• Journal of Advanced Marine Engineering and Technology
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• v.27 no.5
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• pp.641-647
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• 2003

The use of infrared range-finder sensor as the environment recognition system for mobile robot have the advantage of low sensing cost compared with the use of other vision sensor such as laser finder CCD camera. However, it is not easy to find the previous works on the use of infrared range-finder sensor for a mobile robot because of the non-linear characteristic of that. This paper describes the error due to non-linearity of a sensor and the correction of it using neural network. The neural network consists of multi-layer perception and Levenberg-Marquardt algorithm is applied to learning it. The effectiveness of the proposed algorithm is verified from experiment.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.5
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• pp.560-570
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• 2005

In this paper, we discuss the tracking system for a wheelchair which can follow the path of a human target such as a nurse in hospital. The problem of human tracking is that it requires recognition of feature as well as the tracking of human positions. For this purpose the use of a high cost visual sensor such as laser finder or streo camera makes the tracking a high cost additional expense. This paper proposes the tracking system uses a low cost infrared range-finder and CCD camera, The Infrared range-finder and CCD camera can create a target candidate through each target recognition algorithm. and this information is fused in order to reduce the uncertainties of a target decision and correct the positional error of the human. The effectiveness of the proposed system is verified through experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.9
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• pp.922-931
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• 2011

An efficient calibration algorithm for mobile robot localization using infrared range finder is proposed. A calibration is important to guarantee the performance of other algorithms which use sensor data because it is pre-process. We experimentally found that the infrared range finder PBS-03JN has error characteristics depending on both distance and scan angle. After obtaining 2-D grid error characteristic data on distance and scan angle, we proposed a simple and efficient calibration algorithm with a 2-D piecewise linear function set. The performance of our proposed calibration algorithm is verified by experiments and simulation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.36-54
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• 2017

The purpose of this paper is to develop a multi-sensor fusion-based traffic information acquisition system with robust to environmental changes. it combines the characteristics of each sensor and is more robust to the environmental changes than the video detector. Moreover, it is not affected by the time of day and night, and has less maintenance cost than the inductive-loop traffic detector. This is accomplished by synthesizing object tracking informations based on a radar, vehicle classification informations based on a video detector and reliable object detections of a infrared range finder. To prove the effectiveness of the proposed system, I conducted experiments for 6 hours over 5 days of the daytime and early evening on the pedestrian - accessible road. According to the experimental results, it has 88.7% classification accuracy and 95.5% vehicle detection rate. If the parameters of this system is optimized to adapt to the experimental environment changes, it is expected that it will contribute to the advancement of ITS.