Using maximum likelihood to calibrate a six-DOF force/torque sensor

Abstract

This study presents a new six-DOF force/torque sensor and its calibration method. This calibration method apply the method of so-called maximum likelihood estimation (MLE). MLE is utilized to determine and identify the parameters related to applied torques/forces towards resulted deformations at varied locations of the sensor structure. Formulating such relations in a vector-matrix form, those parameters are captured as coefficients in a matrix related to torques/forces towards angular/linear deformations in different directions. In addition to applying MLE, finite element modeling and analysis are conducted to generate realistic-like empirical data for the afore-mentioned calibration computation. The matrix formed by these coefficients can predict exactly forces and torques by this sensor based on deformations detected by strain gauges. In simulated results the worst sum of error for three forces is less than 0.04% while the worst sum of error for three torques is less than 0.005%. Experiments are also conducted, and the results show that the designed sensor and maximum-likelihood parameter estimation approach achieve favorable performance of predicting forces and torques with error less than 1%. The developed sensor is suitable for real-time sensing of multi-dimensional interactive forces/torques in a robot arm.