• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.836-839
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• 2004

Qualitative elevation of products is very important Part. A business racking us brains to find for qualitative elevation of products. Recently, measurement accuracy of a non-contact 3D scanner has been rapidly improving. As a result, the number application cases of non-contact 3D scanners are increasing. A non-contact 3D scanner is capable of measuring a curved surface rapidly and has high resolution. It is more affordable and potable than the CMMs, It is therefore expected to be applied more frequently in more diverse industries. Automating the measuring process using a non-contact 3D scanner and developing a technology, which allows a user to measure easily, will eventually improve the quality of products. As their inspection and analysis processes improve.

• Journal of Mechanical Science and Technology
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• v.16 no.6
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• pp.762-769
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• 2002

This paper presents how effective it is to use many features for improving the speed and accuracy of visual servo systems. Some rank conditions which relate the image Jacobian to the control performance are derived. The focus is to describe that the accuracy of the camera position control in the world coordinate system is increased by utilizing redundant features in this paper. It is also proven that the accuracy is improved by increasing the number of features involved. Effectiveness of the redundant features is evaluated by the smallest singular value of the image Jacobian which is closely related to the accuracy with respect to the world coordinate system. Usefulness of the redundant features is verified by the real time experiments on a Dual-Arm robot manipulator made by Samsung Electronic Co. Ltd..

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.255-260
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• 2001

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.133-137
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• 1996

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have bevome increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking arre indispensable capabilities for their versatile application. the need to meet demanding control requirement in increasingly complex dynamical control systems under sygnificant uncertainties leads toward design of implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme the ntworks intrduced are neural nets with dynamic neurouns whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure fast in computation and suitable for implementation of real-time control, Performance of the neural controller is illustrated by simulation and experimental results for a SCAEA robot.

• Design & Manufacturing
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• v.16 no.4
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• pp.60-66
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• 2022

Recently, with the development of industry and the improvement of living standards, various wastes are generated along with the production of various products. Most of these wastes are used as containers for products, and plastic or aluminum is used. Various attempts are being made to automate the classification of these wastes due to the high labor cost, but most of them are solved by manpower due to the geometrical shape change due to the nature of the waste. In this study, in order to automate the waste sorting task, Deep Learning technology is applied to a robot system for waste sorting and a vision system for waste sorting to effectively perform sorting tasks according to the shape of waste. As a result of the experiment, a Deep Learning parameter suitable for waste sorting was selected. In addition, through various experiments, it was confirmed that 99% of wastes could be selected in individual & group image learning. It is expected that this will enable automation of the waste sorting operation.

• Industry Promotion Research
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• v.7 no.4
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• pp.9-15
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• 2022

This study evaluated the automated system of the roll bending process, which is one of the difficult processes. In the past, 20 cartridges were produced per hour. but Automation changed it to a process that produces 50 pieces per hour. The average value of production was 57.6 pieces per hour, error of repeatability was 0.03 mm, average roll diameter error value was 0.49 mm, average alignment error value was 0.09 mm and average process lead time was 43.21 seconds. This paper presented specific evaluation methods such as productivity, repeatability, defect rate, alignment defect rate, and process lead time. It is thought that the contents performed in this study will be helpful in the verification of other automation systems in the future.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.405-413
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• 2006

A novel approach based on genetic algorithms (GA) is developed to find a global minimum-jerk trajectory of a space robotic manipulator in joint space. The jerk, the third derivative of position of desired joint trajectory, adversely affects the efficiency of the control algorithms and stabilization of whole space robot system and therefore should be minimized. On the other hand, the importance of minimizing the jerk is to reduce the vibrations of manipulator. In this formulation, a global genetic-approach determines the trajectory by minimizing the maximum jerk in joint space. The planning procedure is performed with respect to all constraints, such as joint angle constraints, joint velocity constraints, joint angular acceleration and torque constraints, and so on. We use an genetic algorithm to search the optimal joint inter-knot parameters in order to realize the minimum jerk. These joint inter-knot parameters mainly include joint angle and joint angular velocities. The simulation result shows that GA-based minimum-jerk trajectory planning method has satisfactory performance and real significance in engineering.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.730-744
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• 2023

The topic of this paper is the recognition of human activities using egocentric vision, particularly captured by body-worn cameras, which could be helpful for video surveillance, automatic search and video indexing. This being the case, it could also be helpful in assistance to elderly and frail persons for revolutionizing and improving their lives. The process throws up the task of human activities recognition remaining problematic, because of the important variations, where it is realized through the use of an external device, similar to a robot, as a personal assistant. The inferred information is used both online to assist the person, and offline to support the personal assistant. With our proposed method being robust against the various factors of variability problem in action executions, the major purpose of this paper is to perform an efficient and simple recognition method from egocentric camera data only using convolutional neural network and deep learning. In terms of accuracy improvement, simulation results outperform the current state of the art by a significant margin of 61% when using egocentric camera data only, more than 44% when using egocentric camera and several stationary cameras data and more than 12% when using both inertial measurement unit (IMU) and egocentric camera data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.503-509
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• 2014

In this paper, a PID controller with adaptive neural network compensator is proposed to control the formations of mobile robot. The control system is composed of a kinematic controller based on the leader-following robot and dynamic controller for considering the dynamics of the mobile robot. The dynamic controller is constituted by a PID controller and the adaptive neural network compensator for improving the performance and compensating the change in dynamic characteristics. Simulation results show the performance of the PID controller and the neural network compensator for the circular trajectory and linear trajectory. And it is verified that by improving the performance of a PID controller via the adaptive neural network compensator, the following robot's tracking performance is improved.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.3
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• pp.254-260
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• 2002

In this study, two types of mobile robotic systems using NDT (Non-destructive testing) method are developed for automatic diagnosis of the boiler tubes and large size pipelines. The developed mobile robots crawl the outer surface of the tubes or pipelines and detect in-pipe defects such as pinholes, cracks and thickness reduction by corrosion and/or erosion using EMAT (Electro-magnetic Acoustic Transducer). Automation of fault detection by means of mobile robotic systems for these large-scale structures helps to prevent significant troubles without danger of human beings under harmful environment.