• Journal of Astronomy and Space Sciences
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• 제30권4호
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• pp.279-290
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• 2013

Due to the continuous space development by mankind, the number of space objects including space debris in orbits around the Earth has increased, and accordingly, difficulties of space development and activities are expected in the near future. In this study, among the stages for space debris removal, the implementation of a vision-based approach technique for approaching space debris from a far-range rendezvous state to a proximity state, and the ground test performance results were described. For the vision-based object tracking, the CAM-shift algorithm with high speed and strong performance, and the Kalman filter were combined and utilized. For measuring the distance to a tracking object, a stereo camera was used. For the construction of a low-cost space environment simulation test bed, a sun simulator was used, and in the case of the platform for approaching, a two-dimensional mobile robot was used. The tracking status was examined while changing the position of the sun simulator, and the results indicated that the CAM-shift showed a tracking rate of about 87% and the relative distance could be measured down to 0.9 m. In addition, considerations for future space environment simulation tests were proposed.

• 한국항해항만학회지
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• 제35권2호
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• pp.121-130
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• 2011

The localization of vehicle is an important part of an unmanned vehicle control problem. Pseudolite ultrasonic system(PUS) is the method to find an absolute position with a high accuracy by using ultrasonic sensor. And Gyro is the inertial sensor to measure yaw angle of vehicle. PUS can be able to estimate the position of mobile robot precisely, in which errors are not accumulated. And Gyro is a more faster measure method than PUS. In this paper, we suggest a more accuracy method of calculating PUS which is numerical analysis approach named Newtonian method. And also propose the fusion method to increase the accuracy of estimated angle on moving vehicle by using PUS and Gyro integrated system by Kalman filtering. To control the 4WS unmanned vehicle, the trajectory following algorithm is suggested. And the new concept arbitration of goal controller is suggested. This method considers the desirability function of vehicle state. Finally, the performances of Newtonian method and designed controller were verified from the experimental results with the 4WS vehicle scaled 1/10.

• 한국산학기술학회논문지
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• 제16권6호
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• pp.3714-3721
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• 2015

서셉터 히터에서 쉬스 열선을 사용하는 방법이 일반화되어 있지만, 대면적 초고온 조건에서는 서셉터의 온도 균일도 성능 저하의 문제가 있다. 본 연구에서는 온도균일도 성능을 향상시킬 수 있도록 판형 형태의 열선을 기본으로 새로운 서셉터를 설계하여 프로토타입을 개발하였다. 표면 온도 $450^{\circ}C$의 고온에서 1.4% 이내로 온도 균일도가 시제작된 서셉터에서 검증될 수 있었다. 또한 온도 학습 데이터를 이용하여 측정 온도 데이터를 예측할 수 있는 커널 회귀 알고리즘을 개발하고, 이러한 예측 데이터와 측정 데이터의 비교 분석으로 균일도 측정 온도의 신뢰성을 확인할 수 있었다.

• 한국멀티미디어학회논문지
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• 제19권9호
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• pp.1647-1658
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• 2016

RGB+D database has been widely used in object recognition, object tracking, robot control, to name a few. While rapid advance of active depth sensing technologies allows for the widespread of indoor RGB+D databases, there are only few outdoor RGB+D databases largely due to an inherent limitation of active depth cameras. In this paper, we propose a novel method used to build outdoor RGB+D databases. Instead of using active depth cameras such as Kinect or LIDAR, we acquire a pair of stereo image using high-resolution stereo camera and then obtain a depth map by applying stereo matching algorithm. To deal with estimation errors that inevitably exist in the depth map obtained from stereo matching methods, we develop an approach that estimates confidence of depth maps based on unsupervised learning. Unlike existing confidence estimation approaches, we explicitly consider a spatial correlation that may exist in the confidence map. Specifically, we focus on refining confidence feature with the assumption that the confidence feature and resultant confidence map are smoothly-varying in spatial domain and are highly correlated to each other. Experimental result shows that the proposed method outperforms existing confidence measure based approaches in various benchmark dataset.

• 한국지능시스템학회논문지
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• 제17권5호
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• pp.701-706
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• 2007

최근 고층건물의 방진시스템 및 자동차의 현가장치 분야에 MR 유체를 이용한 반능동 진동제어에 관한 연구가 활발히 진행되면서 MR 유체의 밸브모드 특성을 이용한 선형 MR 댐퍼가 널리 사용되고 있다. 그러나 전단모드를 이용한 회전형 MR 댐퍼에 관한 연구는 초기단계에 머물고 있다. 본 연구에서는, 주행로봇의 무한회전형 액츄에이터용 회전형 댐퍼의 진동제어에 관한 연구를 수행하였으며, 퍼지 로직을 기반으로 한 회전형 MR 댐퍼용 진동제어 알고리즘을 제안하였다. 퍼지제어기의 입출력 이득과 멤버쉽 함수의 모양은 유전자 알고리즘을 이용하여 조정하였다. 시뮬레이션 결과는 MR 댐퍼에 충격력이 가해질 때 퍼지 스카이-훅 제어의 제어성능을 보여준다.

