• Journal of Platform Technology
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• 제6권4호
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• pp.18-25
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• 2018

This paper describes a real-time human and robot tracking method in Intelligent Space with multi-camera networks. The proposed method detects candidates for humans and robots by using the histogram of oriented gradients (HOG) feature in an image. To classify humans and robots from the candidates in real time, we apply cascaded structure to constructing a strong classifier which consists of many weak classifiers as follows: a linear support vector machine (SVM) and a radial-basis function (RBF) SVM. By using the multiple view geometry, the method estimates the 3D position of humans and robots from their 2D coordinates on image coordinate system, and tracks their positions by using stochastic approach. To test the performance of the method, humans and robots are asked to move according to given rectangular and circular paths. Experimental results show that the proposed method is able to reduce the localization error and be good for a practical application of human-centered services in the Intelligent Space.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1927-1932
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• 2003

In a previous study, the authors have proposed the Cooperative Collision Avoidance (CCA) method which enables mobile robots to cooperatively avoid collisions, by extending the concept of the Velocity Obstacle to multiple robot systems. The method introduced an evaluation function considering an operation objective so that each robot can choose the velocity which optimizes the function. As the evaluation function could be of an arbitrary type, this method is applicable to a wide variety of tasks. However, it complicates the optimization of the function especially in real-time. In addition, construction of the evaluation function requires an operation objective of the other robot which is very hard to obtain without communication. In this paper, the CCA method is improved considering such problems for implementation. To decrease computational costs, the previous method is simplified by introducing two essential assumptions. Then, by treating the desired direction of locomotion for each robot as the operation objective, an operation objective estimator which estimates the desired direction of the other robot is introduced. The only measurement required is the other robot's relative position, since the other information can be obtained through the estimation. Hence, communicational devices that are necessary for most other cooperative methods are not required. Moreover, mobile robots employing the method can avoid collisions with uncooperative robots or moving obstacles as well as with cooperative robots. Consequently, this improved method can be applied to general dynamic environments consisting of various mobile robots.

• 한국지능시스템학회논문지
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• 제13권2호
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• pp.180-185
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• 2003

본 논문은 장애물에 대한 사전 정보를 가지고 있지 않은 공간에서 장애물의 회피와 지정된 목표점으로 이동할 수 있는 자율이동로봇을 위한 지능제어 알고리즘을 제안하고, 제안된 제어기의 효용성을 실험을 통하여 검증을 한다. 제시하는 지능 제어기는 계층구조의 알고리즘으로써, 그 하부에 로봇이 목표에 도달하기 위한 퍼지 알고리즘과 주행 중 만날 수 있는 장애물들에 대한 회피를 수행하는 퍼지-뉴럴 알고리즘이 존재하고, 상부의 가중치 퍼지 알고리즘은 로봇이 이동하면서 만날 수 있는 여러 가지 상황에 따라서 하부의 두 알고리즘에 적당한 가중치를 부여하여 장애물 회피동작과 목표점 도달동작을 수행할 수 있도록 구성되어 있다. 그리고 로봇의 현재 운동정보와 장애물까지의 거리정보를 바탕으로 가중치 퍼지 알고리즘의 출력부 소속도 함수를 조절함으로서 오목한 장애물에 대해서도 장애물 회피 동작을 수행하도록 하였다. 제작된 로봇으로 제시한 알고리즘의 실효성을 검증하였다.

• 한국지능시스템학회논문지
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• 제23권1호
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• pp.46-50
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• 2013

본 논문은 다수의 구동기 고장을 가진 유연 관절 로봇의 적응 고장 수용 제어기를 제안한다. 다수의 구동기 고장의 크기와 일어나는 시간은 알려지지 않는다고 가정한다. 구동기 고장을 수용하기 위해 추종 오차의 수렴율과 오버슈트의 최대치로 특성화된 미리 정의된 성능을 보장하는 유계 조건을 갖는 적응 고장 수용 제어기를 설계한다. 르아프노브 안정도 이론을 이용하여 페루트 시스템의 모든 신호가 준 전역적이고 균일하고 궁극적으로 유계됨을 증명하고 추종 오차가 미리 정의된 성능 유계에서 벗어나지 않음을 보인다.

• 로봇학회논문지
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• 제9권1호
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• pp.1-10
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• 2014

In recent years, the research of 3D mapping technique in urban environments obtained by mobile robots equipped with multiple sensors for recognizing the robot's surroundings is being studied actively. However, the map generated by simple integration of multiple sensors data only gives spatial information to robots. To get a semantic knowledge to help an autonomous mobile robot from the map, the robot has to convert low-level map representations to higher-level ones containing semantic knowledge of a scene. Given a 3D point cloud of an urban scene, this research proposes a method to recognize the objects effectively using 3D graph model for autonomous mobile robots. The proposed method is decomposed into three steps: sequential range data acquisition, normal vector estimation and incremental graph-based segmentation. This method guarantees the both real-time performance and accuracy of recognizing the objects in real urban environments. Also, it can provide plentiful data for classifying the objects. To evaluate a performance of proposed method, computation time and recognition rate of objects are analyzed. Experimental results show that the proposed method has efficiently in understanding the semantic knowledge of an urban environment.

