Inertial Parameter Identification Including Friction and Motor Dynamics

TL;DR

This paper introduces a comprehensive and simple method for identifying inertial, friction, and motor parameters of humanoid robot joints using a single data collection process, enhancing control accuracy and contact detection.

Contribution

A novel unified identification procedure for inertial, friction, and motor parameters using PWM and force/torque measurements with PLS regression, validated on the iCub robot.

Findings

Accurately predicts force/torque sensor measurements

Effectively detects external contacts

Outperforms other methods in completeness and simplicity

Abstract

Identification of inertial parameters is fundamental for the implementation of torque-based control in humanoids. At the same time, good models of friction and actuator dynamics are critical for the low-level control of joint torques. We propose a novel method to identify inertial, friction and motor parameters in a single procedure. The identification exploits the measurements of the PWM of the DC motors and a 6-axis force/torque sensor mounted inside the kinematic chain. The partial least-square (PLS) method is used to perform the regression. We identified the inertial, friction and motor parameters of the right arm of the iCub humanoid robot. We verified that the identified model can accurately predict the force/torque sensor measurements and the motor voltages. Moreover, we compared the identified parameters against the CAD parameters, in the prediction of the force/torque sensor measurements. Finally, we showed that the estimated model can effectively detect external contacts, comparing it against a tactile-based contact detection. The presented approach offers some advantages with respect to other state-of-the-art methods, because of its completeness (i.e. it identifies inertial, friction and motor parameters) and simplicity (only one data collection, with no particular requirements).