• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.81-82
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• 2011

• Proceedings of the KIEE Conference
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• 2004.07d
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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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.6986-6992
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• 2014

The generalized Voronoi graph (GVG) is a topological map of a constrained environment. This is defined in terms of workspace distance measurements using only sensor-provided information, with a robot having a maximum distance from obstacles, and is the optimum for exploration and obstacle avoidance. This is the safest path for the robot, and is very significant when studying the GVG edges of highly articulated robots. In previous work, the point-GVG edge and Rod-GVG were built with point robot and rod robot using sensor-based control. An attempt was made to use a higher degree of freedom robot to build GVG edges. This paper presents GVG-based a new local roadmap for the two-link robot in the constrained two-dimensional environment. This new local roadmap is called the two-identical-link generalized Voronoi graph (L2-GVG). This is used to explore an unknown planar workspace and build a local roadmap in an unknown configuration space $R^2{\times}T^2$ for a planar two-identical-link robot. The two-identical-link GVG also can be constructed using only sensor-provided information. These results show the more complex properties of two-link-GVG, which are very different from point-GVG and rod-GVG. Furthermore, this approach draws on the experience of other highly articulated robots.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.896-913
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• 2018

Seam finding algorithm is one of the most important techniques to construct the high quality 360 VR image. We found that some degradations, such as ghost effect, are generated when the conventional seam finding algorithms (for examples, Voronoi algorithm, Dynamic Programming algorithm, Graph Cut algorithm) are applied, because those make the inefficient masks which cross the body of main objects. In this paper, we proposed an advanced seam finding algorithm providing the efficient masks which go through background region, instead of the body of objects. Simulation results show that the proposed algorithm outperforms the conventional techniques in the viewpoint of the quality of the stitched image.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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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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• 1996.10b
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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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• 2003.10a
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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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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• v.29 no.5
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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.