• 제어로봇시스템학회논문지
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• 제17권10호
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• pp.995-1005
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• 2011

An efficient outdoor local localization method is suggested using multi-sensor fusion with MU-EKF (Multi-Update Extended Kalman Filter) for car-like mobile robots. In outdoor environments, where mobile robots are used for explorations or military services, accurate localization with multiple sensors is indispensable. In this paper, multi-sensor fusion outdoor local localization algorithm is proposed, which fuses sensor data from LRF (Laser Range Finder), Encoder, and GPS. First, encoder data is used for the prediction stage of MU-EKF. Then the LRF data obtained by scanning the environment is used to extract objects, and estimates the robot position and orientation by mapping with map objects, as the first update stage of MU-EKF. This estimation is finally fused with GPS as the second update stage of MU-EKF. This MU-EKF algorithm can also fuse more than three sensor data efficiently even with different sensor data sampling periods, and ensures high accuracy in localization. The validity of the proposed algorithm is revealed via experiments.

• 문화기술의 융합
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• 제7권2호
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• pp.345-350
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• 2021

넓은 지역에 설치되는 센서 네트워크 시스템은 유지 관리가 문제가 되어 왔으며, 이를 해결하기 위해 로봇을 사용하여 센서 네트워크에 에너지를 공급하려는 연구가 여러 연구자에 의해 수행되었다. 이 연구에서는 전력을 공급하는 노드들과 그 주변의 센서 노드들로 이루어진 센서 네트워크에 여러 대의 로봇을 사용하여 에너지를 공급하는 문제에서, 로봇이 최소 거리를 이동하도록 수정된 k-means 알고리즘을 사용하여 각 로봇이 작업할 영역을 분할하는 방법을 제안한다. 로봇의 에너지 전달률을 변수로 한 시뮬레이션 실험을 통해 분할된 각 영역의 센서 노드들이 동작을 유지할 수 있음을 보임으로써 제안한 수정 k-means 알고리즘의 타당성을 검증한다.

• 로봇학회논문지
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• 제17권1호
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• pp.25-31
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• 2022

Moving among multiple floors is one of the most challenging tasks for indoor autonomous robots. Most of the previous researches for indoor mapping and localization have focused on singular floor environment. In this paper, we present an algorithm that creates a multi-floor map using 3D point cloud. We implement localization within the multi-floor map using a LiDAR and an IMU. Our algorithm builds a multi-floor map by constructing a single-floor map using a LOAM-based algorithm, and stacking them through global registration that aligns the common sections in the map of each floor. The localization in the multi-floor map was performed by adding the height information to the NDT (Normal Distribution Transform)-based registration method. The mean error of the multi-floor map showed 0.29 m and 0.43 m errors in the x, and y-axis, respectively. In addition, the mean error of yaw was 1.00°, and the error rate of height was 0.063. The real-world test for localization was performed on the third floor. It showed the mean square error of 0.116 m, and the average differential time of 0.01 sec. This study will be able to help indoor autonomous robots to operate on multiple floors.

• 로봇학회논문지
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• 제11권3호
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• pp.127-132
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• 2016

For a practical mobile robot team such as carrying out a search and rescue mission in a disaster area, the localization have to be guaranteed even in an environment where the network infrastructure is destroyed or a global positioning system (GPS) is unavailable. The proposed architecture supports localizing robots seamlessly by finding their relative locations while moving from a global outdoor environment to a local indoor position. The proposed schemes use a cooperative positioning system (CPS) based on the two-way ranging (TWR) technique. In the proposed TWR-based CPS, each non-localized mobile robot act as tag, and finds its position using bilateral range measurements of all localized mobile robots. The localized mobile robots act as anchors, and support the localization of mobile robots in the GPS-shadow region such as an indoor environment. As a tag localizes its position with anchors, the position error of the anchor propagates to the tag, and the position error of the tag accumulates the position errors of the anchor. To minimize the effect of error propagation, this paper suggests the new scheme of full-mesh based CPS for improving the position accuracy. The proposed schemes assuring localization were validated through experiment results.

• 한국정보통신학회논문지
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• 제19권1호
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• pp.26-34
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• 2015

본 논문에서는 이동 로봇의 군집 제어를 위해 퍼지 보상된 PID 제어 시스템을 제안한다. 제어 시스템은 선도-추종기법에 기반한 기구학 제어기와 이동 로봇의 동역학적 영향을 고려한 동역학 제어기로 구성되어 있다. 이동 로봇의 대형 유지를 위해 동역학 제어기는 PID제어기로 구성되었다. 하지만 PID 제어기는 비선형 또는 환경 변화에 취약점을 가진다. 이러한 문제를 보완하기 위해 퍼지 보상기를 추가하였다. 마지막으로 개선된 성능을 보이기 위해 컴퓨터 시뮬레이션을 통해 제안된 제어기를 평가하였다.