• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1105-1109
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• 1995

A CCD-camera, which is structured with vision system, was used to realize automatic seam-tracking system and 3-D information which is needed to generate torch path, was obtained by using laser-slip beam. To extract laser strip and obtain welding-specific point, Adaptive Hough-transformation was used. Although the basic Hough transformation takes too much time to process image on line, it has a tendency to be robust to the noises as like spatter. For that reson, it was complemented with Adaptive Hough transformation to have an on-line processing ability for scanning a welding-specific point. the dead zone,where the sensing of weld line is impossible, is eliminated by rotating the camera with its rotating axis centered at welding torch. The camera angle is controlled so as to get the minimum image data for the sensing of weld line, hence the image processing time is reduced. The fuzzy controller is adapted to control the camera angle.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.172-174
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• 2022

Logistics processing companies in Korea are mostly handling various types of products in and out. In order to process various types of products, the sorting business is performed by hand. In this paper, we propose a real-time QR code detection method using a vision sensor to achieve high efficiency with a small amount of manpower. The limiting system uses a vision sensor to process QR code recognition of logistics in real time. The proposed system can quickly identify a large number of QR codes through multiple recognition rather than QR code recognition, which is a single part of logistics. In the study, the system was actually implemented and verified, and multiple QR recognition was confirmed in the image through the vision center.

• Proceedings of the KWS Conference
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• 1999.10a
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• pp.62-65
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• 1999

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.677-678
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• 2011

• Proceedings of the Korean Society of Laser Processing Conference
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• 2000.06a
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• pp.39-42
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• 2000

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2400-2405
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• 2013

We detect the obstacle for the UGV(unmanned ground vehicle) from the compound image which is generated by stereo vision sensor masking the depth image and color image. Stereo vision sensor can gathers the distance information by stereo camera. The obstacle information from the depth compound image can be send to mobile robot and the robot can localize the indoor area. And, we test the performance of the mobile robot in terms of distance between the obstacle and the robot's position and also test the color, depth and compound image respectively. Moreover, we test the performance in terms of number of frame per second which is processed by operating machine. From the result, compound image shows the improved performance in distance and number of frames.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.901-906
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• 2007

Algorithms of path planning and obstacle avoidance are essential to autonomous mobile robots that are working in unknown environments in the real time. This paper presents a new navigation algorithm for an autonomous mobile robot with vision and IR sensors using fuzzy rules. Temporary targets are set up by distance variation method and then the algorithms of trajectory planning and obstacle avoidance are designed using fuzzy rules. In this approach, several digital image processing technique is employed to detect edge of obstacles and the distances between the mobile robot and the obstacles are measured. An autonomous mobile robot with single vision and IR sensors is built up for experiments. We also show that the autonomous mobile robot with the proposed algorithm is navigating very well in complex unknown environments.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.699-708
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• 2021

In this paper, a steering control and speed control algorithm was presented for autonomous driving based on two vision sensors and road speed sign board. A car speed control algorithm was developed to recognize the speed sign by using TensorFlow, a deep learning program provided by Google to the road speed sign image provided from vision sensor B, and then let the car follows the recognized speed. At the same time, a steering angle control algorithm that detects lanes by analyzing road images transmitted from vision sensor A in real time, calculates steering angles, controls the front axle through PWM control, and allows the vehicle to track the lane. To verify the effectiveness of the proposed algorithm's steering and speed control algorithms, a car's prototype based on the Python language, Raspberry Pi and OpenCV was made. In addition, accuracy could be confirmed by verifying various scenarios related to steering and speed control on the test produced track.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.14-23
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• 2007

Welding fabrication invariantly involves three district sequential steps: preparation, actual process execution and post-weld inspection. One of the major problems in automating these steps and developing autonomous welding system is the lack of proper sensing strategies. Conventionally, machine vision is used in robotic arc welding only for the correction of pre-taught welding paths in single pass. However, in this paper, multipass tracking more than single pass tracking is performed by conventional seam tracking algorithm and developed one. And tracking performances of two algorithm are compared in multipass tracking. As the result, tracking performance in multi-pass welding shows superior conventional seam tracking algorithm to developed one.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.5
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• pp.422-428
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• 2013

Recently, many unmanned aerial vehicle (UAV) studies have focused on small UAVs, because they are cost effective and suitable in dangerous indoor environments where human entry is limited. Map building through distance measurement is a key technology for the autonomous flight of small UAVs. In many researches for unmanned systems, distance could be measured by using laser range finders or stereo vision sensors. Even though a laser range finder provides accurate distance measurements, it has a disadvantage of high cost. Calculating the distance using a stereo vision sensor is straightforward. However, the sensor is large and heavy, which is not suitable for small UAVs with limited payload. This paper suggests a low-cost distance measurement system using a laser pointer and a monocular vision sensor. A method to measure distance using the suggested system is explained and some experiments on map building are conducted with these distance measurements. The experimental results are compared to the actual data and the reliability of the suggested system is verified.