论文部分内容阅读
平行双关节坐标测量机(PDCMM)是一种3自由度、高精密、可现场测量的测量仪器.为了提高平行双关节坐标测量机的测量精度,采用粒子群优化(PSO)算法对其结构参数进行标定,避免了最小二乘法(LSM)在求逆和求偏导数时产生的计算误差.基于Denavit-Hartenberg(D-H)模型对平行双关节坐标测量机建立运动学模型和误差模型,以不同位置实际值和测量值之间的残余误差平方和作为PSO算法的目标函数,优化结构参数.实验结果表明,PSO算法具有标定精度高和收敛速度快的优点,可以有效提高平行双关节测量机的测量精度.
The parallel double-joint coordinate measuring machine (PDCMM) is a 3-DOF, high-precision, field-measuring instrument.In order to improve the measurement accuracy of parallel double-joint CMM, the particle swarm optimization (PSO) And the calculation error of least square method (LSM) in inversion and partial derivative is avoided.Based on the Denavit-Hartenberg (DH) model, kinematics model and error model of parallel double-joint coordinate measuring machine are established, The residual sum of squares between the actual value and the measured value is used as the objective function of the PSO algorithm to optimize the structural parameters.The experimental results show that the PSO algorithm has the advantages of high calibration accuracy and fast convergence speed and can effectively improve the measurement of parallel dual joint measuring machine Accuracy.