• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 학술대회 논문집 정보 및 제어부문
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• pp.85-86
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• 2008

로봇은 오도메터리 정보를 이용해 위치추정을 할 수 있다. 그러나 주행하는 동안 발생되는 슬립현상에 의해 오도메터러 정보만으로는 로봇의 정확한 위치추정을 할 수 없다. 정확한 위치추정을 위해서 관성센서를 이용하여 오도메터리 정보를 보정한 위치추정 방법이 있다. 실내 이동로봇에 적용하려면 관성센서는 소형이어야 하는데, 그에 따라 노이즈는 심해지고, 정확성도 낮아지는 문제가 있나. 그래서 현재까지는 이런 문제를 갖고 있는 관성센서를 실내 이동로봇의 위치추정의 정확성을 높이기 위해 비관성센서 또는 카메라 영상을 조합하는 연구들을 하고 있다. 그러나 이러한 연구들은 대부분 관성센서 성능 실험과 시뮬레이션에 결론을 내리고 있어 실제 실험에 따른 정확성을 확인할 수 없다. 또한 최근 영상 SIFT 알고리즘을 적용한 SLAM 연구에서도 나타나는 문제는 이동로봇의 위치추정의 부정확성이다. 따라서 본 논문은 SLAM에서 문제가 되는 위치추정의 부정확성을 최소화하기 위해 자이로와 가속도계를 이용하여 정학한 위치추정을 하고자 한다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2016년도 춘계학술대회
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• pp.285-286
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• 2016

해상교통관제센터에 의해 실시간으로 수집되는 선박의 항해 데이터를 바탕으로 선박 항적 패턴 인식을 수행하고 이를 바탕으로 항적 모델을 추출하여 사전에 선위를 예측하는 기법을 제안한다. 항적 데이터의 처리와 가공, 항적 모델링을 위하여 Support Vector Regression 알고리즘이 사용되었으며, 적정 파라미터 선정을 위하여 k-fold cross validation과 grid search가 사용되었다. 제안된 항적 데이터 모델링 기법을 통하여 사전에 선박의 선위를 예측하여 해상교통과제사의 의사결정을 지원하고자 한다.

• 로봇학회논문지
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• 제15권1호
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• pp.16-23
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• 2020

This paper describes an alignment algorithm that estimates the initial heading angle of AUVs (Autonomous Underwater Vehicle) for starting navigation in a sea area. In the basic dead reckoning system, the initial orientation of the vehicle is very important. In particular, the initial heading value is an essential factor in determining the performance of the entire navigation system. However, the heading angle of AUVs cannot be measured accurately because the DCS (Digital Compass) corrupted by surrounding magnetic field in pointing true north direction of the absolute global coordinate system (not the same to magnetic north direction). Therefore, we constructed an experimental constraint and designed an algorithm based on extended Kalman filter using only inertial navigation sensors and a GPS (Global Positioning System) receiver basically. The value of sensor covariance was selected by comparing the navigation results with the reference data. The proposed filter estimates the initial heading angle of AUVs for navigation in a sea area and reflects sampling characteristics of each sensor. Finally, we verify the performance of the filter through experiments.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권3호
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• pp.148-155
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• 2017

본 논문에서는 스마트폰 내 가속도, 자기장, 자이로스코프 센서들을 이용해 사용자의 걸음과 걸음 수를 인식하는 시스템을 개발하였다. 센서 데이터 분석을 통해 사용자의 걸음을 스마트폰을 손에 든 상황과 주머니에 넣은 상황에서의 걸음 패턴으로 분류하고 이를 추출할 수 있는 알고리즘을 사용하여 걸음 수 인식의 정확성을 개선하였다. 알고리즘을 적용한 결과 손에든 상황에서 96%, 주머니에 넣은 상황에서 95.5% 수준의 걸음 수 인식 정확도를 보였으며, 나머지 터치 스크린, 위아래 반복 흔들기, 앉아서 일어서기, 오른쪽 왼쪽 흔들기와 같은 행위로 인해 발생하는 6%의 오차를 확인하였다.

• Journal of Construction Engineering and Project Management
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• 제6권2호
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• pp.30-39
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• 2016

This research presents the development of a self-governing mobile robot navigation system for indoor construction applications. This self-governing robot navigation system integrated robot control units, various positioning techniques including a dead-reckoning system, a UWB platform and motion sensors, with a BIM path planner solution. Various algorithms and error correction methods have been tested for all the employed sensors and other components to improve the positioning and navigation capability of the system. The research demonstrated that the path planner utilizing a BIM model as a navigation site map could effectively extract an efficient path for the robot, and could be executed in a real-time application for construction environments. Several navigation strategies with a mobile robot were tested with various combinations of localization sensors including wheel encoders, sonar/infrared/thermal proximity sensors, motion sensors, a digital compass, and UWB. The system successfully demonstrated the ability to plan an efficient path for robot's movement and properly navigate through the planned path to reach the specified destination in a complex indoor construction site. The findings can be adopted to several potential construction or manufacturing applications such as robotic material delivery, inspection, and onsite security.

