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GPS 정보와 차선정보의 정합을 통한 이동로봇의 실외 위치추정
지용훈(Yong-Hoon Ji),배지훈(Ji-Hun Bae),송재복(Jae-Bok Song),유재관(Jae-Kwan Ryu),백주관(Joo-Hyun Baek) 제어로봇시스템학회 2012 제어·로봇·시스템학회 논문지 Vol.18 No.6
Accurate localization is very important to stable navigation of a mobile robot. This paper deals with local localization of a mobile robot especially for outdoor environments. The GPS information is the easiest way to obtain the outdoor position information. However, the GPS accuracy can be severely affected by environmental conditions. To deal with this problem, the GPS and wheel odometry can be combined using an EKF (Extended Kalman Filter). However, this is not enough for safe navigation of a mobile robot in outdoor environments. This paper proposes a novel method using lane features from the road image. The pose data of a mobile robot can be corrected by analyzing the detected lane features. This can improve the accuracy of the localization process substantially.
실외 이동로봇의 고도지도 기반의 전역 위치추정을 위한 Hausdorff 거리 정합 기법
지용훈(Yong-Hoon Ji),송재복(Jea-Bok Song),백주현(Joo-Hyun Baek),유재관(Jae-Kwan Ryu) 제어로봇시스템학회 2011 제어·로봇·시스템학회 논문지 Vol.17 No.9
Mobile robot localization is the task of estimating the robot pose in a given environment. This research deals with outdoor localization based on an elevation map. Since outdoor environments are large and contain many complex objects, it is difficult to robustly estimate the robot pose. This paper proposes a Hausdorff distance-based map matching method. The Hausdorff distance is exploited to measure the similarity between extracted features obtained from the robot and elevation map. The experiments and simulations show that the proposed Hausdorff distance-based map matching is useful for robust outdoor localization using an elevation map. Also, it can be easily applied to other probabilistic approaches such as a Markov localization method.
지용훈,이종성 대한설비관리학회 1998 대한설비관리학회지 Vol.3 No.1
Heuristic rules(sensitizing rules) provide effective decisions for detecting nonrandom patterns on the control chart. To implement these rules sucessfully, it forces user to pay careful attention to sample point patterns. Furthermore, The ability to interpret a particular pattern requires experience and knowledge of the process. This paper provides neural network approach recognize the unnatural pattern on the control chart automatically as well as to heighten the sensitivity of the process change. The proposed method consists of Two-Step Neural Network Control Chart(NNCC) to perform the (원문참조)-R chart's function simultaneously. The first step NNCC detects the change of the process by the traditional neural network approach. The second step NNCC detects the change of the process by learning heuristic rules. The proposed method's performance is compared with traditional Shewhart (원문참조) - R chart in terms of type I & Ⅱ error. Two-step NNCC's errors are calculated by the simulation result.
품질을 고려한 3-stage flow line의 수율 근사계산
이종성,지용훈 江原大學校 産業技術硏究所 1996 産業技術硏究 Vol.16 No.-
This paper develops an algorithm for throughput of a 3~stage flow line with job inspection stations, limited buffer capacity, and exponential provessing times. Each stage consists of a single workstation, and an infinite number of jobs always waits in front of the first workstation, Blocking may occur when a processed job is waiting at one workstation for another workstation to become available. Numerical example results provide insights into the problems related to quality inspection and measure of performance of flow lines.