工业机器人标定技术研究
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摘要
机器人标定是离线编程技术实用化的关键技术之一，所谓标定就是应用先进的测量手段和基于模型的参数识别方法辨识出机器人模型的准确参数，从而提高机器人绝对精度的过程。
本文首先结合某打磨机器人的实际几何结构，建立了该机器人的误差模型，利用单点法来辨识几何参数误差值；提出了误差分类补偿的方法，建立了机器人的理想和实际运动学模型，获得了运动学正解和逆解。
针对机器人关节角误差补偿，本文利用遗传神经网络来获得机器人的关节误差，为克服遗传算法易陷入局部最优的缺点，引入具有动态参数编码特性的Solis&Wets算法对遗传算法进行有益的补充，通过对机器人的关节误差进行补偿可以部分提高机器人的位姿精度。
进一步地，研究了机器人的几何参数标定法，利用位姿匹配原理，采用非线性优化算法和线性方程迭代法分别标定了打磨机器人的几何参数误差并把二者的标定结果进行了比较。在此基础上，考虑到机器人的部分几何参数在工作空间中产生的位姿误差几乎相同而无法区分，推导了非冗余几何参数集，并与完整集的标定结果进行了比较分析。
考虑到几何参数标定法的一些缺陷，提出了基于神经网络的机器人位姿误差标定算法，利用前馈神经网络分别与打磨机器人的理想运动学模型及实际运动学模型相结合，并分别采用四种方案对机器人的位姿进行了精确的标定，并对标定结果进行了比较分析。考虑到机器人的使用由逆运动学控制，利用前馈神经网络对机器人进行了逆运动学标定。仿真结果表明该方法的标定效果优于单纯的几何参数法。
为验证误差模型和单点测量的误差参数辩识方法的实际效果，在打磨机器人工作空间内选取多个位姿进行了实际标定，用六维激光跟踪仪为测量工具，利用单点法来辩识几何误差参数，经补偿后的单轴位置误差不超过5μm，部分指标达到了用户的要求。
推导了机器人的动力学模型，并对计算力矩加PD反馈控制方法进行了Lyapunov稳定性分析；针对计算力矩的不确定性，利用线形神经网络法进行
Robot calibration is one of the key techniques of robot off-line programming. Calibration is defined as using advanced measurement equipments and model -based parameter identification approaches to identify the accurate robot parameters and increase the absolute accuracy.
Combining its real geometrical structure, a polishing robot's error model is established and a single point method is used to identify the geometrical parameter errors. Introducing a new error compensation thought in which different kinds errors can be compensated with different ways. Establishing the ideal and actual kinematics model, and kinematics equation and inverse kinematics one are obtained.
In order to compensate the joint errors, this paper uses genetic neural network to get the joint errors. To overcome the premature of the genetic algorithm, the paper introduces Solis&Wets algorithm that has dynamic parameter encoding character to complement the genetic algorithm. The robot joint errors are achieved through genetic neural network, and robot pose accuracy is increased through compensating the joints.
Further, this paper studies the geometry parameters calibration method. Based on pose matching principle, the paper uses nonlinear optimization procedure and iterative linear equation to calibrate the geometrical parameters and compare their calibration results. Considering that in some case some of the robot parameters that theoretically are necessary could possibly be indistinguishable to others, because they produce almost the same manipulator pose error in the considered working subspace. A new non-redundant error parameter set is obtained and its calibration results are compared and analysised with the complete set.
Considering the shortcomings of the geometrical parameter calibration method this paper introduces a new calibration method based on neural network.
本文首先结合某打磨机器人的实际几何结构，建立了该机器人的误差模型，利用单点法来辨识几何参数误差值；提出了误差分类补偿的方法，建立了机器人的理想和实际运动学模型，获得了运动学正解和逆解。
针对机器人关节角误差补偿，本文利用遗传神经网络来获得机器人的关节误差，为克服遗传算法易陷入局部最优的缺点，引入具有动态参数编码特性的Solis&Wets算法对遗传算法进行有益的补充，通过对机器人的关节误差进行补偿可以部分提高机器人的位姿精度。
进一步地，研究了机器人的几何参数标定法，利用位姿匹配原理，采用非线性优化算法和线性方程迭代法分别标定了打磨机器人的几何参数误差并把二者的标定结果进行了比较。在此基础上，考虑到机器人的部分几何参数在工作空间中产生的位姿误差几乎相同而无法区分，推导了非冗余几何参数集，并与完整集的标定结果进行了比较分析。
考虑到几何参数标定法的一些缺陷，提出了基于神经网络的机器人位姿误差标定算法，利用前馈神经网络分别与打磨机器人的理想运动学模型及实际运动学模型相结合，并分别采用四种方案对机器人的位姿进行了精确的标定，并对标定结果进行了比较分析。考虑到机器人的使用由逆运动学控制，利用前馈神经网络对机器人进行了逆运动学标定。仿真结果表明该方法的标定效果优于单纯的几何参数法。
为验证误差模型和单点测量的误差参数辩识方法的实际效果，在打磨机器人工作空间内选取多个位姿进行了实际标定，用六维激光跟踪仪为测量工具，利用单点法来辩识几何误差参数，经补偿后的单轴位置误差不超过5μm，部分指标达到了用户的要求。
推导了机器人的动力学模型，并对计算力矩加PD反馈控制方法进行了Lyapunov稳定性分析；针对计算力矩的不确定性，利用线形神经网络法进行
Robot calibration is one of the key techniques of robot off-line programming. Calibration is defined as using advanced measurement equipments and model -based parameter identification approaches to identify the accurate robot parameters and increase the absolute accuracy.
Combining its real geometrical structure, a polishing robot's error model is established and a single point method is used to identify the geometrical parameter errors. Introducing a new error compensation thought in which different kinds errors can be compensated with different ways. Establishing the ideal and actual kinematics model, and kinematics equation and inverse kinematics one are obtained.
In order to compensate the joint errors, this paper uses genetic neural network to get the joint errors. To overcome the premature of the genetic algorithm, the paper introduces Solis&Wets algorithm that has dynamic parameter encoding character to complement the genetic algorithm. The robot joint errors are achieved through genetic neural network, and robot pose accuracy is increased through compensating the joints.
Further, this paper studies the geometry parameters calibration method. Based on pose matching principle, the paper uses nonlinear optimization procedure and iterative linear equation to calibrate the geometrical parameters and compare their calibration results. Considering that in some case some of the robot parameters that theoretically are necessary could possibly be indistinguishable to others, because they produce almost the same manipulator pose error in the considered working subspace. A new non-redundant error parameter set is obtained and its calibration results are compared and analysised with the complete set.
Considering the shortcomings of the geometrical parameter calibration method this paper introduces a new calibration method based on neural network.
引文
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