• International Journal of Ocean System Engineering
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• 제2권4호
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• pp.223-232
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• 2012

For the accurate estimation of the position and orientation of a UUV (unmanned underwater vehicle), a low-cost AHRS (attitude heading reference system) was developed using a low-cost IMU (inertial measurement unit) sensor which provides information on the 3D acceleration, 3D turning rate and 3D earth-magnetic field data in the object coordinate system. The main hardware system is composed of an IMU sensor (ADIS16405) and TMS320F28335, which is coded with an extended kalman filter algorithm with a 50-Hz sampling frequency. Through an experimental gimbal device, good estimation performance for the pitch, roll, and yaw angles of the developed AHRS was verified by comparing to those of a commercial AHRS called the MTi system. The experimental results are here presented and analyzed.

• 제어로봇시스템학회논문지
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• 제2권4호
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• pp.330-336
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• 1996

An real-time active beacon localization system for mobile robots is developed and implemented. This system permits the estimation of robot positions when detecting light sources by PSD(Position Sensitive Detector) sensor which are placed sparsely over the robots work space as beacons(or landmarks). An LSE(Least Square Estimation) method is introduced to calibrate the internal parameters of a model for the beacon and robot position. The proposed system has two operational modes of position estimation. One is the initial position calculation by the detection of two or more light sources positions of which are known. The other is the continuous position compensation that calculates the position and heading of the robot using the IEKF(Iterated Extended Kalman Filter) applied to the beacon and dead-reckoning data. Practical experiments show that the estimated position obtained by this system is precise enough to be useful for the navigation of robots.

• 제어로봇시스템학회논문지
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• 제21권9호
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• pp.863-868
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• 2015

In this paper, the vibration of a single-wheel mobile robot is minimized by designing a filter. An AHRS (Attitude and heading reference system) sensor is used for measuring the state of the robot. The measured signals are analyzed using the FFT method to investigate the fundamental vibrational frequency with respect to the flywheel's speed of the gimbal system. The IIR notch filter is then designed to suppress the vibration at the identified frequency. After simulating the performance of the designated filter using the measured sensor data through extensive experiments, the filter is actually implemented in a single-wheel mobile robot, GYROBO. Finally, the performance of the designed filter is confirmed by performing the balancing control task of the GYROBO system.

• 제어로봇시스템학회논문지
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• 제16권11호
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• pp.1110-1116
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• 2010

This paper present an effective complementary filtering method using encoder and gyro sensors for the self-localization(including heading and velocity) of indoor mobile robot. The main idea of the proposed approach is to find the pros and cons of each sensor through a various maneuvering tests and to design of an adaptive complementary filter that works for the entire maneuvering phases. The proposed method is applied to an indoor mobile robot and the performances are verified through extensive experiments.

• 한국도로학회논문집
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• 제13권2호
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• pp.175-185
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• 2011

도로기하구조정보는 도로의 안전성평가 및 도로의 유지관리를 위한 필수적인 요소이다. 본 연구에서는 GPS(Global Positioning System)/INS(Inertial Navigation System)센서가 탑재된 조사차량을 이용하여 기하구조정보를 수집하였으며, 수집된 차량의 자세정보 중 평면선형과 관련된 Roll, Heading 자료를 이용하여 직선, 원곡선, 완화곡선을 구분하는 알고리즘을 개발하였다. 본 연구에서는 평면선형 인식 이전에 전처리 과정으로 이동평균법을 통하여 자료를 평활화함으로써 원시자료의 이상치를 제거하여 평면선형 인식의 신뢰성을 제고하였다. 유전알고리즘(GA, Genetic Algorithm)을 이용하여 분류정확도(CCR, Correct Classification Rate)를 최대로 하는 알고리즘 파라미터를 설정한 결과 100%의 분류정확도를 보였다. 설정된 파라미터를 이용하여 고속도로와 국도 주행자료를 이용하여 알고리즘을 평가한 결과 90.48%와 88.24%의 분류정확도를 보여, 제안된 평면선형인식 알고리즘은 현장에서 적용 시 높은 신뢰도를 가지는 정보를 제공 가능한 것으로 분석되었다. 본 연구에서 개발한 평면선형인식 알고리즘은 조사차량에 GPS/INS센서의 소프트웨어로 탑재되어 도로 및 교통기술자에게 도로기하구조정보를 보다 용이하게 수집하고 분석할 수 있는 환경을 제공하는데 기여할 것으로 기대된다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.1034-1039
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• 2005

