文章基本信息

标题：A hybrid least-squares genetic algorithm–based algorithm for simultaneous identification of geometric and compliance errors in industrial robots
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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