• 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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.8
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• pp.42-47
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• 2006

In this paper, we propose a sliding mode control with the bound estimation for robot manipulators without requiring exact knowledge of the robot dynamics. For the bound estimation, the upper bound of the uncertain nonlinearities of robot dynamics is represented as a Fredholm integral equation of the first kind and we propose an adaptive scheme which is only dependent on the sliding surface function. Also, we prove the asymptotic stability for the robot systems using two important properties in the robot dynamics: skew-symmetry and positive-definiteness of robot parameters.

• The Journal of Korea Robotics Society
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• v.5 no.1
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• pp.64-69
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• 2010

In this paper, we propose remote navigation control for intelligent robot using particle swarm optimization(PSO). The proposed system consists of interfaces for intelligent robot navigation and user interface in order to control the intelligent robot remotely. And communication interfaces using TCP/IP socket is used. To do this, we first design the fuzzy navigation controller based on expert's knowledge for intelligent robot navigation. At this time, we use the PSO algorithm in order to identify the membership functions of fuzzy control rules. And then, we propose the remote system in order to navigate the robot remotely. Finally, we show the effectiveness and feasibility of the developed controller and remote system through some experiments.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.244-248
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• 1996

Nowadays, most shop floors using industrial robots have many problems such as constructing robot workcell, generating robot arm moving trajectory, etc.. In the case of programming robot-arms for a specific task, shop operator commonly use the teach pendant to record the target position and determine the moving trajectory. However, such a teaching process may result in an inefficient trajectory in the sense of moving distance and joint angle fluctuation. Moreover, shop operators who have little knowledge about robot programming process need a lot of learning time and cost. The purpose of this paper is to propose a user friendly robot programming method to program robot-arms easily and efficiently for shop operator so that the programming time is reduced and a short and stable trajectory is obtained.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.9
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• pp.740-752
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• 1989

Many works have been reported in various fields on the subject of controlling a robot with high-level robot languages. This paper presents one such effort and explains the development of an automatic robot programming system which utilizes the concept of the task level language. This system is expected to act as an intelligent supporting tool in robot programming and be put into practical use. Emphasis is placed on the role of the programming system as a tool that generates the executable robot program according to the user specified tasks. Several task level commands are used in the developed system, and the resulting inflexibility is complemented by the motion level commands of the motion level robot languages. Thus, the advantages of both task and motion level languages are utilized, and no knowledge of specific language grammer is needed even when using motion level commands. To increase the usability of the developed system, various methods are provided for supplementing the programming system using taught data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.293-298
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• 2006

In this paper, a sensor fusion based robot navigation method for the autonomous control of a miniature human interaction robot is presented. The method of navigation blends the optimality of the Fuzzy Neural Network(FNN) based control algorithm with the capabilities in expressing knowledge and learning of the networked Intelligent Robotic Space(IRS). States of robot and IR space, for examples, the distance between the mobile robot and obstacles and the velocity of mobile robot, are used as the inputs of fuzzy logic controller. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. To identify the environments, a sensor fusion technique is introduced, where the sensory data of ultrasonic sensors and a vision sensor are fused into the identification process. Preliminary experiment and results are shown to demonstrate the merit of the introduced navigation control algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1887-1891
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• 2005

Despite significant advances in multimedia transferring technologies in various fields of robotics, it is sometimes quite difficult for the operator to fully understand the context of 3D remote environments from 2D image feedback. Particularly, in the remote control of mobile robots, the recognition of the object associated with the task is very important, because the operator has to control the robot safely in various situations not through trial and error. Therefore, it is necessary to provide the operator with 3D volumetric models of the object and object-related information as well such as locations, shape, size, material properties, and so on. Thus, in this paper, we propose a vision-based human interface system that provides an interactive, information-rich map through network-based information brokering. The system consists of an object recognition part, a 3D map building part, a networked knowledge base part, and a control part of the mobile robot.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.171-177
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• 2004

In this paper, we propose a cognition-based navigational algorithm for mobile robots in dynamic environments. The proposed algorithm consists of two main stages: (i) the fuzzy logic-based perception stage that constructs knowledge from the sensory data for subsequent usage in reasoning, and (ii) the planning stage that identifies the path between a starting and a goal position within its environment on the basis of the knowledge base on the environment and information from the perception stage. A mobile robot reasons places and moves to goal using ambiguous information and ambiguous knowledge through ‘perception’ and ‘planning’. We provide computer simulation results for a mobile robot in order to show the validity of the proposed algorithm.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.1-10
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• 2014

In recent years, the research of 3D mapping technique in urban environments obtained by mobile robots equipped with multiple sensors for recognizing the robot's surroundings is being studied actively. However, the map generated by simple integration of multiple sensors data only gives spatial information to robots. To get a semantic knowledge to help an autonomous mobile robot from the map, the robot has to convert low-level map representations to higher-level ones containing semantic knowledge of a scene. Given a 3D point cloud of an urban scene, this research proposes a method to recognize the objects effectively using 3D graph model for autonomous mobile robots. The proposed method is decomposed into three steps: sequential range data acquisition, normal vector estimation and incremental graph-based segmentation. This method guarantees the both real-time performance and accuracy of recognizing the objects in real urban environments. Also, it can provide plentiful data for classifying the objects. To evaluate a performance of proposed method, computation time and recognition rate of objects are analyzed. Experimental results show that the proposed method has efficiently in understanding the semantic knowledge of an urban environment.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.651-656
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• 2003

This paper describes research on mental commit robot that seeks a different direction from industrial robot, and that is not so rigidly dependent on objective measures such as accuracy and speed. The main goal of this research is to explore a new area in robotics, with an emphasis on human-robot interaction. Mental commit robots provide psychological, physiological, and social effects to human beings through physical interaction. In the previous research, we categorized robots into four categories in terms of appearance. Then, we introduced a cat robot and a seal robot, and evaluated them by interviewing many people. The results showed that physical interaction improved subjective evaluation. Moreover, a priori knowledge of a subject has much influence into subjective interpretation and evaluation of mental commit robot. In this paper, 95 subjects evaluated the seal robot, Paro by questionnaires in an exhibition at the Japan cultural institute in Rome, Italy for 4 days from June 25th to 28th, 2003. This paper reports the results of statistical analysis of evaluation data.