Shape from Motion Approach to Rapid and Precise Force/Torque Sensor Calibration

Abstract

We present a new technique for multi-axis force/torque sensor calibration called shape from motion. This technique retains the noise rejection of a highly redundant data set but eliminates the need for explicit knowledge of the redundant applied load vectors, yielding faster, more accurate calibration results. A constant-magnitude force (a mass in a gravity field) is randomly moved through the sensing space while raw data is continuously gathered. Using only the raw sensor signals, the motion of the force vector (the "motion") and the calibration matrix (the "shape") are simultaneously extracted by singular value decomposition. We have applied this technique to several types of force/torque sensors and present experimental results for a 2-DOF fingertip and a 6-DOF wrist sensor with comparisons to the standard least squares approach.