• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1983-1989
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• 2005

Industrial robots are powerful, extremely accurate multi-jointed systems, but they are heavy and highly rigid because of their mechanical structure and motorization. Therefore, sharing the robot working space with its environment is problematic. A novel pneumatic artificial muscle actuator (PAM actuator) has been regarded during the recent decades as an interesting alternative to hydraulic and electric actuators. Its main advantages are high strength and high power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available and cheap power source, inherent safety and mobility assistance to humans performing tasks. The PAM is undoubtedly the most promising artificial muscle for the actuation of new types of industrial robots such as Rubber Actuator and PAM manipulators. However, some limitations still exist, such as the air compressibility and the lack of damping ability of the actuator bring the dynamic delay of the pressure response and cause the oscillatory motion. In addition, the nonlinearities in the PAM manipulator still limit the controllability. Therefore, it is not easy to realize motion with high accuracy and high speed and with respect to various external inertia loads in order to realize a human-friendly therapy robot To overcome these problems a novel controller, which harmonizes a phase plane switching control method with conventional PID controller and the adaptabilities of neural network, is newly proposed. In order to realize satisfactory control performance a variable damper - Magneto-Rheological Brake (MRB) is equipped to the joint of the manipulator. Superb mixture of conventional PID controller and a phase plane switching control using neural network brings us a novel controller. This proposed controller is appropriate for a kind of plants with nonlinearity uncertainties and disturbances. The experiments were carried out in practical PAM manipulator and the effectiveness of the proposed control algorithm was demonstrated through experiments, which had proved that the stability of the manipulator can be improved greatly in a high gain control by using MRB with phase plane switching control using neural network and without regard for the changes of external inertia loads.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.237-242
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• 2023

Research on high-performance walking robots is being actively conducted, and quadruped walking robots are receiving a lot of attention due to their excellent mobility and adaptability on uneven terrain, but they are difficult to introduce and utilize due to high cost. In this paper, to increase utilization by applying intelligent functions to a low-cost quadruped robot, we present a method of improving uneven terrain overcoming ability by mounting IMU and reinforcement learning on embedded board and automatically detecting objects using camera and deep learning. The robot consists of the legs of a quadruped mammal, and each leg has three degrees of freedom. We train complex terrain in simulation environments with designed 3D model and apply it to real robot. Through the application of this research method, it was confirmed that there was no significant difference in walking ability between flat and non-flat terrain, and the behavior of performing person detection in real time under limited experimental conditions was confirmed.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.4
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• pp.193-205
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• 2003

There has been so many research activities about robot soccer system in the many research fields, for example, intelligent control, communication, computer technology, sensor technology, image processing, mechatronics. Especially researchers research strategy for attacking in the field of strategy, and develop intelligent strategy. Then, soccer robots cannot defense completely and efficiently by using simple defense strategy. Therefore, intention extraction of attacker is needed for efficient defense. In this thesis, intention extractor of soccer robots is designed and developed based on FMMNN(Fuzzy Min-Max Neural networks ). First, intention for soccer robot system is defined, and intention extraction for soccer robot system is explained.. Next, FMMNN based intention extractor for soccer robot system is determined. FMMNN is one of the pattern classification method and have several advantages: on-line adaptation, short training time, soft decision. Therefore, FMMNN is suitable for soccer robot system having dynamic environment. Observer extracts attack intention of opponents by using this intention exactor, and this intention extractor is also used for analyzing strategy of opponent team. The capability of developed intention extractor is verified by simulation of 3 vs. 3 robot succor simulator. It was confirmed that the rates of intention extraction each experiment increase.

• Communications of Mathematical Education
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• v.34 no.1
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• pp.1-15
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• 2020

Today's healthcare, intelligent robots, smart home systems, and car sharing are already innovating with cutting-edge information and communication technologies such as Artificial Intelligence (AI), the Internet of Things, the Internet of Intelligent Things, and Big data. It is deeply affecting our lives. In the factory, robots have been working for humans more than several decades (FA, OA), AI doctors are also working in hospitals (Dr. Watson), AI speakers (Giga Genie) and AI assistants (Siri, Bixby, Google Assistant) are working to improve Natural Language Process. Now, in order to understand AI, knowledge of mathematics becomes essential, not a choice. Thus, mathematicians have been given a role in explaining such mathematics that make these things possible behind AI. Therefore, the authors wrote a textbook 'Basic Mathematics for Artificial Intelligence' by arranging the mathematics concepts and tools needed to understand AI and machine learning in one or two semesters, and organized lectures for undergraduate and graduate students of various majors to explore careers in artificial intelligence. In this paper, we share our experience of conducting this class with the full contents in http://matrix.skku.ac.kr/math4ai/.

