• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.282-287
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• 2008

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.2
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• pp.107-113
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• 2010

In this paper, a prototype of cooperative work model for multi-robots system is introduced and the evolutionary approach is applied to generate the motions for the cooperative works of multi-robots system using genetic algorithm. The cooperative tasks can be performed by a humanoid robot and a mobile robot to deliver objects from shelves. Generation of the humanoid motions such as pick up, rotation, and place operation for the cooperative works are evolved. Modeling and computer simulation for the cooperative robots system are executed in Webots environments. Experimental results show the feasible and reasonable solutions for humanoid cooperative tasks are obtained.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.556-559
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• 1997

In this paper, we propose a cooperative multi-robot control algorithm. Specifically, the cooperative task is that two mobile robots should transfer a long rigid object along a predefined path. To resolve the problem, we introduce the master-slave concept for two mobile robots, which have the same structure. According to the velocity of the master robot and the positions of two robots on the path, the velocity of the slave robot is determined. In case that the robots can't move further, the role of the robot is interchanged. The effectiveness of this decentralized algorithm is proved by computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.555-557
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• 1992

Recently, realization of an intelligent cooperative interaction system between a man and robot systems is required. In this paper, HyperCard with a voice control is used for above system because of its easy handling and excellent human interfaces. Clicking buttons in the HyperCard by a mouse device or a voice command means controlling each joint of a robot system. Robot teaching operation of grasping a bin and pouring liquid in it into a cup is carried out. This robot teaching method using HyperCard provides a foundation for realizing a user friendly cooperative interaction system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.346-349
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• 1995

In this paperwe develop a real-time simulator for direct drive cooperative robot by using OpenGL in a Windows NT based system. This simulator is composed of 2 parts, a display part and an interface part. In the display part the robot is modelled and rendered in 3D space. To do this OpenGL, a kind of graphic library, is used for rendering and animating robots and kinematics gives the information of the current robot configuration. The control and the feedback data are sent and received via the interface part. In real time simulation interfacing part needs fast data transfer rate and good nosic immunity. In experiment we have simulated 2-link direct drive cooperative robots using the trajectory tracking algorithm proposed in reference.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.942-946
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• 2005

In this paper, we propose that the mutual cooperative control in the chaotic mobile robot. In order to achieve mutual cooperative control in the mobile robot, we apply coupled synchronization technique and driven synchronization technique in the mobile robot with obstacle.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.355-359
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• 2005

In this paper, we propose that the synchronization method for mutual cooperative control in the mobile robot. In order to achieve the synchronization for mutual cooperative control in the mobile robot, we apply coupled synchronization technique and driven synchronization technique in the mobile robot with obstacle.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.264-272
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• 2022

Human-robot cooperative tasks are increasingly required in our daily life with the development of robotics and artificial intelligence technology. Interactive reinforcement learning strategies suggest that robots learn task by receiving feedback from an experienced human trainer during a training process. However, most of the previous studies on Interactive reinforcement learning have required an extra feedback input device such as a mouse or keyboard in addition to robot itself, and the scenario where a robot can interactively learn a task with human have been also limited to virtual environment. To solve these limitations, this paper studies training strategies of robot that learn table balancing tasks interactively using deep reinforcement learning with human's facial expression feedback. In the proposed system, the robot learns a cooperative table balancing task using Deep Q-Network (DQN), which is a deep reinforcement learning technique, with human facial emotion expression feedback. As a result of the experiment, the proposed system achieved a high optimal policy convergence rate of up to 83.3% in training and successful assumption rate of up to 91.6% in testing, showing improved performance compared to the model without human facial expression feedback.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.10
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• pp.50-58
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• 1993

The problem of trajectory planning in two redundant robot systems is considered. The trajectory of each robot for the cooperative task is generated so that the robots assume their optimal configurations while following a given desired task. The cooperative task compatibility in and the weighted sum of manipulabilities are proposed and investigated as quality measures. The cooperative task compatibility includes the velocity and force transmission charateristics to the task requirements and so it measures the compatibilities of robot postures with respect to a given task. The weighted sum of manipuabilities of robot postures with respect to a given task. The weighted sum of manipulabilities is also considered as a quality measure since it is helpful to avoid singularities. The usefulness of the cooperative task compatibility and the weighted sum of manipulabilities are shown by computer simulation studies.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.1
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• pp.121-129
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• 2017

In this study, we investigated a cooperative control system of assembly robots using wireless LAN. We developed two different types of robots to assemble three blocks on a workbench. Robot1 can assemble blocks on a workbench and Robot2 can carry blocks to Robot1. We constructed an ROS-based communication system and shared data. Three blocks and one workbench were recognized by camera-image processing By developing the UI using Windows programming language Visual C#, we evaluated the status of the robots and blocks and controlled the robots. The control system was developed by constructing all elements necessary for cooperative control, such as robot design and fabrication, motor control, ROS-based communication, and image processing. Thus, we completed fundamental tasks required for assembly.