• The Journal of Korea Robotics Society
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• v.7 no.2
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• pp.113-119
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• 2012

In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.7
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• pp.635-641
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• 2008

In this paper, we used a laser range finder (LRF) to detect both the static and dynamic obstacles for the safe navigation of a mobile robot. LRF sensor measurements containing the information of obstacle's geometry are first processed to extract the characteristic points of the obstacle in the sensor field of view. Then the dynamic states of the characteristic points are approximated using kinematic model, which are tracked by associating the measurements with Probability Data Association Filter. Finally, the collision avoidance algorithm is developed by using fuzzy decision making algorithm depending on the states of the obstacles tracked by the proposed obstacle tracking algorithm. The performance of the proposed algorithm is evaluated through experiments with the experimental mobile robot.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.05a
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• pp.180-180
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• 2004

정밀한 계측의 필요성이 높아지면서 레이저는 그 신뢰성과 사용 편의성으로 인해 거리측정에도 높은 활용을 보이고 있다. 레이저 거리측정기(laser range finder, LRF)의 원리는 20ns 미만의 짧은 레이저 펄스를 표적에 발사한 후 반사되어 돌아오는 신호의 시간과 빛의 속도를 곱하여 거리를 계산하는 방식이다. 이러한 반사펄스(pulse-echo techniques)법은 수m-수백만 km까지의 거리측정에 사용되는 방식으로서 정밀하고 빠른 측정이 가능할 뿐 아니라 단지 목표물을 확인하고 측정버튼을 누름으로써 결과를 얻을 수 있는 사용 편의성을 장점으로 한다.(중략)

• Journal of Korean Society of Water and Wastewater
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• v.18 no.6
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• pp.758-769
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• 2004

The trends and seasonalities of most time series have a large variability. The result of the Singular Spectrum Analysis(SSA) processing is a decomposition of the time series into several components, which can often be identified as trends, seasonalities and other oscillatory series, or noise components. Generally, forecasting by the SSA method should be applied to time series governed (may be approximately) by linear recurrent formulae(LRF). This study examined forecasting ability of SSA-LRF model. These methods are applied to daily water demand data. These models indicate that most cases have good ability of forecasting to some extent by considering statistical and visual assessment, in particular forecasting validity shows good results during 15 days.

• The Journal of Korea Robotics Society
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• v.6 no.3
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• pp.255-262
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• 2011

Many intelligent robots have to be given environmental information to perform tasks. In this paper an intelligent robot, that is, a cleaning robot used a sensor fusing method of two sensors: LRF and StarGazer, and then was able to obtain the information. Throughout wall following using laser displacement sensor, LRF, the working area is built during the robot turn one cycle around the area. After the process of wall following, a path planning which is able to execute the work effectively is established using flow network algorithm. This paper describes an algorithm for minimal turning complete coverage path planning for intelligent robots. This algorithm divides the whole working area by cellular decomposition, and then provides the path planning among the cells employing flow networks. It also provides specific path planning inside each cell guaranteeing the minimal turning of the robots. The proposed algorithm is applied to two different working areas, and verified that it is an optimal path planning method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.877-878
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• 2013

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.10a
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• pp.133-133
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• 2003

• Journal of the Korean Society for Precision Engineering
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• v.23 no.1 s.178
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• pp.41-47
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• 2006

• Proceedings of the Optical Society of Korea Conference
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• 2005.02a
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• pp.124-125
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• 2005

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.295-296
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• 2009