机器人逆标定方法研究

Research of robot inverse calibration

在分析传统机器人位姿标定方法的基础上,提出了一种新的机器人标定方法:基于神经网络的逆标定方法。这种标定方法把机器人关节角和相应的误差分别作为前馈神经网络的输入和输出来训练网络,从而实时获得机器人任意关节角的误差值,通过修改关节值来提高机器人的位姿精度。仿真和试验结果均证明了这种方法的有效性。。

An innovative robot calibration approach： inverse robot calibration based on neural network, is proposed in this paper, based on the analysis of traditional calibration approach. This method takes the robot joint angles and corresponding joint errors as inputs and outputs of a feed- forward neural network, and achieving the real - time errors in arbitrary angles through the neural network, pose accuracy is improved only through correcting the joints angles. Simulation and experiment results verify, its effect.