• Journal of Positioning, Navigation, and Timing
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• 제9권1호
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• pp.33-42
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• 2020

In Integration Approach (IA)-based Pedestrian Dead Reckoning (PDR), it is important to detect the exact zero-velocity of the foot with an Inertial Measurement Unit (IMU). By detecting zero-velocity during the stance phase of the foot touching the ground and executing Zero-velocity UPdaTe (ZUPT) at the exact time, stable navigation information can be provided by the PDR. When the pace is fast, however, it is not easy to accurately detect the zero-velocity because of the small stance phase interval and the large signal variance of the corresponding interval. Incorrect zero-velcity detection greatly causes navigation errors of IA-based PDR. In this paper, we propose a method to detect the zero-velocity stably even at high speed by novel buffering of IMU's output data and signal processing of the buffer. And we design a PDR based on this. By analyzing the performance of the proposed Zero-Velocity Detection (ZVD) algorithm and ZVD-based PDR through experiemnts, we confirm that the proposed method can provide accurate navigation information of pedestrians such as firefighters in the indoor space.

• Journal of Advanced Marine Engineering and Technology
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• 제39권5호
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• pp.522-526
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• 2015

Generally, underwater unmanned vehicle have adopted an inertial navigation system (INS), dead reckoning (DR), acoustic navigation and geophysical navigation techniques as the navigation method because GPS does not work in deep underwater environment. Even if the tactical inertial sensor can provide very detail measurement during long operation time, it is not suitable to use the tactical inertial sensor for small size and low cost UUV because the tactical inertial sensor is expensive and large. One alternative to INS is attitude heading reference system (AHRS) with the micro-machined electro mechanical system (MEMS) inertial sensor because of MEMS inertial sensor's small size and low power requirement. A cost effective and small size attitude heading reference system (AHRS) which incorporates measurements from 3-axis micro-machined electro mechanical system (MEMS) gyroscopes, accelerometers, and 3-axis magnetometers has been developed to provide a complete attitude solution for UUV. The AHRS based MEMS overcome many problems that have inhibited the adoption of inertial system for small UUV such as cost, size and power consumption. Several evaluation experiments were carried out for the validation of the developed AHRS's function and these experiments results are presented. Experiments results prove the fact that the developed MEMS AHRS satisfied the required specification.

• 제어로봇시스템학회논문지
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• 제13권9호
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• pp.903-909
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• 2007

Mobile Robots are increasingly being used to perform tasks in unknown environment. The potential of robots to undertake such tasks lies in their ability to intelligently and efficiently search in an environment. To achieve autonomous mobile robot navigation, efficient path planner and accurate localization technique are the fundamental issues that should be addressed. This paper presents mobile robot localization using IRID(InfraRed IDentification) as artificial landmarks. IRID has highly deterministic characteristics, different from RFID. By putting several IRID emitters on the ceiling, the floor is divided into many different sectors and each sector is set to have a unique identification. Dead-reckoning provides the estimated robot configuration but the error becomes accumulated as the robot travels. IRID information tells the sector the robot is in, but the size of the uncertainty is too large if only the IRID information is used. This paper presents an algorithm which combines both the encoder and the IRID information so that the size of the uncertainty becomes smaller. It also introduces a framework which can be used with other types of the artificial landmarks. The characteristics of the developed IRID and the proposed algorithm are verified from the simulation results and experiments.

• 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.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2006년 창립20주년기념 정기학술대회 및 국제워크샵
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• pp.339-346
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• 2006

Increased usage of autonomous underwater vehicle (AUV) has led to the development of alternative navigational methods that do not employ the acoustic beacons and dead reckoning sensors. This paper describes a simultaneous localization and mapping (SLAM) scheme that uses range sonars mounted on a small AUV. The SLAM is one of such alternative navigation methods for measuring the environment that the vehicle is passing through and providing relative position of AUV by processing the data from sonar measurements. A technique for SLAM algorithm which uses several ranging sonars is presented. This technique utilizes an unscented Kalman filter to estimate the locations of the AUV and objects. In order for the algorithm to work efficiently, the nearest neighbor standard filter is introduced as the algorithm of data association in the SLAM for associating the stored targets the sonar returns at each time step. The proposed SLAM algorithm is tested by simulations under various conditions. The results of the simulation show that the proposed SLAM algorithm is capable of estimating the position of the AUV and the object and demonstrates that the algorithm will perform well in various environments.