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로봇 기구학적 오차 및 관절 강성 오차의 동시 추정과 최적 측정자세 선정을 통한 향상된 로봇 캘리브레리션 기술 개발 : Development of Enhanced Robot Calibration Techniques Through Simultaneous Identification of Kinematic and Joint Compliance Errors and Selection of Optimal Measurement Poses 원문보기

  • 저자

    주지안

  • 학위수여기관

    울산대학교

  • 학위구분

    국내박사

  • 학과

    전기전자정보시스템공학전공

  • 지도교수

    강희준

  • 발행년도

    2014

  • 총페이지

    105 p.

  • 키워드

    Industrial robot manipulator Constant joint stiffness Non-linear joint stiffness Optimal measurement poses;

  • 언어

    eng

  • 원문 URL

    http://www.riss.kr/link?id=T13540334&outLink=K  

  • 초록

    Robotic manipulators have been used widely in industrial applications such as assembly, painting and welding operations, and machining operations. To perform these operations precisely, the robots must possess good kinematic and elastostatic performance. However, present robot manipulators have high repeatability but low accuracy (kinematic performance). In addition, robot stiffness usually is not known parameter (related to elastostatic performance). Therefore, robot manipulators need to be calibrated to meet the accuracy demands of industrial applications. The conventional kinematic calibration has been well developed to identify the kinematic parameters of a robot manipulator which describe the most correctly its kinematic model. Researchers have paid attention to identify robot stiffness (link and joint stiffness) that is a relevant performance index for robot machining and can be used for compensating compliance errors due to the flexibility of robot joints and link deflection under self-gravity and external load. In this study, a new robot calibration algorithm is presented, which aims to simultaneously identify joint compliance and kinematic parameters, thus the relative kinematic and compliance errors can be compensated for improving position accuracy of industrial robots. The compliance error model is established by modeling the robot joint as a linear torsional spring. A comprehensive error model is then derived by combing kinematic and compliance errors. An iterative linear least squares algorithm is then used to identify the desired parameters. The proposed calibration algorithm is implemented on two kinds of industrial robot manipulators (serial robot manipulator and that having a closed chain mechanism) to show its effectiveness in improving robot position accuracy. In addition, another new technique for identifying and compensating joint compliance errors is presented. Within this technique, a comprehensive error model consisting of both kinematic and joint compliance errors is established. In contrast previous work, joint compliance is modeled as a piecewise linear function of joint torque to approximate the nonlinear relation between joint torque and torsional angle. A hybrid least squares-GA-based algorithm is then developed to simultaneously identify the kinematic and non-kinematic parameters defined in the comprehensive error model. The identified parameters can be used to compensate kinematic and compliance errors. The developed technique is applied to a 6 dof serial industrial robot to demonstrate the effectiveness of the identification and compensation techniques. Moreover, to improve the robustness of robot calibration with respect to sensor noise and unmodeled errors, a new pose selection algorithm is presented. In contrast the related works in the literature, the proposed pose selection algorithm allows us to select a given number of optimal poses out of a large set of previously measured poses, which is derived from the DETMAX algorithm with the improvements benefiting from the randomized search technique to avoid local convergence. The benefit of selecting optimal measurement poses is validated through both simulation and experimentation of calibration of a 6 dof serial robot manipulator.


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