• Journal of Intelligence and Information Systems
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• v.12 no.3
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• pp.67-82
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• 2006

We propose the HBEM(Hybrid Behavior Evolution Model) composed of rule classification and evolutionary neural network using rule descriptor and link descriptor for evolutionary behavior of virtual robots. In our model, two levels of the knowledge of behaviors were represented. In the upper level, the representation was improved using rule and link descriptors together. And then in the lower level, behavior knowledge was represented in form of bit string and learned adapting their chromosomes by the genetic operators. A virtual robot was composed by the learned chromosome which had the best fitness. The composed virtual robot perceives the surrounding situations and they were classifying the pattern through rules and processing the result in neural network and behaving. To evaluate our proposed model, we developed HBES(Hybrid Behavior Evolution System) and adapted the problem of gathering food of the virtual robots. In the results of testing our system, the learning time was fewer than the evolution neural network of the condition which was same. And then, to evaluate the effect improving the fitness by the rules we respectively measured the fitness adapted or not about the chromosomes where the learning was completed. In the results of evaluating, if the rules were not adapted the fitness was lowered. It showed that our proposed model was better in the learning performance and more regular than the evolutionary neural network in the behavior evolution of the virtual robots.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.42-47
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• 2015

This paper presents a rank-based formation for multiple agents based on potential functions, where the proposed method uses the relative position of two neighboring agents. The conventional formation scheme of multiple systems requires communication between agents and a central computer to get the positions of all multiple agents. In the study, differently from previous studies, the formation scheme uses the relative position of two neighboring agents in a local coordinate system. In addition, it introduces a singular agent association that considers only the relative position between an agent and its neighboring agents, instead of multiple associations among all information about all agents. Furthermore, the proposed framework explores the benefits of different formation types. Extensive simulation results show that the proposed approach verifies the viability and effectiveness of the proposed formation.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.599-604
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• 2008

Recently, many researches for autonomous mobile system have been proposed, which can recognize surrounded environment and navigate to destination without outside intervention. The basic sufficient condition for the autonomous mobile system is to navigate to destination safely without accident. In this paper, we propose a path planning method in local region for safe navigation of autonomous system through evaluation of the road surface distortion(damaged/deformed road, unpaved road, obstacle and etc.). We use laser distance sensor to get the information on the road surface distortion and apply image binalization method to evaluate safe region in the detected local region. We show the validity of the proposed method through the computer simulation based on the artificial local road map.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.448-453
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• 2012

There are many researches to develop robots that improve its mobility to adapt in various uneven environments. In the paper, a hybrid wheeled and legged mobile robot is designed and a obstacle avoidance algorithm is proposed based on low power walking using LRF(Laser Range Finder). In order to stabilize the robot's motion and reduce energy consumption, we implement a low-power walking algorithm through comparison of the current value of each motors and correction of posture balance. A low-power obstacle avoidance algorithm is proposed by using LRF sensor. We improve walking stability by distributing power consumption and reduce energy consumption by selecting a shortest navigation path of the robot. The proposed methods are verified through walking and navigation experiments with the developed hybrid robot.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.443-448
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• 2006

In this paper, a novel method is proposed for generating the low-power and stable walking trajectory of biped robots, and then a biped robot with 25 DOFs(degrees of freedom) is designed and implemented for the realization of the low-power walking trajectory generated by the proposed method. In our method, first a stable VPCG(vertically projected center of gravity) trajectory is generated, and then the trajectories of ankle and pelvis of a biped robot are planned to follow the preplanned stable VPCG trajectory, which produces a waking pattern without bending its knees and enables a biped robot to walk with less power consumption. On the other hand, a biped robot implemented in this paper has the mechanical structure of foot that enables a biped robot to support on the ground well, and the mechanical structure of pelvis that enables a biped robot to move flexibly. From results of the walking experiment and power consumption measurement, it was confirmed that the proposed method can generate the more stable and flexible trajectory with less power consumption compared with the existing methods which do not use the ankle of a biped robot.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.383-389
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• 2016

In this paper, we present the hull blasting machine with vision-based weld bead recognition device for cleaning shipment exterior wall. The purpose of this study is to introduce the mechanism design of the high efficiency hull blasting machine using the vision system to recognize the weld bead. Therefore, we have developed a robot mechanism and drive controller system of the hull blasting robot. And hull blasting characteristics such as the climbing mechanism, vision system, remote controller and CAN have been discussed and compared with the experimental data. The hull blasting robots are able to remove rust or paint at anchor, so the re-docking is unnecessary. Therefore, this can save time and cost of undergoing re-docking process and build more vessels instead. The robot uses sensors to navigate safely around the hull and has a filter system to collect the fouling removed. We have completed a pilot test of the robot and demonstrated the drive control and CAN communication performance.