This study addresses the problem of nonlinear error predictive compensation to achieve high positioning accuracy for advanced industrial applications. An improved calibration method based on the generalisation performance evaluation is proposed to enhance the stability and accuracy of robot calibration. With the development of technology, a deep neural network (DNN) optimised by a genetic algorithm (GA) is applied to predict the nonlinear error of the calibrated robot. To address the change of external payload, an extra compliance error model is established with a linear piecewise method. A global compensation method combining the GA-DNN nonlinear regression prediction model and the compliance error model is then proposed to achieve the robot’s high-precision positioning performance under any external payload. Experimental results obtained on a Staubli RX160L robot with a FARO laser tracker are introduced to demonstrate the effectiveness and benefits of our proposed methodology. The enhanced positioning accuracy can reach 0.22 mm with 98% probability (i.e., the maximum positioning error in all test data).

中文翻译：

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基于GA-DNN和额外柔度误差模型的进化机器人标定和非线性补偿方法

这项研究解决了非线性误差预测补偿的问题，以实现高级工业应用中的高定位精度。提出了一种基于泛化性能评估的改进标定方法，以提高机器人标定的稳定性和准确性。随着技术的发展，应用遗传算法（GA）优化的深度神经网络（DNN）来预测校准机器人的非线性误差。为了解决外部有效负载的变化，使用线性分段方法建立了额外的一致性误差模型。然后提出一种结合GA-DNN非线性回归预测模型和柔度误差模型的全局补偿方法，以在任何外部有效载荷下实现机器人的高精度定位性能。介绍了在带有FARO激光跟踪仪的Staubli RX160L机器人上获得的实验结果，以证明我们提出的方法的有效性和益处。定位精度提高到0.22 mm，概率为98％（即所有测试数据中的最大定位误差）。