• 로봇학회논문지
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• 제18권2호
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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.

• 조명전기설비학회논문지
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• 제20권8호
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• pp.42-47
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• 2006

본 논문에서는 로봇의 동력학에 대한 정확한 지식을 요구하지 않는 로봇 머니퓰레이터를 위한 경계 추정기법을 가진 슬라이딩 모드 제어기를 제안한다. 경계 추정을 위해 로봇 동력학의 불확실한 비선형 요소들의 경계치를 제 1종의 Fredholm 적분식을 이용하여 표현하고, 슬라이딩 평면 함수값만을 이용한 적응 기법을 제안한다. 또한 로봇 동력학의 중요한 두가지 특성인 왜대칭성과 양정치성을 이용하여 로봇 시스템의 점근적 안정성을 증명한다.

• 로봇학회논문지
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• 제5권1호
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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.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
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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.

• 대한전기학회논문지
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• 제38권9호
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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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• 제6권4호
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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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• 제어로봇시스템학회 2005년도 ICCAS
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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.

• 한국지능시스템학회논문지
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• 제14권2호
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• pp.171-177
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• 2004

본 논문에서는, 동적환경 하에서 움직이는 이동 로봇을 위한 인지에 기반한 이동 로봇의 운항계획 알고리즘을 제안한다. 제안된 알고리즘은 크게 ‘지각’과 ‘계획’ 부분으로 구성되어 있으며, ‘지각’은 지식을 구성하는 퍼지 규칙과 센서에서 얻은 데이터를 근거로 하는 위치 추론을 담당하고, ‘계획’은 환경에 대한 지식과 ‘지각’ 과정에서 얻은 위치에 대한 정보를 통해 시작점과 목표점 사이의 경로를 생성한다. ‘지각’과 ‘계획’을 통해 이동 로봇은 애매한 정보와 애매한 지식으로 위치를 추론하고 목표점을 찾아 이동한다. 컴퓨터 모의실험을 통해 제안된 알고리즘의 타당성을 보인다.

• 로봇학회논문지
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• 제9권1호
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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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• 제어로봇시스템학회 2003년도 ICCAS
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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.