• Journal of Institute of Control, Robotics and Systems
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• v.16 no.10
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• pp.991-996
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• 2010

In this paper, a new indoor positioning system based on incident angle measurement of infrared sensor has been suggested. Though there have been various researches on indoor positioning systems using vision sensor or ultrasonic sensor, they have not only advantages, but also disadvantages. In a new positioning system, there are three infrared emitters on fixed known positions. An incident angle sensor measures the angle differences between each two emitters. Mathematical problems to determine the position with angle differences and position information of emitters has been solved. Simulations and experiments have been implemented to show the performance of this new positioning system. The results of simulation were good. Since there existed problems of noise and signal conditioning, the experimented has been implemented in limited area. But the results were acceptable. This new positioning method can be applied to any indoor systems that need absolute position information.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.369-372
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• 1993

In this thesis, we consider an iterative learning controller to control the continuous trajectory of 2 links direct drive robot manipulator and process computer simulation and real-time experiment. To improve control performance, we adapt an iterative learning control algorithm, drive a sufficient condition for convergence from which is drived extended conventional control algorithm and get better performance by extended learning control algorithm than that by conventional algorithm from simulation results. Also, experimental results show that better performance is taken by extended learning algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.495-495
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• 2000

The Stewart platform manipulator is a closed-kinematis chain robot manipulator that is capable of providing high st겨ctural rigidity and positional accuracy. However, this is a complex structure, so controllability of the system is not so good. In this paper, it introduces a new robust motion control algorithm using partial state feedback for a class of nonlinear systems in the presence of modelling uncertainties and external disturbances. The major contribution of this work introduces the development and design of robust observer for the slate and the perturbation w.hich is integrated into a variable structure controller(VSC) structure. The combination of controller/observer gives rise to the robust routine called sliding mode control with sliding perturbation observer(SMCSPO). The optimal gains of SMCSPO are easily obtained by genetic algorithm. Simulation and experiment are presented in order to apply to the stewart platform manipulator. There results show highly' accuracy and performance.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1435-1439
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• 2004

Most recently, CP(Cellular Phone) has been one of the most important technologies in the IT(Information Tech-nology) field, and it is situated in a position of great importance industrially and economically. To produce the best CP in the world, a new technological concept and its advanced implementation technique is required, due to the extreme level of competition in the world market. The RT(Robot Technology) has been developed as the next generation of a future technology. Current robots require advanced technology, such as soft computing, human-friendly interface, interaction technique, speech recognition, object recognition etc. unlike the industrial robots of the past. Therefore, this paper explains conceptual research for development of the RCP(Robotic Cellular Phone), a new technological concept, in which a synergy effect is generated by the merging of IT & RT. RCP infra consists of $RCP^{Mobility}$ $RCP^{Interaction}$, $RCP^{Integration}$ technologies. For $RCP^{Mobility}$, human-friendly motion automation and personal service with walking and arming ability are developed. $RCP^{Interaction}$ ability is achieved by modeling an emotion-generating engine and $RCP^{Integration}$ that recognizes environmental and self conditions is developed. By joining intelligent algorithms and CP communication network with the three base modules, a RCP system is constructed. Especially, the RCP mobility system is focused in this paper. $RCP^{Mobility}$ is to apply a mobility technology, which is popular robot technology, to CP and combine human-friendly motion and navigation function to CP. It develops a new technological application system of auto-charging and real-world entertainment function etc. This technology can make a CP companion pet robot. It is an automation of human-friendly motions such as opening and closing of CPs, rotation of antenna, manipulation and wheel-walking. It's target is the implementation of wheel and manipulator functions that can give service to humans with human-friendly motion. So, this paper presents the definition, the basic theory and experiment results of the RCP mobility system. We confirm a good performance of the RCP mobility system through the experiment results.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1178-1183
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• 2003

As personal robots coexist with a person with a role to help a person, while adapting various human life and environment, the personal robots have to accommodate frequently-changing or different-from-home-to-home environment. In addition, personal robots may have many kinds of different Kinematic configurations depending on the capabilities. Some may have a mobile base and others may have arms and a head. The motivation of this study arises from this not-well-defined home environment and varying Kinematic configuration. So the goal of this study is to develop a general control architecture for personal robots. There exist three major architectures; deliberative, reactive and hybrid. We found that these are applicable only for the defined environment with a fixed Kinematic configuration. Neither could accommodate the above two requirements. For the general solution, we propose a Supervised Hybrid Architecture (SHA), in which we use double layers of deliberative and reactive controls, distributed control with a modular design of Kinematic configurations, and real-time Linux OS. Deliberative and reactive actions interact through a corresponding arbitrator. These arbitrators help a robot to choose an appropriate architecture depending on the current situation to successfully perform a given task. The distributed control modules communicate through IEEE 1394 for the easy expandability. With a personal robot platform with a mobile base, two arms, a head and a pan-tilt stereo eye system, we tested the developed SHA for static as well as dynamic environments. For this application, we developed decision-making rules for selecting appropriate control methods for several situations of navigation task. Examples are shown to show the effectiveness.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.783-788
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• 2003

