为了提高6R焊接机器人的位姿精度和焊接轨迹的准确度,提出了一种基于RBF神经网络的6R焊接机器人逆运动学求解方法.针对6R焊接机器人逆运动学方程组具有高维、非线性、求解复杂的特点,基于RBF神经网络建立运动学逆解预测模型,采用尺度空间理论对焊接机器人的位姿参数样本所在的工作空间进行分区,采用均匀设计法和模糊聚类理论对分区后的训练样本进行优选,并根据Z-Y-Z坐标转换原理进行转换和归一化处理,将逆运动学求解问题转换为基于RBF的6输入6输出预测系统.运用该系统对6R焊接机器人进行了复杂焊接轨迹仿真和点焊实验,并与基于组合优化迭代法和BP神经网络的逆运动学求解效果与焊接精度进行了比较,结果表明,基于RBF的6R焊接机器人运动学逆解预测模型具有求解简单、精度高、便于轨迹规划的特点,证明了该方法的可行性和有效性.%A new method of solving inverse kinematics of 6R welding robot based on radial basis function (RBF) neural networks was presented to improve the precision of the position and orientation and the accuracy of welding trajectory for the 6R welding robot.The inverse kinematics solution prediction model of the 6R welding robot was established based on RBF neural networks because the inverse kinematics equations were high-dimensionally nonlinear and solving these equations was complex.The work space in which 6R welding robot position and orientation sample parameters were situated was divided based on scale-space theory.After that the training sample set was selected optimally based on uniform design and the cluster theory.The parameters were transformed and normalized according to the Z-Y-Z coordinate conversion principle.The problem of solving the inverse kinematics equations was transformed into six inputs and six outputs prediction system based on RBF neural network.Complex movement trajectory of 6R robot was simulated and the spot welding experiments were done by means of this prediction system.The results of the prediction and welding track accuracy were compared with the inverse kinematics solution based on combinatorial optimization iteration algorithm and back propagation (BP) neural networks.The results showed that the RBF prediction model of solving 6R welding robot inverse kinematics equations was simpler,more accurate and easier to do trajectory planning,and it was proved to be feasible and effective.
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