• Journal of the Korean Society of Industry Convergence
• /
• v.22 no.2
• /
• pp.87-93
• /
• 2019

This research deals with posture optimization for humanoid robot against external forces using genetic algorithm and neural network. When the robot takes a motion to push an object, the torque of each joint is generated by reaction force at the palm. This study aims to optimize the posture of the humanoid robot that will change this torque. This study finds an optimized posture using a genetic algorithm such that torques are evenly distributed over the all joints. Then, a number of different optimized postures are generated from various the reaction forces at the palm. The data is to be used as training data of MLP(Multi-Layer Perceptron) neural network with BP(Back Propagation) learning algorithm. Humanoid robot can find the optimal posture at different reaction forces in real time using the trained neural network include non-training data.

• Laser Solutions
• /
• v.11 no.4
• /
• pp.21-28
• /
• 2008

Remote Laser welding technology for manufacturing automotive body is studied. Laser welding and industrial robot systems are used for the robot based laser welding system. The laser system is used 1.6kW Fiber laser(YLR-1600) of IPG. The robot system is used HX130-02 of Hyundai Heavy Industry(payload : 130kg). The robot based laser welding system is equipped with laser scanner system for remote laser welding. The welding joints of steel plate and steel plate coated with zinc are butt and lapped joints. The quality test of the laser welding are through the observation the shape of bead on plate and cross-section of welding part. During past three years the laser system, 4kW Nd:YAG laser (HL4006D) of Trumpf was used and the robot system, IRB6400R of ABB (payload:120kg) was used. The new laser source, robot and laser scanner system are used to increase the processing speed and to improve the process efficiency. This paper introduces the robot based remote laser welding system to resolve the limited welding speed and accuracy of the conventional laser welding system.

• Journal of the Korean Society of Industry Convergence
• /
• v.21 no.2
• /
• pp.87-93
• /
• 2018

This Study Propose A New Approach To Impliment A Intelligent Robot Control Based on Voice Recognition For Smart Factory Automation Since human usually communicate each other by voices, it is very convenient if voice is used to command humanoid robots or the other type robot system. A lot of researches has been performed about voice recognition systems for this purpose. Hidden Markov Model is a robust statistical methodology for efficient voice recognition in noise environments. It has being tested in a wide range of applications. A prediction approach traditionally applied for the text compression and coding, Prediction by Partial Matching which is a finite-context statistical modeling technique and can predict the next characters based on the context, has shown a great potential in developing novel solutions to several language modeling problems in speech recognition. It was illustrated the reliability of voice recognition by experiments for humanoid robot with 26 joints as the purpose of application to the manufacturing process.

• Proceedings of the KIEE Conference
• /
• 2002.11c
• /
• pp.495-500
• /
• 2002

Recently, a service robot industry outstands as an up and coming industry of the next generation. Specially, there are so many research in self-steering movement(SSM). In order to implement SSM, robot must effectively recognize all around, detect objects and make a surrounding map with sensors. So, many robots have a sonar and a infrared sensor, etc. But, in these sensors, We only know informations about between the robot and the object as well as resolution faculty is of inferior quality. In this paper, we will introduce new algorithm that recognizes objects around robot and makes a two dimension surrounding map with a omni-direction vision camera and two stereo vision cameras.

• Journal of the Korean Society of Industry Convergence
• /
• v.22 no.4
• /
• pp.447-457
• /
• 2019

The intelligent sensory system is required to ensure the accurate welding performance. This paper describes the development of an intelligent vision sensor for the robotic laser welding. The sensor system includes a PC based vision camera and a stripe-type laser diode. A set of robust image processing algorithms are implemented. The laser-stripe sensor can measure the profile of the welding object and obtain the seam line. Moreover, the working distance of the sensor can be changed and other configuration is adjusted accordingly. The robot, the seam tracking system, and CW Nd:YAG laser are used for the laser welding robot system. The simple and efficient control scheme of the whole system is also presented. The profile measurement and the seam tracking experiments were carried out to validate the operation of the system.

• Journal of Digital Convergence
• /
• v.13 no.8
• /
• pp.301-313
• /
• 2015

Robot technology has become a crucial part of today's business operation. In fact, more manufacturing firms have been utilizing robot technology in order to increase operational efficiency and productivity. Thus, this study develops the research model investigating firms; behavior for process innovation with intelligent robot. Three categories - Technical, Entrepreneur, and Environmental characteristic - are proposed in the research model as determinants of process innovation. These three characteristics include six variables(Perceived Direct Usefulness, Perceived Indirect Usefulness, Innovation, Risk Senstivity, Perceived Industry Pressure, and Perceived Government Pressure) as influencing factors on process innovation. The data from 77 employee at manufacturing firms were analyzed to test proposed hypotheses. The results reveal that all variables with exception of Perceived Government Pressure have a significant influence on process innovation. Based on the study results, theoretical and practical implications for process innovation with intelligent robot technology are discussed.

• Journal of the Korean Society of Industry Convergence
• /
• v.19 no.1
• /
• pp.1-9
• /
• 2016

In this paper, we propose a new learning control scheme for various walk motion control of biped robot with same learning-base by neural network. We show that learning control algorithm based on the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multi layer back propagation neural network identification is simulated to obtain a dynamic model of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The biped robots have been received increased attention due to several properties such as its human like mobility and the high-order dynamic equation. These properties enable the biped robots to perform the dangerous works instead of human beings. Thus, the stable walking control of the biped robots is a fundamentally hot issue and has been studied by many researchers. However, legged locomotion, it is difficult to control the biped robots. Besides, unlike the robot manipulator, the biped robot has an uncontrollable degree of freedom playing a dominant role for the stability of their locomotion in the biped robot dynamics. From the simulation and experiments the reliability of iterative learning control was illustrated.

• Journal of the Korean Society of Industry Convergence
• /
• v.19 no.2
• /
• pp.75-80
• /
• 2016

This study proposes a efficient technology to control the optimal trajectory planning and real-time implementation method which can perform autonomous travelling for unmaned factory automation. Online path planning should plan and execute alternately in a short time, and hence it enables the robot avoid unknown dynamic obstacles which suddenly appear on robot's path. Based on Route planning and control algorithm, we suggested representation of edge cost, heuristic function, and priority queue management, to make a modified Route planning algorithm. Performance of the proposed algorithm is verified by simulation test.

• Proceedings of the Korean Nuclear Society Conference
• /
• 2005.05a
• /
• pp.1339-1340
• /
• 2005

• Journal of the Korean Society of Industry Convergence
• /
• v.20 no.4
• /
• pp.285-297
• /
• 2017

This study proposed a new approach to develop an Intelligent control algorithm for cooperative working of human and robot based on voice recognition. In general case of speaker verification, Gaussian Mixture Model is used to model the feature vectors of reference speech signals. On the other hand, Dynamic Time Warping based template matching techniques were presented for the voice recognition about several years ago. We converge these two different concepts in a single method and then implement in a real time voice recognition enough to make reference model to satisfy 95% of recognition performance. In this paper it was illustrated the reliability of voice recognition by simulation and experiments for humanoid robot with 18 joints.