【摘要】 基于RBF神经网络算法,在不改变机器人控制器参数的情况下,提出了一种通过减小单关节转角偏差,提高机器人绝对定位精度的方法。首先,建立机器人D-H运动学模型,并分析机器人末端位置误差和关节转角偏差的计算方法。其次,采用基于RBF神经网络算法对机器人关节转角偏差进行训练,得到关节转角偏差的预测值接近于实测值,最后将预测转角偏差补偿到理论关节转角,机器人末端的位置误差有一定的改善,证明文中的方法可以有效改善机器人的绝对定位精度。更多还原

【Abstract】 Based on the RBF neural network algorithm, without changing the parameters of the robot controller, a method is proposed to improve the absolute positioning accuracy of the robot by reducing the deviation of the single joint angle. First, the robot D-H kinematics model is established, and the calculation method of the robot end position error and joint rotation angle deviation is analyzed. Secondly, the principle of compensation based on the RBF neural network algorithm for robot joint rotation angle deviation is adopted. Finally, by training the neural network, the predicted value of the joint rotation angle deviation is closer to the measured value, and after the predicted rotation angle deviation is compensated, the position error of the robot end is also improved to a certain extent, which proves that the method in the article can effectively improve the absolute positioning accuracy of the robot.更多还原