• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.23-26
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• 2003

The vector field histogram(VFH) uses a two-dimensional Cartesian histogram grid as a world model. The VFH method subsequently employs a two-stage data-reduction process in order to compute the desired control commands for the vehicle. In the first stage the histogram grid is reduced to a one dimensional polar histogram that is constructed around the robot's momentary location. Each sector in the polar histogram contains a value representing the polar obstacle density in that direction. In the second stage, the algorithm selects the most suitable sector from among all polar histogram sectors with a low polar obstacle density, and the steering of the robot is aligned with that direction. We applied this algorithm to our four-legged robot.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1076-1079
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• 2003

The vector field histogram(VFH) uses a two-dimensional Cartesian histogram grid as a world model. The VFH method subsequently employs a two-stage data-reduction process in order to compute the desired control commands for the vehicle. In the first stage the histogram grid is reduced to a one dimensional polar histogram that is constructed around the robot's momentary location. Each sector in the polar histogram contains a value representing the polar obstacle density in that direction. In the second stage, the algorithm selects the most suitable sector from among all polar histogram sectors with a low polar obstacle density, and the steering of the robot is aligned with that direction. We applied this algorithm to our simulation program and tested..

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.05a
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• pp.277-277
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• 2004

로봇의 실시간 장애물 회피 방법은 연구되어 왔고 실행되어 왔다. 이러한 방법을 vector field histogram(VFH)라 하며 이러한 방법은 알려져 있지 않는 장애물의 발견과 장애물과의 충돌을 피하는 동시에 목표점으로의 로봇의 이동을 위한 알고리즘이다. The vector field histogram(VFH)방법은 world model로 이차원 Cartesian histogram grid를 이용하였다. VFH 방법은 Vehicle을 원하는 데로 컨트롤하기 위한 과정으로 두 단계 데이터 줄이는 과정이다. Histogram grid 의 첫 번째 단계는 로봇의 순간위치를 구성하기 위한 일 차원 polar histogram에 포함된 각 섹터의 값은 polar obstacle density(POD)로 방향을 표시한다.(중략)

• Journal of Ocean Engineering and Technology
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• v.30 no.5
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• pp.426-430
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• 2016

Recently, many unmanned surface vehicles (USVs) have been developed and researched for various fields such as the military, environment, and robotics. In order to perform purpose specific tasks, common autonomous navigation technologies are needed. Obstacle avoidance is important for safe autonomous navigation. This paper describes a vector field histogram+ (VFH+) based obstacle avoidance method that uses the monocular vision of an unmanned surface vehicle. After creating a polar histogram using VFH+, an open space without the histogram is selected in the moving direction. Instead of distance sensor data, monocular vision data are used for make the polar histogram, which includes obstacle information. An object on the water is recognized as an obstacle because this method is for USV. The results of a simulation with sea images showed that we can verify a change in the moving direction according to the position of objects.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.275-284
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• 2006

This paper deals with problems of safe and efficient navigation algorithm for autonomous mobile robots in unknown environments. Since the obstacle avoidance algorithms are very important in mobile robot navigation, two obstacle avoidance algorithms: VFH(vector field histogram) algorithm and a fuzzy algorithm are combined to have optimal performance in various environments. And a upper-level supervisor is to select the proper one from VFH algorithm and the fuzzy algorithm according to the situations the robot faces. Computer simulation results show the effectiveness of the proposed navigation algorithm for autonomous mobile robots.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.9
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• pp.76-90
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• 1999

In this paper, a color image retrieval algorithm is proposed based on color histogram and color texture. The representative color vectors of a color image are made from k-means clustering of its color histogram, and color texture is generated by centering around the color of pixels with its color vector. Thus the color texture means texture properties emphasized by its color histogram, and it is analyzed by Gaussian Markov Random Field (GMRF) model. The proposed algorithm can work efficiently because it does not require any low level image processing such as segmentation or edge detection, so it outperforms the traditional algorithms which use color histogram only or texture properties come from image intensity.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.945-953
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• 2018

In this paper, we use 3-D LIDAR for obstacle detection and avoidance maneuver for autonomous unmanned operation. It is aimed to avoid obstacle avoidance in unmanned water under marine condition using only single sensor. 3D lidar uses Quanergy's M8 sensor to collect surrounding obstacle data and includes layer information and intensity information in obstacle information. The collected data is converted into a three-dimensional Cartesian coordinate system, which is then mapped to a two-dimensional coordinate system. The data including the obstacle information converted into the two-dimensional coordinate system includes noise data on the water surface. So, basically, the noise data generated regularly is defined by defining a hypothetical region of interest based on the assumption of unmanned water. The noise data generated thereafter are set to a threshold value in the histogram data calculated by the Vector Field Histogram, And the noise data is removed in proportion to the amount of noise. Using the removed data, the relative object was searched according to the unmanned averaging motion, and the density map of the data was made while keeping one cell on the virtual grid map. A polar histogram was generated for the generated obstacle map, and the avoidance direction was selected using the boundary value.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.2
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• pp.65-72
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• 2011

In this paper, a real-time monocular vision based navigation system is developed for the disabled people, where online background learning and vector field histogram are used for identifying obstacles and recognizing avoidable paths. The proposed system is performed by three steps: obstacle classification, occupancy grid map generation and VFH-based path recommendation. Firstly, the obstacles are discriminated from images by subtracting with background model which is learned in real time. Thereafter, based on the classification results, an occupancy map sized at $32{\times}24$ is produced, each cell of which represents its own risk by 10 gray levels. Finally, the polar histogram is drawn from the occupancy map, then the sectors corresponding to the valley are chosen as safe paths. To assess the effectiveness of the proposed system, it was tested with a variety of obstacles at indoors and outdoors, then it showed the a'ccuracy of 88%. Moreover, it showed the superior performance when comparing with sensor based navigation systems, which proved the feasibility of the proposed system in using assistive devices of disabled people.

• Journal of the Korea Society of Computer and Information
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• v.3 no.4
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• pp.27-34
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• 1998

In this paper we present a method for automatic motion vector and brightness based video indexing and retrieval. We extract a representational frame from each shot and compute some motion vector and brightness based features. For each R-frame we compute the optical flow field; motion vector features are then derived from this flow field, BMA(block matching algorithm) is used to find motion vectors and Brightness features are related to the cut detection of method brightness histogram. A video database provided contents based access to video. This is achieved by organizing or indexing video data based on some set of features. In this paper the index of features is based on a B+ search tree. It consists of internal and leaf nodes stores in a direct access a storage device. This paper defines the problem of video indexing based on video data models.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1898-1899
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• 2011

In this paper, we propose an obstacle avoidance algorithm for a network-based autonomous mobile robot. The obstacle avoidance algorithm is based on the VFH (Vector Field Histogram) algorithm and delay-compensative methods with the VFH algorithm are proposed for the network-based robot that is a unified system composed of distributed environmental sensors, mobile actuators, and the VFH controller. Firstly, the compensated readings of the sensors are used for building the polar histogram of the VFH algorithm. Secondly, a sensory fusion using the Kalman filter is proposed for the localization of the robot to compensate both the delay of the readings of an odometry sensor and the delay of the readings of the environmental sensors. The performance enhancements of the proposed obstacle avoidance algorithm from the viewpoint of efficient path generation and accurate goal positioning are also shown in this paper through some simulation experiments by the Marilou Robotics Studio Simulator.