• 제어로봇시스템학회논문지
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• 제19권9호
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• pp.797-804
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• 2013

In this paper, a study of a new hovering six dof underwater robot with redundant horizontal thrusters, titled HAUV (hovering AUV), is presented. The results of study on the structure design, deployment of thrusters, and development of the developed control system of the AUV was presented. For the HAUV structure, a structure design and an analysis of the thrusting system was performed. For navigation, a sensor fusion board which can proceed various sensor signals to identify correct positions and speeds was developed and a total control system including EKF (Extended Kalman Filter) was designed. Rolling, pitching and depth control tests of the HAUV have been performed, and relatively small angle error and depth tracking error results were shown.

• 한국군사과학기술학회지
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• 제14권4호
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• pp.684-693
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• 2011

Measurement time delay in the transfer alignment is very important. It has been well known that the time delay degrades the alignment performance and makes some navigation errors on the transfer alignment of slave INS(SINS). Therefore there are many schemes to eliminate that time delay but the compensation technique through the estimation by Kalman filter through modeling the time delay as a random constant is generally used. In the case of change over measurement time delay or the large measurement time delay, estimation performance in the existing compensation technique is degraded because model of time delay is not correct any more. In this paper, we propose the method to keep the time delay almost constant even though in the abnormal communication state and very small through feedback compensation using double buffer. Double buffer consists of two moving window to temporarily store measurements from master INS and slave INS in real time.

• 제어로봇시스템학회논문지
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• 제12권10호
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• pp.955-961
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• 2006

The global ultrasonic system for the self-localization of a mobile robot consists of several ultrasonic transmitters fixed at some reference positions in the global coordinates of robot environment. By activating the ultrasonic transmitters, the mobile robot is able to get the distance to the ultrasonic transmitters and compute its own position in the global coordinate. Due to the limitation on the ultrasonic signal strength and beam width as well as the environmental obstacles however, the ultrasonic signals from some generator may not be transmitted to the robot. Thus, instead of activating the all ultrasonic transmitters, it is necessary to select some ultrasonic generators to activate based on the current robot position. In this paper, we propose a selective activation algorithm for self-localization with the global ultrasonic system. The selective activation algorithm gets the meaningful ultrasonic data at every sampling instants, which results in the faster and more accurate response of the self-localization than the conventional sequential activation. Through the self-localization and path following control, we verify the effectiveness of the proposed selective activation algorithm.

• 한국측량학회지
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• 제32권6호
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• pp.599-606
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• 2014

Since the accuracy of Global Positioning System (GPS)-based vehicle positioning system is significantly degraded or does not work appropriately in the urban canyon, the integration techniques of GPS with Inertial Navigation System (INS) have intensively been developed to improve the continuity and reliability of positioning. However, its accuracy is degraded as INS errors are not properly corrected due to the GPS signal blockage. Recently, the image-based positioning techniques have been started to apply for the vehicle positioning for the advanced in processing techniques as well as the increased the number of cars installing the camera. In this study, Single Photo Resection (SPR), which calculates the camera exterior orientation parameters using the Ground Control Points (GCPs,) has been integrated with the INS/GPS for continuous and stable positioning. The INS/GPS/SPR integration was implemented in both of a loosely and a tightly coupled modes, based on the Extended Kalman Filter (EKF). In order to analyze the performance of INS/SPR integration during the GPS outage, the simulation tests were conducted with a consideration of factors affecting SPR performance. The results demonstrate that the accuracy of INS/SPR integration is depended on magnitudes of the GCP errors and SPR processing intervals. Additionally, the simulation results suggest some required conditions to achieve accurate and continuous positioning, used the INS/SPR integration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권11호
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• pp.4103-4117
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• 2014

Image processing and computer vision algorithms are gaining larger concern in a variety of application areas such as robotics and man-machine interaction. Vision allows the development of flexible, intelligent, and less intrusive approaches than most of the other sensor systems. In this work, we determine the location and orientation of a mobile robot which is crucial for performing its tasks. In order to be able to operate in real time there is a need to speed up different vision routines. Therefore, we present and evaluate a method for introducing parallelism into the multiple non-overlapping camera pose estimation algorithm proposed in [1]. In this algorithm the problem has been solved in real time using multiple non-overlapping cameras and the Extended Kalman Filter (EKF). Four cameras arranged in two back-to-back pairs are put on the platform of a moving robot. An important benefit of using multiple cameras for robot pose estimation is the capability of resolving vision uncertainties such as the bas-relief ambiguity. The proposed method is based on algorithmic skeletons for low, medium and high levels of parallelization. The analysis shows that the use of a multiprocessor system enhances the system performance by about 87%. In addition, the proposed design is scalable, which is necaccery in this application where the number of features changes repeatedly.