SALM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important tasks in mobile robot research. Until now expensive sensors such as a laser sensor have been used for mobile robot localization. Currently, the proliferation of RFID technology is advancing rapidly, while RFID reader devices, antennas and tags are becoming increasingly smaller and cheaper. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used for identifying location of the mobile robot in the smart floor. We discuss a number of challenges related to this approach, such as tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, the localization error results from the sensing area of the RFID reader, because the reader just knows whether the tag is in the sensing range of the sensor and, until now, there is no study to estimate the heading of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. The Markov localization method is used to reduce the location(X,Y) error and the Kalman Filter method is used to estimate the heading($\theta$) of mobile robot. The algorithms which are based on Markov localization require high computing power, so we suggest fast Markov localization algorithm. Finally we applied these algorithms our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors such as odometers and RFID tags for mobile robot localization in the smart floor

• 한국측량학회:학술대회논문집
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• 한국측량학회 2006년도 춘계학술발표회 논문집
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• pp.77-81
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• 2006

GPS cannot determine random errors such as multipath and signal cutoff caused by surrounding environment that determines the visibility of satellites and the speed of data creation and transmission is lower than the speed of vehicles, it is difficult to determine accurate dynamic positions. Thus this study purposed to implement a method of deciding the accurate dynamic position of vehicles by combining AHRS (Attitude Heading Reference System) IMU (Initial Measurement Unit) based on low-priced MEMS (Micro Electro Mechanical System) in order to provide the information of attitude, position and speed at a high transmission rate without external help. This study conducted an initialization test to decide dynamic position using AHRS IMU sensor, and derived attitude correction angles of vehicles against time through regression analysis. The roll angle was $y=(A{\times}10^{-6})x^2 -(B{\times}10^{-5})x+Cr{\times}10^{-2}$ and the pitch angle was $y=(A{\times}10^{-6})x^2-(B{\times}10^{-7})x+C{\times}10^{-2}$, each of which was derived from second-degree polynomial regression analysis. It was also found that the heading angle was stabilized with variation less than $1^{\circ}$ after 60 seconds.

• 한국음향학회지
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• 제20권8호
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• pp.74-80
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• 2001

가늘고 유연한 선배열을 해상에서 운용할 때 비선형 형상이 유도되므로 음원에 대한 정확한 탐지를 위하여 배열형상 추정이 필요하다. 방위센서를 이용한 배열형상 추정을 위하여 배열의 휜 정도가 작은 경우에만 적용 가능한 다항 근사화 방법의 제한점을 극복하기 위하여 반복법을 제안하고, 수치 시뮬레이션을 통하여 반복회수에 따른 배열형상 추정결과를 분석하였으며, 제안한 방법의 실제 시스템에 대한 적용성을 검토하였다.

• 한국항공우주학회지
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• 제39권2호
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• pp.128-136
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• 2011

뱅크턴을 수행하는 항공기에 운용되는 관성항법장치의 특정 자이로 비정렬 오차가 수직축 자세오차를 연속적으로 증가시키는 현상에 대해서 분석을 수행하고, 이 자이로 비정렬 오차를 제거하기 위해 GPS을 결합한 INS/GPS 시스템에 대해서 새로운 비정렬 모델링 방법을 제시하고, 그 성능 향상을 시뮬레이션을 통해 제시한다.

• 제어로봇시스템학회논문지
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• 제10권7호
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• pp.625-633
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• 2004

In this paper, position control of a car-like mobile robot using a neural network is presented. positional information of the mobile robot is given by a laser range finder located remotely through wireless communication. The heading angle is measured by a gyro sensor. Considering these two sensor information as a reference, the robot posture is corrected by a cascaded controller. To improve the tracking performance, a neural network with a cascaded controller is used to compensate for any uncertainty in the robot. The neural network functions as a compensator to minimize the positional errors in on-line fashion. A car-like mobile robot is built as a test-bed and experimental studies of several controllers are conducted and compared. Experimental results show that the best position control performance can be achieved by a cascaded controller with a neural network.