• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1073-1078
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• 2012

This paper deals with the trajectory planning of multi agent robots using complex potential theory for robot soccer. The complex potential theory is introduced, then the circle theorem is used to avoid obstacles, and the vortex pair is used to make precise kicking direction of robot. Various situations of robot soccer are simulated and the effect of vortex strength and the speed of robots are discussed and the better way to avoid obstacles and to kick the precise direction is found. The feasibilities of complex potential theory to apply for the multi agent robots are successful.

• 한국IT서비스학회:학술대회논문집
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• 2007.11a
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• pp.373-377
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• 2007

유비쿼터스 환경에서 사용자에게 유용한 서비스를 제공하기 위해서는 기능을 명시적으로 제공하기 보다는 사용자의 요구에 따라서 능동형으로 제공하는 기능이 필요하다. 이러한 능동형 서비스는 알아서 서비스를 수행하는 기능으로 CAMUS 시스템에서는 서비스 탐색 부분에서 사용된다. 즉 시스템의 상황에 따라서 메시지 시스템을 구현하는 경우에 자원의 형태에 따라서 영상 메세징, TTS 기능을 이용한 메세징, Text 기반 메세징 시스템으로 자동적으로 처리할 수 있다. 이를 위해 CAMUS 서버에는 SAM(Service Agent Manager) 과의 통신을 통해 환경 내에 존재하는 센서와 장치들 (Service Agent)을 관리 및 제어한다. 이러한 Service Agent Manager 는 여러 다양한 환경에 설치되어 환경 내에 위치한 다양한 센서로부터 정보를 얻고 그 정보를 CAMUS 메인 서버에 전달하는 한편 CAMUS 메인 서버로부터 실행명령을 받아 환경 내 장치를 제어하는 역할을 한다. 이러한 Service Agent Manager는 임의의 공간 내에 설치될 수 있으며 로봇단말이나 개인 휴대단말 등에도 설치될 수 있다 이 논문에서는 SAM(Service Agent Manager) 과 CAMUS 서버에서 원하는 서비스를 탐색하는 방법에 대한 내용을 기술하였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.434-438
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• 1996

In this paper we present the multi-agent robot system and the vision system developed for participating in micro robot soccer tournament. The multi-agent robot system consists of micro robot, a vision system, a host computer and a communication module. Micro robot are equipped with two mini DC motors witf encoders and gearboxes, a R/F receiver, a CPU and infrared sensors for obstacle detection. A vision system is used to recognize the position of the ball and opponent robots, position and orientation of our robots. The vision system is composed of a color CCD camera and a vision processing unit(AISI vision computer). The vision algorithm is based on morphological method. And it takes about 90 msec to detect ball and 3-our robots and 3-opponent robots with reasonable accuracy

• ETRI Journal
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• v.29 no.2
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• pp.169-178
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• 2007

With the advent of ubiquitous computing environments, it has become increasingly important for applications to take full advantage of contextual information, such as the user's location, to offer greater services to the user without any explicit requests. In this paper, we propose context-aware active services based on context-aware middleware for URC systems (CAMUS). The CAMUS is a middleware that provides context-aware applications with a development and execution methodology. Accordingly, the applications based on CAMUS respond in a timely fashion to contextual information. This paper presents the system architecture of CAMUS and illustrates the content recommendation and control service agents with the properties, operations, and tasks for context-aware active services. To evaluate CAMUS, we apply the proposed active services to a TV application domain. We implement and experiment with a TV content recommendation service agent, a control service agent, and TV tasks based on CAMUS. The implemented content recommendation service agent divides the user's preferences into common and specific models to apply other recommendations and applications easily, including the TV content recommendations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.9-11
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• 2022

Applying Reinforcement Learning in everyday applications and varied environments has proved the potential of the of the field and revealed pitfalls along the way. In robotics, a learning agent takes over gradually the control of a robot by abstracting the navigation model of the robot with its inputs and outputs, thus reducing the human intervention. The challenge for the agent is how to implement a feedback function that facilitates the learning process of an MDP problem in an environment while reducing the time of convergence for the method. In this paper we will implement a reward shaping system avoiding sparse rewards which gives fewer data for the learning agent in a ROS environment. Reward shaping prioritizes behaviours that brings the robot closer to the goal by giving intermediate rewards and helps the algorithm converge quickly. We will use a pseudocode implementation as an illustration of the method.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.149.5-149
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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 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 ...

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2023-2028
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• 2004

This paper reveals the utilization of the multi-gent system that based on the Blackboard system basis as the controller of Soccer Robot. This system is a portion of developing the Soccer Robot team for Robocup 2004 Competition. In this development, the intelligent control system was initiated by the combination of parallel and distributed blackboard structures with the principle design that generated from human body structures, which consists of the combination of two main systems, the organs system and the brain system. The system is designed using the control system theory based on Blackboard basis. Modification of the initial structures to corroborate the Soccer Robot and the structure's constituents are clarified accordingly. To demonstrate the idea, ITE-old team is given as a case study.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.43-46
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• 2001

This thesis proposes the design method of intelligent robot agent system and deals with the implementation of the system which is able to produce key internet statistics. It is believed that the statistics lead to effective investment from internet industry on its development. The system consists of robot agent process module, statistics production module and management module, and has an algorithm that can produce periodically the number of domestic homepages, active domain using .kr or gTLD and intenet hosts. It provides the result of the implementation and performance of the system as well.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10e
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• pp.574-576
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• 2002

인터넷상에서의 정보검색은 검색엔진을 이용하여 이루어지는데, 방대한 사이트들을 검색하여야 하므로 검색효율이나 검색된 정보의 유용성에 문제가 있게 된다. 만약 이러한 정보들을 미리 자동적으로 검색, 분류해서 저장한다면 위의 두 가지 문제들을 해결할 수 있을 것이다. 자동적으로 이런 일을 처리하도록 고안된 것이 웹 로봇 에이전트라고 하며 현재국내에도 여러 개의 웹 로봇 에이전트를 이용한 검색엔진이 사용되고 있다. 본 논문에서는 검색엔진을 구현하기 위해 하이퍼텍스트 전송규약에 대한 연구와 웹 로봇 에이전트에 대한 연구를 하여 올바른 로봇 에이전트를 구현하여, 구현된 검색엔진을 통한 효율적인 정보검색을 실현하는데 목적이 있다.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.292-297
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• 1999

Evolving in artificial agent is an extremely difficult problem, but on the other hand, a challenging task. At present the studies mainly centered on single agent learning problem. In our case, we use simulated soccer to investigate multi-agent cooperative learning. Consider the fundamental differences in learning mechanism, existing reinforcement learning algorithms can be roughly classified into two types-that based on evaluation functions and that of searching policy space directly. Genetic Programming developed from Genetic Algorithms is one of the most well known approaches belonging to the latter. In this paper, we give detailed algorithm description as well as data construction that are necessary for learning single agent strategies at first. In following step moreover, we will extend developed methods into multiple robot domains. game. We investigate and contrast two different methods-simple team learning and sub-group loaming and conclude the paper with some experimental results.