• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2342-2345
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• 2005

Robot control system considers various requirements. Firstly, it needs adaptation for unpredictable and dynamic environment. Secondly, it needs way to make do not injurious action to human because live with a person. Thirdly, it needs processing about aim of robots. In this paper proposed that these requirements effective robot control architecture. Robot control architecture can divide Deliberative, Reactive, Hybrid. Recently, robot control architecture that come Deliberative and use hybrid architecture that apply advantage of Reactive architecture is studied much. Hybrid control purpose to combine the real-time response of Reactive with the rationality of Deliberative. Our purpose is enhancement of hybrid architecture that is studied in these days. Proposed architecture that applied human's nervous system can reduce relativity between each module of existent architecture and drive response speed guarantee and safe robot action.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.149.4-149
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• 2001

In this paper, the multiple micro robot soccer playing system is introduced at first. Learning and evolving in artificial agents is a difficult problem, but on the other hand a challenging task. In our laboratory, this soccer studies mainly centered on single agent learning problem. The construction of such experimental system has involved lots of kinds of challenges such as robot designing, vision processing, motion controlling. At last we will give some results showing that the proposed approach is feasible to guide the design of common agents system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.45-49
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• 2006

홈네트워크 시스템의 본격적인 보급과 함께 가정용서비스 로봇의 최근 연구 성과들은 인간과 지능로봇이 가정에 공존하며 서로 의사소통을 할 수 있는 시대가 가까운 미래에 현실화 될 수 있음을 보여주고 있다. 그러나 가정의 환경적인 특징은 open되어 있기 때문에 그러한 환경에 적응하고 주어진 임무를 수행하는 데는 단일 로봇 또는 단일 홈서버 보다는 로봇을 포함하는 홈네트워크 시스템 내의 여러 장치들이 어울려 분산처리를 수행하는 multi-agent 시스템이 일반적으로 더 좋다고 알려져 있다. 따라서 본 논문은 홈네트워크 시스템 환경에서 가정에서 필요한 agent들을 정의하기 위한 framework 모델을 구축하고 각 agent 간의 통신 protocol architecture를 제시한다. 또한 로봇 또는 홈서버의 단일 지능이나 기능보다는 그 안에 존재하는 복수개의 agent instance들의 집합으로 agent를 정의하고 각 agent 내외에서 agent들 사이의 협력(cooperation)과 (타협)negotiation을 통해 환경과 적응하는 방법 및 사람과 교감(interactive)하는 방법을 제시한다.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.621-624
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• 1997

This paper presents the design procedure for soccer-playing rovots based on the centralized approach. Using a fast vision system, we obtain the configuration of each robot and then the host computer computes the desired motion and commands each robot directly via RF communication. The robot soccer game has a lot of problems such as obstacle avoidance, coordination between robots, dribbling the ball, and so on. To implement such motions, we think that the centralized approach seems to be more powerful than the distributed approach. We describe the technical tips for developing the robots in detail here and explain our strategy for getting the scores.

• 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.

• The Transactions of the Korea Information Processing Society
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• v.7 no.11S
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• pp.3732-3838
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• 2000

As the Internet sites and users have rapidly been increased, the development for search engines has also been accelerated to satisfy users' expectations. As the result, not only the action of collecting documents through many search engines gave hosts workload, but also regular updating all the information is needed since information is newly added. With the circumstances, the necessity of the technology to collect massive information in hosts has been increased for the speed which is a basic requisite of search systems, and for more accurate collection of documents. Also, the role of search engines grows bigger for Internet users' various demands and flexible process through World Wide Web. In this paper, we design and implement a robot agent and a remote control system which doesn't give an excessive workload on a target server and makes the collection of documents done in a short period by considering an average workload rate on the target server and the rate of the workload that a robot experience in collection time, after we compare and analyze the existing Robot Agent Systems and supplement their weak points.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.49-58
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• 2009

Top-down approach in the intelligent robot research has focused on the single object intelligence however, it has two weaknesses. One is that has a high cost and a long spending time of sensing, calculating and communications. The other is the difficulty of responding to react changes in the unpredictable environment. we propose the collective intelligence algorithm based on Bottom-up approach for improving these weaknesses and the applied agent model and verify by simulation. The Modified Flocking Algorithm proposed in this research is the algorithm which is modified version of the concept of the Flocking (Craig Reynolds) which is used to model the flocks, herds, and schools in the graphics or games, and simplified the operation of conventional Flocking algorithm to make it easy to apply for the number of group robots. We modeled the Boid agent and verified possibility collectivization of the Modified Flocking Algorithm by simulation. And We validated by the actual multiple mobile robot experiment.

• 전자공학회논문지 IE
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• v.44 no.4
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• pp.11-18
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• 2007

Concept of URC does "with me wherever when, and the robot" which provides necessary service to me can be simply defined. This paper uses URC technology and various robots are implemented with a design. That is, we are going to implement that a user controls a virtual robot by communication between URC server with a design. We used an intelligent robot simulation tool, and a developer was easy, and it was intelligent, and we were connected to active URC server, and modeling did a system for simulation to be able to do an URC robot usefully. It was connected to an URC system and various robots and environments were composed with 3D, and, in this paper, a design and implementation did an intelligent robot simulation system so that it was possible by various contents development through simulation. The URC communication protocol and the URC server were based on a Planet v.1.2 ; Network Protocol, CAMUS(Context-Aware Middleware for URC Systems); URC Server, SAM(Service Agent Manager) v.1.2 ; Service API module developed in Electronics & Telecommunications Research Institute (ETRI).

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.40-49
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• 2019

Reinforcement learning has been applied to various problems in robotics. However, it was still hard to train complex robotic manipulation tasks since there is a few models which can be applicable to general tasks. Such general models require a lot of training episodes. In these reasons, deep neural networks which have shown to be good function approximators have not been actively used for robot manipulation task. Recently, some of these challenges are solved by a set of methods, such as Guided Policy Search, which guide or limit search directions while training of a deep neural network based policy model. These frameworks are already applied to a humanoid robot, PR2. However, in robotics, it is not trivial to adjust existing algorithms designed for one robot to another robot. In this paper, we present our implementation of Guided Policy Search to the robotic arms of the Baxter Research Robot. To meet the goals and needs of the project, we build on an existing implementation of Baxter Agent class for the Guided Policy Search algorithm code using the built-in Python interface. This work is expected to play an important role in popularizing robot manipulation reinforcement learning methods on cost-effective robot platforms.

• Science of Emotion and Sensibility
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• v.24 no.3
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• pp.115-124
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• 2021

Non-verbal communication is important in human interaction. It provides a layer of information that complements the message being transmitted. This type of information is not limited to human speakers. In human-robot communication, increasing the animacy of the robotic agent-by using non-verbal cues-can aid the expression of abstract concepts such as emotions. Considering the physical limitations of artificial agents, robots can use light and movement to express equivalent emotional feedback. This study analyzes the effects of LED and motion animation of a spherical robot on the emotion being expressed by the robot. A within-subjects experiment was conducted at the University of Tsukuba where participants were asked to rate 28 video samples of a robot interacting with a person. The robot displayed different motions with and without light animations. The results indicated that adding LED animations changes the emotional impression of the robot for valence, arousal, and dominance dimensions. Furthermore, people associated various situations according to the robot's behavior. These stimuli can be used to modulate the intensity of the emotion being expressed and enhance the interaction experience. This paper facilitates the possibility of designing more affective robots in the future, using simple feedback.