• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.680-684
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• 1996

Welding systems cannot adapt to changes in the joint geometry which may occur due to a variety of reason. Automatic seam tracking technigue is essential to adjust the welding torch position in real time as it moves along the seam. Automatic tracking system must keep the welding speed constant unrelation to the change of the welding path. Therefore, the information from the laser displacement sensor must be converted into the input to operate the X-Y table and to rotate the desired torch position by proposed algorithm. In this research, laser displacement sensor is used as a seam finder in the automatic tracking system. X-Y moving table manipulated by ac servo motor controls the position and velocity of the torch-and-sensor part. DC motor controls the position and velocity of the torch. X-Y table controls the position of sensor and relative position of torch is controlled by dc motor which is mounted at sensor-and-torch part. Sensor is always ahead of torch to preview the weld line. From the experimental results, we could see the possiblity that the laser displacement sensor can be used as a seam finder in welding process and that the seam tracking system controlled by proposed algorithm is well done.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.170-178
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• 2017

This paper proposes an improved path-planning technique based on the $A^*$ algorithm. The conventional $A^*$ algorithm only considers the optimality of the planned path and sometimes produces a path that an unmanned underwater vehicle (UUV) cannot navigate due to its dynamic constraint such as the limit of the radius of gyration. It is because that the previous method evaluate the moving cost based on the straight distance between nodes. We enhance the conventional method by evaluating the moving cost on the basis of the practically navigable trajectory, which is generated by the waypoint-tracking control of the UUV dynamics. The simulation examples indeed show the effectiveness of the proposed technique.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1261-1264
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• 2005

In this paper, a new intelligent moving target tracking and surveillance system basing on the pan/tilt-embedded stereo camera system is suggested and implemented. In the proposed system, once the face area of a target is detected from the input stereo image by using a YCbCr color model and then, using this data as well as the geometric information of the tracking system, the distance and 3D information of the target are effectively extracted in real-time.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1138-1141
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• 1995

An arc sensor for seam tracking is developed to automate sub-assembly welding in shipbuilding. We utilize a moving average method, which produces an effect of low-pass filter, to generate the position compensation. Therefore the sensor is able to modify the path of the weld seam in real time. By simplifying the compension process, the tunning time is reduced so that operators react quickly. It turns out that this sensor is highly reliable and it is installed and being used in SHI Keoje shipbuilding yard.

• ETRI Journal
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• v.43 no.4
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• pp.617-629
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• 2021

Although the actual visual simultaneous localization and mapping (SLAM) algorithms provide highly accurate tracking and mapping, most algorithms are too heavy to run live on embedded devices. In addition, the maps they produce are often unsuitable for path planning. To mitigate these issues, we propose a completely closed-loop online dense RGB-D SLAM algorithm targeting autonomous indoor mobile robot navigation tasks. The proposed algorithm runs live on an NVIDIA Jetson board embedded on a two-wheel differential-drive robot. It exhibits lightweight three-dimensional mapping, room-scale consistency, accurate pose tracking, and robustness to moving objects. Further, we introduce a navigation strategy based on the proposed algorithm. Experimental results demonstrate the robustness of the proposed SLAM algorithm, its computational efficiency, and its benefits for on-the-fly navigation while mapping.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.2
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• pp.101-106
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• 2010

We propose an approach to estimate the real-time moving trajectory of an object in this paper. The object's position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Extended Kalman Filter(EKF) and neural networks are utilized cooperatively. Since the EKF needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach the Kohonen networks, which have a high adaptability to the memory of the inputoutput relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the EKF is demonstrated through real experiments.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.57-65
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• 2008

Recently many researches on intelligent robots have been studied. An intelligent robot is capable of recognizing environments or objects to autonomously perform specific tasks using sensor readings. One of fundamental problems in vision-based robot applications is to recognize where it is and to decide safe path to perform autonomous navigation. However, previous approaches only consider well-organized environments that there is no moving object and environment changes. In this paper, we introduce a novel navigation strategy to handle occlusions caused by moving objects using various computer vision techniques. Experimental results demonstrate the capability to overcome such difficulties for autonomous navigation.

• Journal of IKEEE
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• v.13 no.2
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• pp.150-158
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• 2009

For the autonomous movement, the optimal path planning connecting between current and target positions is essential, and the optimal path of mobile robot means obstacle-free and the shortest length path to a target position. Many actual mobile robots should move without any information of surrounded obstacles. Thus, this paper suggests new methods of path planning and obstacle avoidment, suitable in unknown environments. This method of path planning always tracks the local target expected as the optimal one, and the result of continuous tracking becomes the first generated moving path. This path, however, do not regard the collision with obstacles. Thus, this paper suggests a new method of obstacle avoidance resembled with the Potential Field method. Finally, a simulation confirms the performance and correctness of the path planning and obstacle avoidance, suggested in this paper.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.447-454
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• 2013

As the surveillance CCTVs are increasing every year, big data image processing for the CCTV image data has become a hot issue. In this paper, we propose a Hadoop-based big data image processing technique to recognize a vehicle number from a large amount of automatic number plate images taken from CCTVs. We also implement the vehicle route tracking system that displays the moving path of the searched vehicle on Google Maps with the related information together. In order to evaluate the performance we compare and analysis the vehicle number recognition time for a lot of CCTV image data in Hadoop and the single PC environment.

• The Journal of Korea Robotics Society
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• v.15 no.3
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• pp.221-232
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• 2020

In a four-wheel independent drive platform, four wheels and motors are connected directly, and the rotation of the motors generates the power of the platform. It uses a skid steering system that steers based on the difference in rotational power between wheel motors. The platform can control the speed of each wheel individually and has excellent mobility on dirt roads. However, the difficulty of the straight-running is caused due to torque distribution variation in each wheel's motor, and the direction of rotation of the wheel, and moving direction of the platform, and the difference of the platform's target direction. This paper describes an algorithm to detect the slip generated on each wheel when a four-wheel independent drive platform is traveling in a harsh environment. When the slip is detected, a compensation control algorithm is activated to compensate the torque of the motor mounted on the platform to improve the trajectory tracking performance of the platform. The four-wheel independent drive platform developed for this study verified the algorithm. The wheel slip detection and the compensation control algorithm of the platform are expected to improve the stability of trajectory tracking.