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

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.