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  • 标题:A hybrid least-squares genetic algorithm–based algorithm for simultaneous identification of geometric and compliance errors in industrial robots
  • 本地全文:下载
  • 作者:Jian Zhou ; Hee-Jun Kang
  • 期刊名称:Advances in Mechanical Engineering
  • 印刷版ISSN:1687-8140
  • 电子版ISSN:1687-8140
  • 出版年度:2015
  • 卷号:7
  • 期号:6
  • DOI:10.1177/1687814015590289
  • 语种:English
  • 出版社:Sage Publications Ltd.
  • 摘要:Due to the flexibility of robot joints and links, industrial robots can hardly achieve the accuracy required to perform tasks when a payload is attached at their end-effectors. This article presents a new technique for identifying and compensating compliance errors in industrial robots. Within this technique, a comprehensive error model consisting of both geometric and compliance errors is established, where 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 genetic algorithm–based algorithm is then developed to simultaneously identify the geometric parameters, joint compliance values, and the transition joint torques. These identified geometric and non-geometric parameters are then used to compensate geometric and joint compliance errors. Finally, the developed technique is applied to a 6 degree-of-freedom industrial serial robot (Hyundai HA006). Experimental results are presented that demonstrate the effectiveness of the identification and compensation techniques.
  • 关键词:Nonlinear joint stiffness modeling; joint stiffness identification; compliance error compensation; genetic algorithm; hybrid algorithm
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