• 대한조선학회 특별논문집
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• 대한조선학회 2008년도 특별논문집
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• pp.68-75
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• 2008

This paper describes the design and implementation of a low cost inertial navigation system(INS) using an inertial measurement unit(IMU), a digital compass, GPS, and an embedded system. The system has been developed for a transporter that load and unload ship blocks in a shipbuilding yard. When the transporter would move from place to place, they would periodically pass under obstructions that would obscure the GPS signal. This increases the error when estimating the position. Thus the INS has been used to improve position accuracy. INS is also capable of providing continuous estimates of the transporter's position and orientation. Even though IMU is typically very expensive, this INS is made of "low cost" components and the indirect Kalman filtering algorithm.

• 대한공간정보학회지
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• 제16권3호
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• pp.71-78
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• 2008

현재 해양 측량 및 선박항해 목적으로 음향측심기가 사용되어지고 있다. 하지만 신뢰성 있는 수심인식이 주 목적인 음향측심기가 낮은 수심 또는 험한 해저 지형에서 수심 인식률이 현저히 떨어지는 문제점이 있다. 이를 해결하기 위해 트랜듀서의 음파특성 파악 및 최적 알고리즘을 연구를 하였다. 알고리즘 연산처리를 하기 위해 최신 DSP프로세서(TMS320F2812)를 적용하여 고속으로 데이터처리를 하는 하드웨어 설계를 하였다. 마지막으로 기존방식과 최적 알고리즘을 적용한 방식을 비교하여 데이터 신뢰성을 비교하였다.

• 제어로봇시스템학회논문지
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• 제18권3호
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• pp.243-250
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• 2012

This paper proposes a 3D map-building algorithm using one LRF (Laser Range Finder) while a mobile robot is navigating on the slanted surface. There are several researches on 3D map buildings using the LRF. However most of them are performing the map building only on the flat surface. While a mobile robot is moving on the slanted surface, the view angle of LRF is dynamically changing, which makes it very difficult to build the 3D map using encoder data. To cope with this dynamic change of the view angle in build 3D map, IMU and balance filters are fused to correct the unstable encoder data in this research. Through the real navigation experiments, it is verified that the fusion of multiple sensors are properly performed to correct the slope angle of the slanted surface. The effectiveness of the balance filter are also checked through the hill climbing navigations.

• 한국정보통신학회논문지
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• 제11권10호
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• pp.1986-1991
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• 2007

본 논문에서 지구자기장을 측정하여 방위를 결정하는 자기 저항 소자의 경사각의 이상 현상에서 있어 예측할 수 없는 특성에 대해 불확실성에서의 추론을 통해 센서로서의 안정성을 확보하는데 그 목적이 있다. 따라서 퍼지 알고리즘을 적용하여 외부 환경 변화에 민감하게 변화하는 소자의 왜곡현상을 프로그래밍 적인 요소를 실험하여 센서의 왜율적인 요소에서 벗어남을 보인다. 나아가 소자의 고속, 고신뢰성을 갖는 응용에 사용할 수 있음을 보인다.

• 로봇학회논문지
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• 제14권2호
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• pp.87-93
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• 2019

This paper presents a 6-DOF relocalization using a 3D laser scanner and a monocular camera. A relocalization problem in robotics is to estimate pose of sensor when a robot revisits the area. A deep convolutional neural network (CNN) is designed to regress 6-DOF sensor pose and trained using both RGB image and 3D point cloud information in end-to-end manner. We generate the new input that consists of RGB and range information. After training step, the relocalization system results in the pose of the sensor corresponding to each input when a new input is received. However, most of cases, mobile robot navigation system has successive sensor measurements. In order to improve the localization performance, the output of CNN is used for measurements of the particle filter that smooth the trajectory. We evaluate our relocalization method on real world datasets using a mobile robot platform.