터에 적용하여 그 정확성을 검증하였다.

• 한국항행학회논문지
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• 제3권2호
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• pp.97-107
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• 1999

항공기의 착륙을 추적하기 위해 많이 사용되는 수동 센서인 레이저 거리 측정기(LRF)와 전방관측 적외선 카메라(FLIR)는 배기가스교란(Exhaust Plume Disturbance)으로 인한 고도각 측정 시에 오차를 발생시킨다. 이 경우에 확장형 칼만필터(EKF)를 사용하여 거리 및 고도를 측정하면 배기가스(plume)와 같은 비-가우시안 잡음 때문에 추적 성능이 저하된다. 본 논문에서는 배기가스의 발생 타이밍을 검출기(PD)를 사용하여 확인한 후에 배기가스가 발생하면 적응형 추산법을 사용하고 배기가스의 영향이 없을 때에는 기존의 확장형 칼만필터를 사용하는 복합 방식을 제안하고 이를 위한 적응형 필터를 설계한다. 이 혼합형 필터는 배기가스와 같은 미지의 바이어스를 제거하는데 매우 효과적인 방법이며 시뮬레이션을 통하여 이러한 성능을 예증한다.

• Journal of Positioning, Navigation, and Timing
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• 제6권4호
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• pp.167-179
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• 2017

In this paper, a simplified Cubature Kalman Filter (SCKF) is proposed to reduce the computation load of CKF, which is then used as a filter for transfer alignment of shipboard INS. CKF is an approximate Bayesian filter that can be applied to non-linear systems. When an initial estimation error is large, convergence characteristic of the CKF is more stable than that of the Extended Kalman Filter (EKF), and the reliability of the filter operation is more ensured than that of the Unscented Kalman Filter (UKF). However, when a system degree is large, the computation amount of CKF is also increased significantly, becoming a burden on real-time implementation in embedded systems. A simplified CKF is proposed to address this problem. This filter is applied to shipboard inertial navigation system (INS) transfer alignment. In the filter design for transfer alignment, measurement type and measurement update rate should be determined first, and if an application target is a ship, lever-arm problem, flexure of the hull, and asynchronous time problem between Master Inertial Navigation System (MINS) and Slave Inertial Navigation System (SINS) should be taken into consideration. In this paper, a transfer alignment filter based on SCKF is designed by considering these problems, and its performance is validated based on simulations.

• 전자공학회논문지SC
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• 제48권3호
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• pp.21-29
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• 2011

본 논문은 스테레오 비전 시스템에서 객체의 기동 상태에 상관없이 안정된 거리 및 속도를 추정할 수 있는 방법을 제안한다. 스테레오 비전은 좌우 영상의 시차를 이용하여 거리를 추정할 수 있지만, 영상 화소의 양자화 오차로 인해 거리 오차가 발생할 수 있다. 부화소 보간법은 이러한 양자화 오차를 최소화하여 실수를 갖는 정밀 시차를 추정할 수 있다. 확장형 칼만 필터는 추정된 정밀 시차의 공분산을 최소화하고 객체의 속도를 추정하기 위하여 사용되어진다. 하지만, 시스템 모델의 불확실성으로 인해 기동이 발생했을 때, 발산 문제가 생기고 이는 오히려 추정 오차를 증가시킨다. 본 논문에서는 연산 시간을 최소화하면서, 객체의 기동 상태에 상관없이 안정된 상태 추정 성능을 제공할 수 있는 가상 모델 확장형 칼만 필터를 제안한다. 모의실험 및 실제 도로 환경에서의 실험 결과는 제안한 방법이 기존 추정 필터들에 비하여, 다양한 기동 상태에서 안정된 추정 성능과 향상된 연산시간을 제공한다는 것을 보여준다.