基于深度神经网络的机器人定位误差补偿方法

Robot Positioning Error Compensation Method Based on Deep Neural Network

工业机器人由于高效率、低成本被广泛应用于智能制造业,但较低的绝对定位精度限制了其在高精度制造领域的推广应用。为提升机器人绝对定位精度并解决传统复杂的误差建模问题,提出了一种基于深度神经网络的机器人定位误差补偿方法。首先在笛卡尔空间进行拉丁超立方采样规划,获得目标点姿态对误差的影响规律;然后建立基于遗传粒子群算法优化深度神经网络(GPSO–DNN)的定位误差预测模型,实现对误差的预测和补偿;最后为验证该方法的准确性和优越性,与其他误差补偿模型进行对比。试验结果表明,基于GPSO–DNN的定位误差补偿方法的补偿精度最高,定位误差由补偿前的1.529mm减小为0.343mm,精度提高了77.57%。该方法能有效补偿机器人定位误差,大幅提高机器人的定位精度。

Industrial robots are widely used in intelligent manufacturing industry because of their high efficiency and low cost,but their low absolute positioning accuracy limits their application in the field of high-precision manufacturing.To improve the absolute positioning accuracy of robot and solve the traditional complex error modeling problems,a robot positioning error compensation method based on deep neural network is proposed.Firstly,the Latin hypercube sampling planning is carried out in Cartesian space,and the influence rule of target attitude on error is obtained.Then,positioning error prediction model based on GPSO–DNN is established to realize the prediction and compensation of the error.Finally,to verify the correctness and superiority of the method,other error compensation models are used to compare with it.The experimental results show that the positioning error compensation method based on GPSO–DNN has the highest compensation accuracy.The positioning error is reduced from 1.529mm before compensation to 0.343mm,and the accuracy is increased by 77.57%.This method can effectively compensate the positioning error of the robot and greatly improve the positioning accuracy of the robot.