• 한국통신학회논문지
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• 제22권4호
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• pp.855-869
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• 1997

In this paper, we proposed two kids of dynamic priority control mechanisms controlling the cell service ratio in order to improve the QOS(Quality of Service). We also analyse theoretically the characteristics of cell loss probability and mean cell delay time by applying the proposed priority control mechanisms to ATM switch with output buffer. The proposed priority control mechanisms have the same principles of storing cells into buffer but the different principles of serving cells from buffer. The one is the control mechanism controlling the cell service ratio according to the relative cell occupancy ratio of buffer, the other is the control mechanism controlling the cell service ratio according to both the relative cell occupancy ratio of buffer and the average arrival rate. The two service classes of our concern are the delay sensitive class and the loss sensitive class. The analytical results show that the proposed control mechanisms are able to improve the QOS, the characteristics of cell loss probability and mean cell delay time, by selecting properly the relative cell occupancy ratio of buffer and the average arrival rate. conventional DLB algorithm does not support synchronous cells, but the proposed algorithm gives higher priority to synchronous cells. To reduce synchronous cell loss rate, the synchronous cell detector is used in the proposed algorithm. Synchronous cell detector detects synchronous cells, and passes them cells to the 2nd Leaky-Bucket. So it is similar to give higher priority to synchronous cells. In this paper, the proposed algorithm used audio/video traffic modeled by On/Off and Two-state MMPP, and simulated by SLAM II package. As simulation results, the proposed algorithm gets lower synchronous cell loss rate than the conventional DLB algorithms. The improved DLB algorithm for multimedia synchronization can be extended to any other cells which require higher priority.

• 대한원격탐사학회지
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• 제39권1호
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• pp.111-127
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• 2023

In visual slam and 3D data modeling, the Iterative Closest Point method is a primary fundamental algorithm, and many technical fields have used this method. However, it relies on search methods that take a high search time. This paper solves this problem by applying an effective point cloud refinement method. And this paper also accelerates the point cloud registration process with an indexing scheme using the spatial decomposition method. Through some experiments, the results of this paper show that the proposed point cloud refinement method helped to produce better performance.

• 로봇학회논문지
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• 제17권3호
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• pp.314-321
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• 2022

Depth information used in SLAM and visual odometry is essential in robotics. Depth information often obtained from sensors or learned by networks. While learning-based methods have gained popularity, they are mostly limited to RGB images. However, the limitation of RGB images occurs in visually derailed environments. Thermal cameras are in the spotlight as a way to solve these problems. Unlike RGB images, thermal images reliably perceive the environment regardless of the illumination variance but show lacking contrast and texture. This low contrast in the thermal image prohibits an algorithm from effectively learning the underlying scene details. To tackle these challenges, we propose multi-channel remapping for contrast. Our method allows a learning-based depth prediction model to have an accurate depth prediction even in low light conditions. We validate the feasibility and show that our multi-channel remapping method outperforms the existing methods both visually and quantitatively over our dataset.

• 로봇학회논문지
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• 제18권3호
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• pp.337-345
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• 2023

In this paper, we propose a 3D LiDAR sensor-based costmap generation and path planning algorithm using it for reliable autonomous flight in complex indoor environments. 3D path planning is essential for reliable operation of UAVs. However, existing grid search-based or random sampling-based path planning algorithms in 3D space require a large amount of computation, and UAVs with weight constraints require reliable path planning results in real time. To solve this problem, we propose a method that divides a 3D space into several 2D spaces and a path planning algorithm that considers the distance to obstacles within each space. Among the paths generated in each space, the final path (Best path) that the UAV will follow is determined through the proposed objective function, and for this purpose, we consider the rotation angle of the 2D space, the path length, and the previous best path information. The proposed methods have been verified through autonomous flight of UAVs in real environments, and shows reliable obstacle avoidance performance in various complex environments.

• 로봇학회논문지
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• 제18권1호
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• pp.93-98
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• 2023

In this paper, the development of an autonomous electric vehicle for logistics with a robotic arm is introduced. The manual driving electric vehicle was converted into an electric vehicle platform capable of autonomous driving. For autonomous driving, an encoder is installed on the driving wheels, and an electronic power steering system is applied for automatic steering. The electric vehicle is equipped with a lidar sensor, a depth camera, and an ultrasonic sensor to recognize the surrounding environment, create a map, and recognize the vehicle location. The odometry was calculated using the bicycle motion model, and the map was created using the SLAM algorithm. To estimate the location of the platform based on the generated map, AMCL algorithm using Lidar was applied. A user interface was developed to create and modify a waypoint in order to move a predetermined place according to the logistics process. An A-star-based global path was generated to move to the destination, and a DWA-based local path was generated to trace the global path. The autonomous electric vehicle developed in this paper was tested and its utility was verified in a warehouse.