• 한국정보통신학회논문지
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• 제11권10호
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• pp.1977-1985
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• 2007

문자 인식은 인공지능의 한 분야로써 자동화 시스템, 로봇, HCI 분야에서 그 응용성 이 증대되고 있는 첨단 기술이다. 본 논문에서는 숫자, 기호, 영어, 한글이 여러 가지 형태로 조합되어 사용될 수 있는 영역에서의 문자 인식을 위해 인식 문자 집합과 대표 문자를 도입하였다. 여러 가지 조합의 언어 집합에 따른 소규모 인식기를 계층적으로 조합하여 인식 결과의 정확성을 높이고 시간 비용을 줄일 수 있는 효율적인 인식기 구조를 제안하였다. 그리고 학습 성능이 우수한 Delta-bar-delta 알고리즘을 이용하여 개별 소규모 인식기를 학습한 다음 다양한 개별 문자를 대상으로 그 인식 성능을 살펴본 결과 99%의 인식률을 획득함으로써 혼용 언어 문자 인식의 효율성과 신뢰성을 증명하였다.

• 한국반도체및디스플레이장비학회:학술대회논문집
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• 한국반도체및디스플레이장비학회 2005년도 추계 학술대회
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• pp.110-123
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• 2005

This paper proposes an intelligent method for the optimal load distribution of two cooperating robots(TCRs) using fuzzy logic. The proposed scheme requires the knowledge of the robots' dynamics, which in turn depend upon the characteristics of large flat panel displays(LFPDs) carried by the TCRs. However, the dynamic properties of the LFPD are not known exactly, so that the dynamics of the robots, and hence the required joint torque, must be calculated for nominal set of the LFPD characteristics. The force of the TCRs is an important factor in carrying the LFPD. It is divided into external force and internal force. In general , the effects of the internal force of the TCRs are not considered in performing the load distribution in terms of optimal time, but they are essential in optimal trajectory planning: if they are not taken into consideration, the optimal scheme is no longer fitting. To alleviate this deficiency, we present an algorithm for finding the internal-force factors for the TCRs in terms of optimal time. The effectiveness of the proposed system is demonstrated by computer simulations using two three-joint planner robot manipulators.

• 한국측량학회지
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• 제37권2호
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• pp.45-53
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• 2019

RGB-D SLAM (Simultaneous Localization and Mapping) refers to the technology of using deep camera as a visual sensor for SLAM. In view of the disadvantages of high cost and indefinite scale in the construction of maps for laser sensors and traditional single and binocular cameras, a method for creating three-dimensional map of indoor environment with deep environment data combined with RGB-D SLAM scheme is studied. The method uses a mobile robot system equipped with a consumer-grade RGB-D sensor (Kinect) to acquire depth data, and then creates indoor three-dimensional point cloud maps in real time through key technologies such as positioning point generation, closed-loop detection, and map construction. The actual field experiment results show that the average error of the point cloud map created by the algorithm is 0.0045m, which ensures the stability of the construction using deep data and can accurately create real-time three-dimensional maps of indoor unknown environment.

• 로봇학회논문지
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• 제16권3호
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• pp.179-188
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• 2021

Construction monitoring is one of the key modules in smart construction. Unlike structured urban environment, construction site mapping is challenging due to the characteristics of an unstructured environment. For example, irregular feature points and matching prohibit creating a map for management. To tackle this issue, we propose a system for data acquisition in unstructured environment and a framework for Intensity and Ambient Enhanced Lidar Inertial Odometry via Smoothing and Mapping, IA-LIO-SAM, that achieves highly accurate robot trajectories and mapping. IA-LIO-SAM utilizes a factor graph same as Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (LIO-SAM). Enhancing the existing LIO-SAM, IA-LIO-SAM leverages point's intensity and ambient value to remove unnecessary feature points. These additional values also perform as a new factor of the K-Nearest Neighbor algorithm (KNN), allowing accurate comparisons between stored points and scanned points. The performance was verified in three different environments and compared with LIO-SAM.

• 로봇학회논문지
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• 제17권4호
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• pp.417-424
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• 2022

In this paper, the developed trunk cargo unloading automation system is introduced, and the RGB-D sensor-based box loading situation recognition method and unloading plan applied to this system are suggested. First of all, it is necessary to recognize the position of the box in a truck. To do this, we first apply CNN-based YOLO, which can recognize objects in RGB images in real-time. Then, the normal vector of the center of the box is obtained using the depth image to reduce misrecognition in parts other than the box, and the inner wall of the truck in an image is removed. And a method of classifying the layers of the boxes according to the distance using the recognized depth information of the boxes is suggested. Given the coordinates of the boxes on the nearest layer, a method of generating the optimal path to take out the boxes the fastest using this information is introduced. In addition, kinematic analysis is performed to move the conveyor to the position of the box to be taken out of the truck, and kinematic analysis is also performed to control the robot arm that takes out the boxes. Finally, the effectiveness of the developed system and algorithm through a test bed is proved.