• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.127-130
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• 2002

Human's brain action can divide by recognition and intelligence. recognition is sensing voice, image and smell and Intelligence is logical judgment, inference, decision. To this concept, Define function of cerebral cortex, and apply the result. Current expert system is lack, that reasoning by cerebral cortex and thalamus, hoppocampal and so on. In this paper, With human's brain action, wish to embody human's action artificially Embody brain mechanism using Modular Neural Network, Applied this result to snake robot.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.9
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• pp.978-985
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• 2012

Starfish are a critical problem for fishermen since they eat every farming product including shellfish. The number of starfish is increasing dramatically because they have no natural enemy underwater. We consider the concept of capturing starfish using a semi-autonomous robot. A new underwater robot design to capture starfish is proposed using cooperation between humans and the robot. A requirements list for the robot is developed and two conceptual designs are proposed. Each robot is designed as a modular platform. The kinematic and dynamic performance of each robot is analyzed and compared. This study is a starting point for developing a starfish capture robot and designing underwater robots for other applications. In the near future, a prototype will be assembled and tested in a marine environment.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.314-321
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• 2005

In this paper an intelligence embedding quadruped pet robot is described. It has 15 degrees of freedom and consists of various sensors such as CMOS image, voice recognition and sound localization, inclinometer, thermistor, real-time clock, tactile touch, PIR and IR to allows owners to interact with pet robot according to human's intention as well as the original features of pet animals. The architecture is flexible and adopts various embedded processors for handling sensors to provide modular structure. The pet robot is also used for additional purpose such like security, gaming visual tracking, and research platform. It is possible to generate various actions and behaviors and to download voice or music files to maintain a close relation of users. With cost-effective sensor, the pet robot is able to find its recharge station and recharge itself when its battery runs low. To facilitate programming of the robot, we support several development environments. Therefore, the developed system is a low-cost programmable entertainment robot platform.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.139-146
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• 2002

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 choose 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-output 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 these algorithms 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 space effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. In this paper we use the AMMQL algorithn as a learning method for dynamic positioning of the robot soccer agent, and implement a robot soccer agent system called Cogitoniks.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.496-499
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• 1999

This paper describes a medular neural network(MNN) for a vision system which tracks a given object using a sequence of images from a camera unit. The MNN is used to precisely recognize the given robot endeffector and to minize the processing time. Since the robot endeffector can be viewed in many different shapes in 3-D space, a MNN structure, which contains a set of feedforwared neural networks, co be more attractive in recognizing the given object. Each single neural network learns the endeffector with a cluster of training patterns. The training patterns for a neural network share the similar charateristics so that they can be easily trained. The trained MNN is less sensitive to noise and it shows the better performance in recognizing the endeffector. The recognition rate of MNN is enhanced by 14% over the single neural network. A vision system with the MNN can precisely recognize the endeffector and place it at the center of a display for a remote operator.

• The Journal of Korean Institute for Practical Engineering Education
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• v.4 no.2
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• pp.162-170
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• 2012

It is very important for the robot to recognize its position to accomplish numerous tasks and to go to the goal. In this paper, we suggest Location Recognition System to distinguish robot's locations using land-mark and the odometer in the environment of sensor network. All in all, we created a basic intelligent robot, Location Recognition System, and Environment Sensor Modules; we verified the proposed algorithm through computer simulation.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.471-482
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• 2012

For disaster exploration and surveillance application, this paper aims to present a novel application of a multi-robot agent based on WSN and to evaluate a multi-hop communication caused by the robotics correspondingly, which are used in the uncertain and unknown subterranean tunnel. A Primary-Scout Multi-Robot System (PS-MRS) was proposed. A chain topology in a subterranean environment was implemented using a trimmed ZigBee2006 protocol stack to build the multi-hop communication network. The ZigBee IC-CC2530 modular circuit was adapted by mounting it on the PS-MRS. A physical experiment based on the strategy of PS-MRS was used in this paper to evaluate the efficiency of multi-hop communication and to realize the delivery of data packets in an unknown and uncertain underground laboratory environment.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.539-545
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• 2011

This paper describes an intelligent service robot system for managing a store in an unmanned environment. The robot can be a good replacement for humans because it is possible to work all day and to remember lots of information. We design a system architecture for configuring many intelligent functions of intelligent service robot system which consists of four layers; a User Interaction Layer, a Behavior Scheduling Layer, a Intelligent Module Layer, and a Hardware Layer. We develop an intelligent service robot 'Part Timer' based on the designed system architecture. The 'Part Timer' has many intelligent function modules such as face detection-recognition-tracking module, speech recognition module, navigation module, manipulator module, appliance control module, etc. The 'Part Timer' is possible to answer the phone and this function gives convenient interface to users.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.907-908
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• 2008