The 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. The novel aspect of this technique is that it does not require explicit knowledge of the redundant applied load vectors, yet it retains the noise rejection of a highly redundant data set and the rigor of least squares. The result is a much faster, slightly more accurate calibration procedure. A constant-magnitude force (produced by 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. Eliminating the need to collect all the applied loads makes collecting large amounts of calibration data nearly effortless. 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.