基于径向基神经网络的Delta机器人位置精度补偿

针对Delta并联机器人末端控制精度问题,提出一种基于RBF的提高Delta并联机构运动学控制精度的方法。首先对Delta并联机器人的运动学逆解进行分析,探讨了影响控制精度的因素和现有提高控制精度方法的局限性。其次,求解Delta并联机器人的工作空间,结合实际工作,通过试验采集训练样本。以末端实际位置为输入样本,末端的期望位置与实际位置之差为输出样本,进行RBF神经网络模型训练,得到末端实际位置与位置偏差之间的非线性映射关系,基于此设计位置补偿策略。最后,在Delta机器人平台上进行实验验证,使用训练好的RBF网络结合运动学逆解,对Delta机器人末端进行轨迹跟踪控制。实验结果表明,末端控制误差由±30mm减小到±5mm,有效的减少了末端位置误差,为Delta机器人精准控制提供了一种简单易行的方法。

Position accuracy compensation of Delta robot based on RBF neural network

Aiming at the end control accuracy of Delta parallel robot, a method based on RBF to improve the kinematics control accuracy of delta parallel mechanism is proposed. Firstly, the inverse kinematics solution of Delta parallel robot is analyzed, and the factors affecting the control accuracy and the limitations of the existing methods to improve the control precision are discussed. Secondly, the working space of Delta parallel robot is solved, and the training samples are collected by experiment. Taking the actual position of the end as the input sample and the difference between the expected position and the actual position of the end as the output sample, the RBF neural network model is trained to obtain the nonlinear mapping relationship between the actual position and the position deviation of the end, based on this, location compensation strategy is designed. Finally, experimental verification was carried out on the Delta robot platform, and trajectory tracking control was carried out on the end of the Delta robot by using the trained RBF network combined with the inverse kinematic solution.The results show that the end control error is reduced from ±30 mm to ±5 mm, which effectively reduces the end position error and provides an simple and easy way for Delta robot precise control.