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1.南京林业大学 机械电子工程学院,江苏 南京 210037
2.南京航空航天大学 江苏省精密与微细制造技术重点实验室,江苏 南京 210016
[ "叶鹏达(1994-),男,江苏溧阳人,博士研究生,2017年于南京林业大学获得学士学位,主要从事并联机器人运动学与动力学的研究。E-mail: yepengda@njfu.edu.cn" ]
收稿日期:2021-01-11,
修回日期:2021-03-16,
纸质出版日期:2021-08-15
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叶鹏达,尤晶晶,仇鑫等.并联机器人工作空间的区间离散法[J].光学精密工程,2021,29(08):1847-1856.
YE Peng-da,YOU Jing-jing,QIU Xin,et al.Interval discretization method for workspace of parallel robot[J].Optics and Precision Engineering,2021,29(08):1847-1856.
叶鹏达,尤晶晶,仇鑫等.并联机器人工作空间的区间离散法[J].光学精密工程,2021,29(08):1847-1856. DOI: 10.37188/OPE.20212908.1847.
YE Peng-da,YOU Jing-jing,QIU Xin,et al.Interval discretization method for workspace of parallel robot[J].Optics and Precision Engineering,2021,29(08):1847-1856. DOI: 10.37188/OPE.20212908.1847.
鉴于运用传统的点离散法计算并联机器人的工作空间时存在计算精度低、有漏判点等不足,以3-RPR平面并联机器人为研究对象,基于区间数学理论提出一种高精度、无漏判点的区间离散法。推导了机器人运动学反解的解析表达式,运用区间离散法计算其位置/姿态工作空间,从效率、精度和误差灵敏度这三个方面对比了区间离散法和点离散法。结果显示:随着计算精度的提高,区间离散法的效率优势越来越明显;区间离散法和点离散法的计算误差分别为0.002%和0.272%;区间离散法和点离散法对机器人结构尺寸误差的敏感度分别为0.394和0.396。这表明区间离散法的数值性态明显优于点离散法。该研究方法及结论为并联机器人的拓扑优化、轨迹规划等工作提供了理论指导。
The traditional point discretization method (PDM) for the workspace determination of parallel robots has some drawbacks such as low accuracy and missing points. In view of these, considering a 3-RPR planar parallel robot as the research object, an interval discretization method (IDM) with high accuracy and no point omission is proposed based on the interval mathematics theory. Firstly, an expression for the analytic inverse solution of the robot is derived. Then, the position/orientation workspace is obtained using the IDM. Subsequently, the efficiency, accuracy, and error sensitivity of the IDM and PDM are compared. The results indicate the following: (i) with the improvement of calculation accuracy, the efficiency advantage of the IDM becomes increasingly evident; (ii) the calculation error of the IDM and PDM are 0.002% and 0.272%, respectively; and (iii) the sensitivities of the IDM and PDM to the errors of structural size of the robot are 0.394 and 0.396, respectively. The research methods and conclusions provide theoretical guidance for topology optimization and trajectory planning of parallel robots.
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