基于BP神经网络的机器人力矩补偿研究

Research on Torque Compensation of Robot Based on BP Neural Network

通过对关节驱动助力减小机器人拖动示教的拖拽力,是提高拖动示教灵活性的有效方法。而在拖动示教过程中准确、实时地计算出机器人各关节补偿力矩,是实现拖拽助力的关键问题。针对拖动示教喷涂机器人进行动力学建模,分析关节力矩补偿值与惯性力、重力等因素之间的关系,提出一种基于无监督学习的BP神经网络力矩控制算法对机器人直接示教进行在线力矩补偿。在六自由度喷涂机器人上进行实验验证。结果表明:该力矩补偿算法的计算效率提升70%,平均计算误差为9%,助力效果明显。

By driving the joint to reduce the drag force of the robot,it is an effective way to improve the flexibility of the robot.In the process of dragging teaching,the accurate and real-time calculation of the torque compensation of each joint of the robot is the key problem to realize the dragging assistance.The dynamic modeling of the dragging teaching spraying robot was carried out,and the relationship between joint torque compensation and inertia force,gravity and other factors was analyzed,and a BP neural network torque control algorithm based on unsupervised learning was proposed to compensate the direct teaching of the robot.Finally,experimental verification was carried out on a 6DOF spraying robot.The experimental results show that the calculation efficiency of the torque compensation algorithm is increased by 70%,the average calculation error is 9%,and the assistance effect is obvious.