• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2011년도 춘계학술대회 논문집
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• pp.81-82
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• 2011

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2450-2452
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• 2004

Map building is essential to a mobile robot navigation system. Localization and path planning methods depend on map building strategies. A topological map is commonly constructed using the GVG(Generalized Voronoi Graph). The advantage of the GVG based topological map is compactness. But the GVG method have many difficulties because it consists of collision-free path. In this paper, we proposed an extended map building method, the AVG (Areal Voronoi Graph) based topological map. The AVG based topological map consists of collision-free area. This feature can improve map building, localization and path planning performance.

• 한국산학기술학회논문지
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• 제15권12호
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• pp.6986-6992
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• 2014

일반화된 보로노이 그래프(GVG)는 자율 주행 로봇을 위한 일종의 로드맵으로서. GVG는 선서에다 받은 정보만 사용하여 작업 공간거리의 계산에 따라 정의를 한다. 로봇은 장애물까지의 최대 거리를 검출할 수 있기 때문에 포인트 뷰에서 GVG의 최적은 정출 몇 장애물 회피이다. 로봇의 경우에는, GVG는 가장 안전적인 길이라고 할 수 있다. 따라서 높이 링크 로봇의 GVG가장거리에 대한 연구가 매우 필요하다. 기존 연구에서 점(point) 로봇을 위한 GVG(point-GVG)와 로드 로봇을 위한 GVG(rod-GVG)가 발표되었다. 이 논문은 더 고차원의 로봇인 두 개의 동일 링크가 관절로 연결된(tow-identical-link; L2) 로봇을 위한 GVG(L2-GVG)에 대한 연구이다. L2-GVG는 미지의 평면 작업공간에서 움직이는 L2 로봇의 짜임새 공간 $R^2{\times}T^2$상에서 로드맵을 생성하되, 이전 연구와 마찬가지로 지역적 센서 정보만을 이용해 로봇이 스스로 주행하면서 맵을 만들어 낸다. 이 논문에서는 이전 point-GVG와 rod-GVG에서는 나타나지 않는, 관절이 존재하여 생기는 복잡한 특성에 대해서 분석한다. 이는 다관절 로봇으로의 확장에 중요한 초석이 될 것이다.

• 방송공학회논문지
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• 제23권6호
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• pp.896-913
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• 2018

360 VR 영상을 구성하는 과정에서 합성되는 영상들 사이의 경계(seam)를 정확하고 효율적으로 추출하는 것은 스티칭된 영상의 화질에 매우 중요한 영향을 끼친다. 기존의 seam finding 기술들인 보로노이(Voronoi) 알고리즘, dynamic programming, graph cut 방법들은 시차(view disparity)가 존재하거나 이동하는 물체가 존재하는 상황에서는 ghost와 같은 왜곡을 발생시킨다. 본 논문에서는 기존 알고리즘들과 달리 물체를 가로지르지 않고, 물체를 피해 배경을 가로질러 접합선을 추출하는 '밝기 대비를 이용한 마스크 추출 방법'을 제안하였다. 이때, 배경과 물체와의 밝기차이를 이용하여, 물체의 형상을 따라 이어지는 접합선을 추출했다. 본 논문의 실험 결과를 통해, 제안하는 기술을 사용하면 왜곡을 감소시키는 위치에서 접합선이 생성되고, 스티칭된 영상의 화질이 개선되는 것을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.2017-2022
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• 2004

Data association is a process that matches a recent observation with known data set, which is used for the localization of mobile robots. Edges in topological maps have rich information which can be used for the data association. However, no systematic approach on using the edge data for data association has been reported. This paper proposes a systematic way of utilizing the edge data for data association. First, we explain a Local Generalized Voronoi Angle(LGA) to represent the edge data in 1-dimension. Second, we suggest a key factor extraction procedure from the LGA to reduce the number by $2^7-2^8$ times, for computational efficiency using the wavelet transformation. Finally we propose a way of data association using the key factors of the LGA. Simulations show that the proposed data association algorithm yields higher probability for similar edges in computationally efficient manner.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.900-903
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• 1996

The objective of this paper is to make the weighted graph map for path planning using the ultrasonic sensor measurements that are acquired when an A.M.R (autonomous mobile robot) explores the unknown circumstance. First, The A.M.R navigates on unknown space with wall-following and gathers the sensor data from the environments. After this, we constructs the occupancy grid map by interpreting the gathered sensor data to occupancy probability. For the path planning of roadmap method, the weighted graph map is extracted from the occupancy grid map using morphological image processing and thinning algorithm. This methods is implemented on an A.M.R having a ultrasonic sensor.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1933-1938
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• 2003

A covariance matrix is a tool that expresses odometry uncertainty of the wheeled mobile robot. The covariance matrix is a key factor in various localization algorithms such as Kalman filter, topological matching and so on. However it is not easy to acquire an accurate covariance matrix because we do not know the real states of the robot. Up to the authors knowledge, there seems to be no established result on the covariance matrix estimation for the odometry. In this paper, we propose a new method which can estimate the covariance matrix from empirical data. It is based on the PC-method and shows a good estimation ability. The experimental results validate the performance of the proposed method.

• 제어로봇시스템학회논문지
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• 제11권5호
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• pp.457-462
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• 2005

An empirical way of a covariance matrix which expresses the odometry uncertainty of mobile robots is proposed. This method utilizes PC-method which removes systematic errors of odometry. Once the systematic errors are removed, the odometry error can be modeled using the Gaussian probability distribution, and the parameters of the distribution can be represented by the covariance matrix. Experimental results show that the method yields $5{\%}$ and $2.3{\%}$ offset for the synchro and differential drive robots.

• 제어로봇시스템학회논문지
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• 제16권1호
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• pp.53-60
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• 2010

We propose a motion planning method for autonomous mobile robots. In order to minimize traveling time, a smooth path and a time optimal velocity profile should be generated under kinematic and dynamic constraints. In this paper, we develop an effective and practical method to generate a good solution with lower computation time. The initial path is obtained from voronoi diagram by Dijkstra's algorithm. Then the path is improved by changing the graph and path simultaneously. We apply the dynamic programming algorithm into the stage of improvement. Simulation results are presented to verify the performance of the proposed method.

• ETRI Journal
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• 제29권5호
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• pp.655-666
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• 2007

There are nodes and edges in a topological map. Node data has been used as a main source of information for the localization of mobile robots. In contrast, edge data is regarded as a minor source of information, and it has been used in an intuitive and heuristic way. However, edge data also can be used as a good source of information and provide a way to use edge data efficiently. For that purpose, we define a data format which describes the shape of an edge. This format is called local generalized Voronoi graph's angle (LGA). However, the LGA is constituted of too many samples; therefore, real time localization cannot be performed. To reduce the number of samples, we propose a compression method which utilizes wavelet transformation. This method abstracts the LGA by key factors using far fewer samples than the LGA. Experiments show that the LGA accurately describes the shape of the edges and that the key factors preserve most information of the LGA while reducing the number of samples.