• Title/Summary/Keyword: SCARA Robot

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Real-Time Motion Tracking Detection System for a Spherical Pendulum Using a USB Camera (USB 카메라를 이용한 실시간 구면진자 운동추적 감지시스템)

  • Moon, Byung-Yoon;Hong, Sung-Rak;Ha, Manh-Tuan;Kang, Chul-Goo
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.40 no.9
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    • pp.807-813
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    • 2016
  • Recently, a spherical pendulum attached to an end-effector of a robot manipulator has been frequently used for a test bed of residual vibration suppression control in a multi-dimensional motion. However, there was no automatic tracking system to detect the current bob position on-line, and there was inconvenience to not be able to store the bob position in real time and plot the trajectory. In this study, we developed a two-dimensional, real-time bob-detecting system using a digital USB camera, of which the key is hardware component design and software C programming for fast image processing and interfacing. The developed system was applied to residual vibration suppression control of a two-dimensional spherical pendulum that is attached at the end-effector of a two degree-of-freedom SCARA robot, and the effectiveness of the developed system has been demonstrated.

Minimum-Time Trajectory Planning Ensuring Collision-Free Motion for Two Robots : Neural Optimization Network Approach (신경 최적화 회로망을 이용한 두 대의 로보트를 위한 최소시간 충돌회피 경로 계획)

  • Lee, Ji-Hong;Bien, Zeung-Nam
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.27 no.10
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    • pp.44-52
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    • 1990
  • A collision-free trajectory planning for two robots with designated paths is considered. The proposed method is based on the concept of decomposing the planning problem into two steps: one is determining coordination of two robots, and the other is velocity planning with determined coordination. Dynamics and maximum allowable joint velocities are also taken into consideration in the whole planning process. The proposed algorithm is converted into numerical calculation version based on neural optimization network. To show the usefulness of proposed method, an example of trajectory planning for 2 SCARA type robot in common workspace is illustrated.

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Direct Learning Control for a Class of Multi-Input Multi-Output Nonlinear Systems (다입력 다출력 비선형시스템에 대한 직접학습제어)

  • 안현식
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.40 no.2
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    • pp.19-25
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    • 2003
  • For a class of multi-input multi-output nonlinear systems which perform a given task repetitively, an extended type of a direct leaning control (DLC) is proposed using the information on the (vector) relative degree of a multi-input multi-output system. Existing DLC methods are observed to be applied to a limited class of systems with the relative degree one and a new DLC law is suggested which can be applied to systems having higher relative degree. Using the proposed control law, the control input corresponding to the new desired output trajectory is synthesized directly based on the control inputs obtained from the learning process for other output trajectories. To show the validity and the performance of the proposed DLC, simulations are performed for trajectory tracking control of a two-axis SCARA robot.

A Study on Mating Chamferless Parts by Integrating Fuzzy Set Tyeory and Neural Network (퍼지 및 신경회로망을 이용한 면취가 없는 부품의 자동결합작업에 관한 연구)

  • 박용길;조형석
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.18 no.1
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    • pp.1-11
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    • 1994
  • This paper presents an intelligent robotic control method for chamferless parts mating by integrating fuzzy control and neural network. The successful assembly task requires an extremely high position accuracy and a good knowledge of mating parts. However, conventional assembly method alone makes it difficult to achieve satisfactory assembly performance because of the complexity and the uncertainties of the process and its environments such as not only the limitation of the devices performing the assembly but also imperfect knowledge of the parts being assembled. To cope with these problems, an intelligent robotic assembly method is proposed, which is composed of fuzzy controller and learning mechanism based upon neural net. In this method, fuzzy controller copes with the complexity and the uncertainties of the assembly process, while neural network enhances the assembly scheme so as to learn fuzzy rules from experience and adapt to changes in environment of uncertainty and imprecision. The performance of the proposed assembly scheme is evaluted through a series of experiments using SCARA robot. The results show that the proposed control method can be effectively applied to chamferless precision parts mating.