• 전자공학회논문지SC
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• 제48권6호
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• pp.91-98
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• 2011

본 논문에서는 수중로봇에 쓰일 수 있는 카메라 영상을 기반으로 하는 인공표식물의 인식 및 추종 기법을 제안한다. 문제를 풀기 위해 제안된 방법은 인식과 추종의 두 개의 단계로 이루어져 있으며 인식단계에서는 물체의 외형에 관한 특징을 분석한 후 비선형 최적화 알고리즘을 통하여 알맞은 목표물로 분류한다. 이 후 추종 단계에서는 분류된 목표물에서 색깔 히스토그램을 추출한 후 meanshift 추종 법을 이용하여 지속적으로 추종하는 방법을 택하였다. 히스토그램 매칭 시에는 Bhattacharyya 거리를 계산하는 방법을 이용하였다. 결과적으로 제안하는 접근법은 수중로봇의 영상처리 분야에 다음과 같은 공헌을 할 것으로 기대한다. 1) 제안하는 방법은 카메라의 움직임으로 생기는 물체의 자세변화나 크기 변화에도 강인하게 대처할 수 있으며 2) 카메라 센서를 통한 방법이므로 초음파 센서 등의 기기들에 비하여 가격 경쟁력이 우수하다. 3) 또한 본 논문에서는 일반적으로 많이 쓰이는 특징 점을 기반으로 한 방법이 탁도 변화에서는 형태를 기반으로 한 방법보다 열등할 수 있음을 실험을 통하여 보였다. 4) 마지막으로 제안된 방법의 성능을 기존의 방법들과 비교하여 수치적으로 검증해 보았다.

• 제어로봇시스템학회논문지
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• 제20권1호
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• pp.70-77
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• 2014

This paper proposes a novel localization algorithm based on ego-motion which used Lucas-Kanade Optical Flow and warping image obtained through fish-eye lenses mounted on the robots. The omnidirectional image sensor is a desirable sensor for real-time view-based recognition of a robot because the all information around the robot can be obtained simultaneously. The preprocessing (distortion correction, image merge, etc.) of the omnidirectional image which obtained by camera using reflect in mirror or by connection of multiple camera images is essential because it is difficult to obtain information from the original image. The core of the proposed algorithm may be summarized as follows: First, we capture instantaneous $360^{\circ}$ panoramic images around a robot through fish-eye lenses which are mounted in the bottom direction. Second, we extract motion vectors using Lucas-Kanade Optical Flow in preprocessed image. Third, we estimate the robot position and angle using ego-motion method which used direction of vector and vanishing point obtained by RANSAC. We confirmed the reliability of localization algorithm using ego-motion based on fisheye warping image through comparison between results (position and angle) of the experiment obtained using the proposed algorithm and results of the experiment measured from Global Vision Localization System.

• 한국통신학회논문지
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• 제25권4B호
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• pp.675-682
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• 2000

본 논문에서는 ATM 네트워크에서 다양한 멀티미디어 트래픽을 효율적으로 전송하기 위해 동적 우선순위 셀 전송 스케쥴링 기법인 AWRR/DT(Adaptive Weighted Round Robin with Delay Tolerance)를 제안하였다. AWRR/DT는 멀티미디어 트랙픽을 지연 특성에 따라 여러 개의 실시간 클래스와 하나의 비실시간 클래스로 분류하고, 각 클래스의 지연 특성과 입력 트래픽의 양을 고려하여 매 사이클마다 해당 클래스에 적절한 가중치(weight)를 할당 하도록 설계되었다. 또한, 제안한 알고리즘은 셀 폐기 메커니즘을 가지고 있어서 순간적인 서비스 품질(QoS:Quality of Service)열화가 계속 연속적으로 이어지는 현상을 줄여준다. AWRR/DT의 성능을 평가하기 위하여, SLAM II를 이용한 컴퓨터 시뮬레이션을 통해 기존의 스케쥴링 기법들과 평균 지연 측면에서 비교하였다. 그 결과, 제안한 기법이 실시간 트래픽 클래스의 QoS를 만족하면서도 비실시간 트래픽 클래스의 평균 지연을 감소시키 수 있음을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.250-255
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• 2005

For navigation of a service robot, mapping and localization are very important. To estimate the robot pose, the map of the environment is required and it can be built by exploration or SLAM. Exploration is the fundamental task of guiding a robot autonomously during mapping such that it covers the entire environment with its sensors. In this paper, an efficient exploration scheme based on the position probability of the end nodes of a topological map is proposed. In this scheme, a topological map is constructed in real time using the thinning-based approach. The robot then updates the position probability of each end node maintaining its position at the current location based on the Bayesian update rule using the range data. From this probability, the robot can determine whether or not it needs to visit the specific end node to examine the environment around this node. Various experiments show that the proposed exploration scheme can perform exploration more efficiently than other schemes in that, in most cases, exploration for the entire environment can be completed without directly visiting everywhere in the environment.

• 한국군사과학기술학회지
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• 제18권6호
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• pp.711-720
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• 2015

Recently many studies of Radar systems mounted on ground vehicles for autonomous driving, SLAM (Simultaneous localization and mapping) and collision avoidance have been reported. Since several pixels per an object may be generated in a close-range radar application, a width of an object can be estimated automatically by various signal processing techniques. In this paper, we tried to attempt to develop an algorithm to estimate obstacle width using Radar images. The proposed method consists of 5 steps - 1) background clutter reduction, 2) local peak pixel detection, 3) region growing, 4) contour extraction and 5)width calculation. For the performance validation of our method, we performed the test width estimation using a real data of two cars acquired by commercial radar system - I200 manufactured by Navtech. As a result, we verified that the proposed method can estimate the widths of targets.