• ETRI Journal
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• v.38 no.6
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• pp.1229-1239
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• 2016

For efficient interaction between humans and robots, robots should be able to understand the meaning and intention of human behaviors as well as recognize them. This paper proposes an interactive human intention reading method in which a robot develops its own knowledge about the human intention for an object. A robot needs to understand different human behavior structures for different objects. To this end, this paper proposes a hierarchical behavior knowledge network that consists of behavior nodes and directional edges between them. In addition, a human intention reading algorithm that incorporates reinforcement learning is proposed to interactively learn the hierarchical behavior knowledge networks based on context information and human feedback through human behaviors. The effectiveness of the proposed method is demonstrated through play-based experiments between a human and a virtual teddy bear robot with two virtual objects. Experiments with multiple participants are also conducted.

• Proceedings of the KAIS Fall Conference
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• 2004.11a
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• pp.166-169
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• 2004

In this paper show how to design a redundant robot which is suitable for the multiple task without any constraints on the workspace. The implementation is possible by the rigid connection of a mobile robot and a task robot. Use a five joint articulated robot as the task robot; designed the 3 joint mobile robot for this usage. For a task execution assigned to the redundant robot, not only the task robot but the mobile robot should work in the coordinated way. therefore, a kinematic connection of the two robots should be cleary represented in a frame. And, also the dynamic interaction between the two robots needs to be analyzed. Clarified these issues considering the control of the redundant robot. Finally, demonstrate away of utilization of the redundancy as the cooperation between the mobile robot and the task robot to execute a common task.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2998-3000
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• 1999

Robot soccer is a challenging research area in which multiple robots collaborate in adversarial environment to achieve specific objectives. We designed and built the robotic agents for robot soccer, especially MIROSOT. We have been developing the appropriate vision algorithm, algorithm for ball tracking and prediction, algorithms for collaboration between the robots in an uncertain dynamic environment. In this work we focus on the development of ball tracking and prediction algorithm using Kalman filter. Robustness and feasibility of the proposed algorithm is demonstrated by simulation.

• Advances in robotics research
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• v.2 no.3
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• pp.219-236
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• 2018

This research focuses on controlling robots and their formations using rough mereology as a means for spatial reasoning. The authors present the state of the art theory behind path planning, robot cooperation domains and ways of creating robot formations. Furthermore, the theory behind Rough Mereology as a way of implementing mereological potential field based path creation and navigation for single and multiple robots is described. An implementation of the algorithm is shown in simulation using RoboSim simulator. Five formations are tested (Line, Rhomboid, Snake, Circle, Cross) along with three decision systems (First In, Leader First, Horde Mode) as compared to other methods.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.225-232
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• 2023

With the recent increase of multiple robots cooperating in smart manufacturing logistics environments, it has become very important how to predict the safety and efficiency of the individual tasks and dynamically assign them to the best one of available robots. In this paper, we propose a novel task policy learner based on deep relational reinforcement learning for predicting the safety and efficiency of tasks in a multi-robot manufacturing logistics environment. To reduce learning complexity, the proposed system divides the entire safety/efficiency prediction process into two distinct steps: the policy parameter estimation and the rule-based policy inference. It also makes full use of domain-specific knowledge for policy rule learning. Through experiments conducted with virtual dynamic manufacturing logistics environments using NVIDIA's Isaac simulator, we show the effectiveness and superiority of the proposed system.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.175-178
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• 2007

기존의 싱글 로봇 시스템이 할 수 없거나 하기 어려운 작업을 다개체 이동 로봇 시스템을 사용함으로써 수행 할 수 있게 되었으며, 로봇 간의 협력적인 작업을 통하여 성능 향상을 가져오게 되었다. 이러한 장점 때문에 상용화가 잘 이루어진 청소 로봇에서도 다개체 로봇 시스템 적용에 대한 연구가 이루어지고 있다. 하지만 기존의 다개체 청소로봇 시스템은 고가의 매핑 방식 청소 로봇을 사용하기 때문에 비용적인 문제에 있어서 큰 문제를 가지고 있다. 그렇기 때문에 본 논문에서는 인간과의 상호 보완 알고리즘을 이용한 청소로봇을 다개체 청소 로봇 시스템에 적용하여 가격적인 단점을 보완하려고 한다. 하지만 인간과의 상호 보완 알고리즘을 이용한 청소 로봇을 그대로 다개체 청소 로봇 시스템에 적용할 수 없기 때문에 이를 보완할 수 있는 방안을 제안하고자 한다.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.357-364
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• 2014

AR contents, if markers to be tracked move fast, show flickering while updating images captured from cameras. Conventional methods employing image based markers and SLAM algorithms for tracking objects have the problem that they do not allow more than 2 objects to be tracked simultaneously and interacted with each other in the same camera scene. In this paper, an improved SLAM type algorithm for tracking dynamic objects is proposed and investigated to solve the problem described above. To this end, method using 2 virtual cameras for one physical camera is adopted, which makes the tracked 2 objects interacted with each other. This becomes possible because 2 objects are perceived separately by single physical camera. Mobile robots used as dynamic objects are synchronized with virtual robots in the well-designed contents, proving usefulness of applying the result of individual tracking for multiple moving objects to augmented visualization of objects.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.211-217
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• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the teaming control field was teaming in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Error wave propagation method will show up in the numerical simulation for five-bar linkage as a vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link at each time step in repetition domain. Those can be helped to apply to the vertical multiple dynamic systems for precision quality assurance in the industrial robots and medical equipments.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.4
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• pp.54-62
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• 2011

Emotional reaction should be different from the purpose of the robot system. The method for emotional reaction is also different from the specification of the robot system. Therefore, emotional behavior decision model, which is applied to social robots regardless of specifications and purposes, is necessary. This paper introduces a universal emotional behavior decision model designed for applying to various social robots that have different specifications and purposes. Multiple emotions, a set of probability value of every emotion, are calculated independently and expressed according to the purpose of the robot system. Then, behavior, for emotional reaction according to the calculated multiple emotions, is decided regarding the specification of the robot system. The decided behavior is a combination of unit behaviors that indicates the smallest expressible behaviors in each expression parts. It is possible to express various undefined behaviors by generating unit behavior combinations according to multiple emotions. The universal emotional behavior decision model is applied to three kinds of social robot systems that have different specifications and purposes.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.2
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• pp.40-47
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• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work the authors presented an iterative precision of linear decentralized learning control based on p-integrated teaming method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the loaming control field was learning in robots doing repetitive tasks such as on a]1 assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Error wave propagation method will show up in the numerical simulation for five-bar linkage as a vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link at each time step in repetition domain. Those can be helped to apply to the vertical multiple dynamic systems for precision quality assurance in the industrial robots and medical equipments.