摘要
利用局部指数积模型,建立了6自由度参数的运动学模型,克服了传统D-H模型只有4自由度参数的缺点,考虑了沿Y轴平移和绕Y轴旋转2个自由度运动学参数的误差对测量精度的影响.以单点测量值的标准差为目标函数,采用基于模拟退火的粒子群优化算法,通过全局寻优,避免了最小二乘法在求导和求逆时产生的较大计算误差,从而优化出运动学参数的标定值.实验结果表明,所提出的新模型及新运动学参数标定方法可以将柔性测量臂X、Y、Z轴的重复性精度分别提高95.91%、96.42%和96.10%.
A kinematic model involving 6 degrees of freedom（DOF）parameters is established by the local product of exponentials formula,where the impact of the error of Yaxis translation and rotation on measurement accuracy are considered make up incompleteness in D-H model.The standard deviation of the single-point repeatability is regarded as an objective function.To optimize the objective function and calibrate actual kinematic parameters,an improved particle swarm optimization（PSO）algorithm is proposed based on simulated annealing,where calculation errors are avoided from the derivation and inversion procedures in the traditional calibration method based on the least square method by global optimization.The experimental results show that the present kinematic model and calibration method enable to significantly improve the repeatability precision of X,Y,Zaxis up to 95.91%,96.42%and 96.10%,respectively.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2010年第8期122-126,共5页
Journal of Xi'an Jiaotong University
基金
国家自然科学基金资助项目(50875093)
关键词
柔性测量臂
参数标定
局部指数积模型
粒子群优化算法
flexible articulated coordinate measuring arm
parameter calibration
local product-of-exponential
particle swarm optimization
