Inertial Parameter Identification Including Friction and Motor Dynamics

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).Comment: Pre-print of paper presented at Humanoid Robots, 13th IEEE-RAS International Conference on, Atlanta, Georgia, 201

Statistical Methods for Estimating the Dynamical Parameters of Manipulators

The determination of the dynamical parameters of robot manipulators is crucial in many applications where model based control architectures are needed to match stringent performance requirements. Unfortunately only the kinematic model of the robot is usually available whereas the dynamical parameters are unknown and very difficult to compute by first principle or CAD analysis. In the last decades several algorithms have been proposed to identify these parameters mainly based on the least-square analysis. In this paper we present an identification method based on a statistical algorithm never used so far in robotics, which brings new insight into the understanding of the identified parameters and improves robustness of the computation. We think that this approach represents a significant improvement as compared to using standard statistical tools, as shown by results of the identification of the Puma 200 robot