• Title/Summary/Keyword: Robot Task Planning

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An Intention-Response Model based on Mirror Neuron and Theory of Mind using Modular Behavior Selection Networks (모듈형 행동선택네트워크를 이용한 거울뉴런과 마음이론 기반의 의도대응 모델)

  • Chae, Yu-Jung;Cho, Sung-Bae
    • Journal of KIISE
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    • v.42 no.3
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    • pp.320-327
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    • 2015
  • Although service robots in various fields are being commercialized, most of them have problems that depend on explicit commands by users and have difficulty to generate robust reactions of the robot in the unstable condition using insufficient sensor data. To solve these problems, we modeled mirror neuron and theory of mind systems, and applied them to a robot agent to show the usefulness. In order to implement quick and intuitive response of the mirror neuron, the proposed intention-response model utilized behavior selection networks considering external stimuli and a goal, and in order to perform reactions based on the long-term action plan of theory of mind system, we planned behaviors of the sub-goal unit using a hierarchical task network planning, and controled behavior selection network modules. Experiments with various scenarios revealed that appropriate reactions were generated according to external stimuli.

Design and Implementation of Tripodal Schematic Control Architecture for Multi-Functional Service Robots

  • Kim, Gun-Hee;Chung, Woo-Jin;Kim, Mun-Sang;Lee, Chong-Won
    • 제어로봇시스템학회:학술대회논문집
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    • 2003.10a
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    • pp.2045-2050
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    • 2003
  • This paper describes the development of service robotic systems with the Tripodal schematic control architecture. We show practical advantages of the proposed architecture by giving examples of our experience. First, we explain how to add new task using Tripodal architecture approach. The Tripodal architecture provides some crucial organizing principles and core components that are used to build the basis for the system. Thus, the newly developed behaviors, motion algorithm, knowledge, and planning schemes are arranged so as to guarantee the efficiency of the performance of components. Second, we describe the reusability and scaleability of our architecture by introducing the implementation process of the guide robot Jinny. Most of modules developed for former robots like PSR-1 and PSR-2 systems are used directly to the Jinny system without significant modification. Experimental results clearly showed that the developed strategy is useful, even if the hardware configurations as well as software algorithms are more complex and more accumulating.

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Robot Task Planning within a Distributed Control Framework (분산 제어 구조내의 로봇 작업 계획)

  • Kim, Hyun-Sik;Shin, Hang-Cheol;Kim, Man-Soo;Kim, In-Cheol
    • Proceedings of the Korean Information Science Society Conference
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    • 2006.10b
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    • pp.319-323
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    • 2006
  • 본 논문에서는 동작 모델과 작업 목표에 따라 지능 로봇 시스템의 작업 순서를 결정하는 작업 계획기의 설계와 데모시스템에 대해 설명한다. 블랙보드 중심의 분산 제어 구조에서 하나의 독립적인 지식원으로 동작하는 작업 계획기는 작업 관리기의 요청이 있을 때마다 지식베이스로부터 동작 모델과 월드 상태 정보를 제공받아 작업 목표 달성을 위한 작업 계획을 생성한다. 그리고 이렇게 생성된 작업 계획은 오류로 인해 재 계획이 필요할 때까지 작업 관리기를 통해 실행된다. 본 연구의 작업 계획기는 효율적인 작업 계획 생성을 위해 지역 상태공간 탐색법의 하나인 EHC+ 탐색법과 계획그래프-기반의 휴우리스틱 계산법을 적용한다. 본 논문에서는 작업 계획기의 효율성과 블랙보드와의 연계성을 시험하기 위한 데모 시스템에 대해 자세히 설명한다. 이를 통해 지식베이스, 작업 관리기, 컴포넌트 서비스기 등 로봇 제어 구조내의 다른 지식원들과의 인터페이스를 위한 메시지 설계도 소개한다.

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Development of a Single-Arm Robotic System for Unloading Boxes in Cargo Truck (간선화물의 상자 하차를 위한 외팔 로봇 시스템 개발)

  • Jung, Eui-Jung;Park, Sungho;Kang, Jin Kyu;Son, So Eun;Cho, Gun Rae;Lee, Youngho
    • The Journal of Korea Robotics Society
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    • v.17 no.4
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    • pp.417-424
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    • 2022
  • In this paper, the developed trunk cargo unloading automation system is introduced, and the RGB-D sensor-based box loading situation recognition method and unloading plan applied to this system are suggested. First of all, it is necessary to recognize the position of the box in a truck. To do this, we first apply CNN-based YOLO, which can recognize objects in RGB images in real-time. Then, the normal vector of the center of the box is obtained using the depth image to reduce misrecognition in parts other than the box, and the inner wall of the truck in an image is removed. And a method of classifying the layers of the boxes according to the distance using the recognized depth information of the boxes is suggested. Given the coordinates of the boxes on the nearest layer, a method of generating the optimal path to take out the boxes the fastest using this information is introduced. In addition, kinematic analysis is performed to move the conveyor to the position of the box to be taken out of the truck, and kinematic analysis is also performed to control the robot arm that takes out the boxes. Finally, the effectiveness of the developed system and algorithm through a test bed is proved.

Development of Intelligent Excavating System - Introduction of research progress - (지능형 굴삭시스템 개발 - 2차 년도 연구내용 -)

  • Seo, Jong-Won;Kim, Young-Wook;Jang, Dal-Sik;Lee, Seung-Soo
    • Proceedings of the Korean Institute Of Construction Engineering and Management
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    • 2008.11a
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    • pp.184-192
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    • 2008
  • Recently, one of the solutions for the problems of construction industry such as low productivity, lack of experts, insufficiency of manpower, high percentage of calamity and so forth, construction automation which let underdeveloped construction production system be a ultramodern technology is under research. Internal study of construction automation has been initiated since 1980s focusing on robotics and semi-automation for reduction of labor. Therefore development of construction robots is being concentrated with the high development of information technology and Intelligent Excavating System(IES) project had been launched by ministry of land, transportation, and maritime affairs as one way of construction technology revolution business. This study introduces the final goal and the research progress until second year of IES.

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