Multi-Axial Force/Torque Sensor Calibration Method Based on Deep-Learning

Abstract

The multi-axial force/torque sensor for advanced robotic applications utilizes the internal strain of deformable structure for its measurement. Thus, there exist coupling and errors due to nonlinearity caused from the variation of the electric signal with respect to the strain. This paper presents a method of calibrating six-axis force/torque sensors with high accuracy based on deep learning. The method can solve the aforementioned problems easily by using deep-neural network (DNN). After performing the structural analysis of the sensor, generalized equations of the electric signal is derived, which leads it to the basic DNN structure, and optimization is performed. Training and test data are prepared by using a dummy and a reference sensor. Then, the proposed method is validated by comparing the three results obtained from the linear transformation method based on least-square method, two-step neural-network, and the DNN-based method for each untrained test data set.