机器人气动夹持力的CNN-LSTM建模估计方法

CNN - LSTM Modeling and Estimating Method of Pneumatic Gripping Force for Robot

由于气动系统具有迟滞、强非线性特性,难以直接依据气压信号实现气动夹持力的有效控制,因此采用建模估计夹持力是实现无力传感器低成本控制的有效途径。为此,提出一种基于卷积神经网络(CNN)优化的长短期记忆神经网络(LSTM)低成本气动夹持力估计方法。根据工业机器人末端气爪夹持力与气路历史输入/输出有关的特点,采用了具有记忆特性的LSTM网络建立无传感器气压/压力估计模型;针对直接采用LSTM网络进行建模存在误差大的问题,利用CNN提取输入信息中气压和夹持力的非线性关系,进一步对LSTM网络结构进行优化,提高模型描述气压和夹持力之间多值对应特性与非线性迟滞特性的能力,实现气爪的夹持力有效估计。实验结果表明:相比LSTM预测模型,所提模型的建模估计与验证估计均方根误差分别减少77.14%和70.83%,最大误差分别减少79.80%和78.84%,证明了所提建模估计方法的有效性。

Since the hysteresis and strong nonlinearity exist in the pneumatic system,it is difficult to achieve effective control of the pneumatic gripping force based on the air pressure signal directly,and using modeling to estimate the gripping force is an effective way to achieve low-cost control without force sensors.A convolutional neural network(CNN)-based optimized long short-term memory neural network(LSTM)was proposed for the low-cost pneumatic gripping force estimation method.According to the characteristic that the gripping force of the end pneumatic gripper of the industrial robot is related to historical inputs/outputs of the pneumatic circuit,a LSTM network with memory characteristics was used to build a sensorless pneumatic/gripping force estimation model.Aiming at the problem of large modeling error by using LSTM network directly,CNN was used to extract the nonlinear relationship of air pressure and gripping force in the input information to optimize the LSTM network structure,which could improve the ability of the model to describe the multi-valued correspondence/nonlinear hysteresis relationship between air pressure and gripping force,and achieve effective estimation of gripping force of pneumatic gripper.The experimental results show that compared with the LSTM prediction models,the root mean square error of the modeling estimation and validation estimation for the proposed model is reduced by 77.14%and 70.83%,and the maximum error is reduced by 79.80%and 78.84%,respectively,which proves the effectiveness of the proposed estimation method.