多关节测量臂的小生境混沌优化校准

多关节测量臂是一种便携式的坐标测量设备,它由一系列的旋转关节组成。为了提高多关节测量臂的测量精度和可重复性水平,必须对其运动学参数进行校准。首先,利用小生境的混沌优化算法提出了一种新的运动学校准方法以及一种混合目标的运动学校准函数,它考虑了诸如单点测量实验、容积性测量实验等多种性能测量实验的实验结果,然后,采用Levenberg Marquardt(L-M)算法和小生境混沌优化算法应用于运动学参数校准。小生境混沌优化算法显示出了优于L-M算法的性能。实验结果表明:使用NCOA算法校准后,测量误差的标准差始终优于LMA算法,并且校准前后多关节测量臂的测量精度提高了40倍。采用L-M算法和小生境混沌优化算法应用于运动学参数校准。小生境混沌优化算法显示出了优于L-M算法的性能。

Calibration method of PCMA by using niching chaos optimization algorithm

A portable coordinate measuring arm (PCMA) was a piece of portable coordinate measuring equipment that employs a series of rotating joints. In order to improve the measuring accuracy and repeatability of a PCMA, it was essential to calibrate its kinematic parameters. First, a new kinematic calibration approach for PCMAs by using a niching chaos optimization algorithm (NCOA) was proposed. A hybrid objective function for kinematic calibration was proposed that reflects the various performance tests, including the single-point articulation performance test and volumetric performance test. Then a Levenberg-Marquardt (L-M) algorithm and an NCOA are employed for calibrating the kinematic parameters. The NCOA exhibits a competitive calibration performance compared to the L-M algorithm. Experimental results show that the standard deviation of the measurement after NCOA calibration is always better than that of the L-M algorithm, and the measurement precision after calibration is improved by 40 times. An L-M algorithm and a NCOA are employed for calibrating the kinematic parameters of a PCMA. The NCOA shows better performance than the L-M algorithm.