• 제목/요약/키워드: peg-in-hole task

검색결과 23건 처리시간 0.02초

재구성된 그래픽 모델을 이용한 원격제어 (Teleoperation Using Reconstructed Graphic Model)

  • 정성엽;윤현중
    • 한국산학기술학회논문지
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    • 제13권9호
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    • pp.3876-3881
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    • 2012
  • 일반적으로 마스터/슬레이브 원격제어 시스템에서 작업자는 원격에서 슬레이브를 제어하기 위하여 카메라 영상 같은 시각 정보를 이용하는 경우가 많은데, 많은 데이터 양으로 인하여 원격지의 영상 정보에 지연이 발생할 수 있고 카메라 위치에 따라 영상 정보가 제한되거나 불완전하여 제어가 어려운 경우가 생길 수 있다. 카메라 영상을 이용한 원격제어 시스템의 이러한 문제점을 해결하기 위하여 본 논문에서는 영상 정보 대신에 3차원 재구성 그래픽 모델을 이용한 원격제어 시스템을 제안한다. 제안된 원격제어시스템은 로봇 제어 모듈, 힘반향 조이스틱을 이용한 마스터 모듈, 그래픽유저인터페이스모듈로 이루어져 있는데, 여기서 그래픽유저인터페이스모듈은 원격지에서 전달된 적은 양의 센싱 데이터를 이용하여 3차원으로 재구성된 그래픽 모델을 작업자에게 제공해 준다. 제안된 원격제어 시스템은 펙인홀(peg-in-hole) 조립 작업을 이용하여 그 효과가 검증되었다.

시청각적 모델링의 관찰이 뇌졸중 환자의 상지기능 재활에 미치는 영향 (Effect of the Observation of an Audio-Visual Modeling on the Rehabilitation of Upper Limb Function in Stroke Patients)

  • 박상범;김미현
    • 한국전문물리치료학회지
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    • 제14권2호
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    • pp.1-10
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    • 2007
  • The purpose of this experiment was to investigate the applicability of audio-visual modeling for improving the efficiency of rehabilitative programs by analyzing the effects of observing these various models on the capacity of stroke patients to perform upper limb activities. Twenty-one stroke patients participated in the experiment and were randomly assigned to either task modeling, sport modeling, or control group. During 2 weeks of intervention, subjects in all groups participated in the physical practice of experimental tasks. These tasks comprised of a Nine Hole Peg Test, the Jebsen-Taylor Hand Function tests, and locomotion. These tasks were performed 5 days a week, 30 min per day. In addition to the physical practice, the task modeling group observed a model performing experimental tasks and locomotive activities for 20 min, while the sport modeling group observed a model performing various sport activities for 20 min. Subjects' ability to perform the experimental tasks was measured 3 times, before, immediately after, and 1 week after the intervention. Analyses of the capacity to perform upper extremity activities displayed significant improvement from the pre-test to immediate and delayed post-tests in all groups. However, the amount of improvement was the highest in the task modeling group. The task modeling group was superior to the control group in the post-test of all experimental tasks, whereas the sport modeling group did not display significant differences from the control group. These results suggest that audio-visual modeling can be used as an effective cognitive intervention for facilitating the rehabilitation of stroke patients, and its rehabilitative effect can be maximized when the program is comprised of performance scenes directly related to the target task.

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서비스 자동화 시스템을 위한 물체 자세 인식 및 동작 계획 (Object Pose Estimation and Motion Planning for Service Automation System)

  • 권영우;이동영;강호선;최지욱;이인호
    • 로봇학회논문지
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    • 제19권2호
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    • pp.176-187
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    • 2024
  • Recently, automated solutions using collaborative robots have been emerging in various industries. Their primary functions include Pick & Place, Peg in the Hole, fastening and assembly, welding, and more, which are being utilized and researched in various fields. The application of these robots varies depending on the characteristics of the grippers attached to the end of the collaborative robots. To grasp a variety of objects, a gripper with a high degree of freedom is required. In this paper, we propose a service automation system using a multi-degree-of-freedom gripper, collaborative robots, and vision sensors. Assuming various products are placed at a checkout counter, we use three cameras to recognize the objects, estimate their pose, and create grasping points for grasping. The grasping points are grasped by the multi-degree-of-freedom gripper, and experiments are conducted to recognize barcodes, a key task in service automation. To recognize objects, we used a CNN (Convolutional Neural Network) based algorithm and point cloud to estimate the object's 6D pose. Using the recognized object's 6d pose information, we create grasping points for the multi-degree-of-freedom gripper and perform re-grasping in a direction that facilitates barcode scanning. The experiment was conducted with four selected objects, progressing through identification, 6D pose estimation, and grasping, recording the success and failure of barcode recognition to prove the effectiveness of the proposed system.