In this paper, we present a new approach to detect and recognize human face in the image from vision camera equipped on the mobile robot platform. Due to the mobility of camera platform, obtained facial image is small and pose-various. For this condition, new algorithm should cope with these constraints and can detect and recognize face in nearly real time. In detection step, ‘coarse to fine’ detection strategy is used. Firstly, region boundary including face is roughly located by dual ellipse templates of facial color and on this region, the locations of three main facial features- two eyes and mouth-are estimated. For this, simplified facial feature maps using characteristic chrominance are made out and candidate pixels are segmented as eye or mouth pixels group. These candidate facial features are verified whether the length and orientation of feature pairs are suitable for face geometry. In recognition step, pseudo-convex hull area of gray face image is defined which area includes feature triangle connecting two eyes and mouth. And random lattice line set are composed and laid on this convex hull area, and then 2D appearance of this area is represented. From these procedures, facial information of detected face is obtained and face DB images are similarly processed for each person class. Based on facial information of these areas, distance measure of match of lattice lines is calculated and face image is recognized using this measure as a classifier. This proposed detection and recognition algorithms overcome the constraints of previous approach [15], make real-time face detection and recognition possible, and guarantee the correct recognition irregardless of some pose variation of face. The usefulness at mobile robot application is demonstrated.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.174-179
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• 2003

Through the simultaneous localization and map building (SLAM) technique, a robot can create maps about its unknown environment while it continuously localizes its position. Grid maps and feature maps have been widely used for SLAM together with application of probability methods and POMDP (partially observed Markov decision process). But this approach based on grid maps suffers from enormous computational burden. Topological maps, however, have drawn more attention these days because they are compact, provide natural interfaces, and are easily applicable to path planning in comparison with grid maps. Some topological SLAM techniques like GVG (generalized Voronoi diagram) were introduced, but it enables the robot to decide only whether the current position is part of GVG branch or not in the GVG algorithm. In this paper, therefore, to overcome these problems, we present a method for updating a global topological map from the local topological maps. These local topological maps are created through a labeled Voronoi diagram algorithm from the local grid map built based on the sensor information at the current robot position. And the nodes of a local topological map can be utilized as the features of the environment because it is robust in light of visibility problem. The geometric information of the feature is applied to the extended Kalman filter and the SLAM in the indoor environment is accomplished. A series of simulations have been conducted using a two-wheeled mobile robot equipped with a laser scanner. It is shown that the proposed scheme can be applied relatively well.

• Journal of The Korean Association of Information Education
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• v.24 no.2
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• pp.131-138
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• 2020

The aim of software education is to increase students' Computational Thinking(CT) skills that they can compose problems and provide solutions which can be carried out effectively by information-processing systems. Furthermore, if problem solving situations can provide students with meaningful problem solving opportunities in authentic social contexts, then software education would be more valuable. This study pursued educational robot-based SW convergence education where 4th grade primary students have access to tangible outputs and can engage in authentic problem solving situations working with peers by using robots and programming. In addition, this study investigated the effectiveness of the classes in terms of computational thinking skills and social capabilities(collaborative skills and communication skills). The current study provides educational robot-based SW convergence education cases of a primary school and discusses the effectiveness of the classes in terms of students' computational thinking skills and social capabilities.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.65-70
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• 2001

We consider a hyper-redundant system that consists of many uniform units. The hyper-redundant system has many degrees of freedom and it can accomplish various tasks. Applysing the reinforcement learning to the hyper-redundant system is very attractive because it is possible to acquire various behaviors for various tasks automatically. In this paper we present a new reinforcement learning algorithm "Q-learning with propagation of motion". The algorithm is designed for the multi-agent systems that have strong connections. The proposed algorithm needs only one small Q-table even for a large scale system. So using the proposed algorithm, it is possible for the hyper-redundant system to learn the effective behavior. In this algorithm, only one leader agent learns the own behavior using its local information and the motion of the leader is propagated to another agents with time delay. The reward of the leader agent is given by using the whole system information. And the effective behavior of the leader is learned and the effective behavior of the system is acquired. We apply the proposed algorithm to a snake-like hyper-redundant robot. The necessary condition of the system to be Markov decision process is discussed. And the computer simulation of learning the locomotion is demonstrated. From the simulation results we find that the task of the locomotion of the robot to the desired point is learned and the winding motion is acquired. We can conclude that our proposed system and our analysis of the condition, that the system is Markov decision process, is valid.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.59-64
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to chose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state- action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem. we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL)as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.