Maxime Gautier

Direct calculation of minimum set of inertial parameters of serial robots

The determination of the minimum set of inertial parameters of robots contributes to the reduction of the computational cost of the dynamic models and simplifies the identification of the inertial parameters. These parameters can be obtained from the classical inertial parameters by eliminating those that have no effect on the dynamic model and by regrouping some others. A direct method is presented for determining the minimum set of inertial parameters of serial robots. The method permits determination of most of the regrouped parameters by means of closed-form relations. >

On the identification of the inertial parameters of robots

An algorithm is presented for the identification of the inertial parameters and friction coefficients of robots. The algorithm is not required to measure or calculate the joint accelerations. The identification model is based on the energy theorem, is linear in the robot parameters, and is easy to calculate. An example of a two-degree-of-freedom robot is presented.<<ETX>>

Dynamic identification of robots with power model

This paper presents a new approach to identify the minimum dynamic parameters of robots using least squares techniques (LS) and a power model. Theoretical analysis is carried out from a filtering point of view and clearly shows the superiority of the power model over the energy one and over the dynamic identification model which has been used to carry out a classical ordinary LS estimation and a new weighted LS estimation. These results are checked from comparing experimental identification of the dynamic parameters of a planar SCARA prototype robot.

Extended Kalman filtering and weighted least squares dynamic identification of robot

Abstract This paper presents an experimental comparison between the weighted least squares (WLS) estimation and the extended Kalman filtering (EKF) methods for robot dynamic identification. Comparative results and discussion are presented for a SCARA robot, depending on a priori knowledge and data filtering.

New criteria of exciting trajectories for robot identification

Inertial and friction parameters of robots can be estimated using the minimal linear energy model in relation to the parameters and least squares techniques or a Bayesian approach when a priori statistical information (expected value, covariance matrix) on the solution vector are available. These techniques are sensitive to noise measurements and error modeling, and require the use of exciting trajectories. New criteria are presented taking into account a priori information on the solution to generate exciting trajectories. An application for a two degrees of freedom SCARA robot is simulated to show the efficiency of these methods.<<ETX>>

A direct determination of minimum inertial parameters of robots

A direct method is presented to determine the minimum set of inertial parameters of robots. The set consists of the classical parameters that affect the dynamic model and the regrouped parameters. The method permits to most of the regrouped parameters to be determined by means of a closed-form function of the geometric parameters of the robot. It is proved that the minimum number of inertial parameters is less than 7n-4, where n is the number of joints. The method is general whatever the values of the geometric parameters. The method can be extended to tree structure robots and will be integrated into the software package SYMORO.<<ETX>>

A New Closed-Loop Output Error Method for Parameter Identification of Robot Dynamics

Offline robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is sampled while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. The efficiency of this method has been proved through the experimental identification of many prototypes and industrial robots. However, this method requires the joint force/torque and position measurements and the estimate of the joint velocity and acceleration, through the bandpass filtering of the joint position at high sampling rates. The proposed new method called DIDIM requires only the joint force/torque measurement, which avoids the calculation of the velocity and acceleration by bandpass filtering of the measured position. It is a closed-loop output error method where the usual joint position output is replaced by the joint force/torque. It is based on a closed-loop simulation of the robot using the direct dynamic model, the same structure of the control law, and the same reference trajectory for both the actual and the simulated robot. The optimal parameters minimize the 2-norm of the error between the actual force/torque and the simulated force/torque. This is a nonlinear least-squares problem which is dramatically simplified using the inverse dynamic model to obtain an analytical expression of the simulated force/torque, linear in the parameters. A validation experiment on a two degree-of-freedom direct drive rigid robot shows that the new method is efficient.

Identification of the dynamic parameters of a closed loop robot

This paper presents the experimental results of the identification of the dynamic parameters of the 6 degree of freedom SR400 robot. This industrial robot is characterized by having a parallelogram closed loop and a mechanical coupling between the joints of the hand. The different steps starting from the modelling up to the validation of the results are given. Practical issues are addressed.

Identification of the payload inertial parameters of industrial manipulators

In this paper we present four methods for the identification of the inertial parameters of the load of a manipulator. The knowledge of the values of these parameters can be used to tune the control law parameters in order to improve the dynamic accuracy of the robot. They can also be exploited to verify the load transported by the robot. The methods presented have been validated using Staubli RX 90 robot. The experimentation has been carried out using data collected from the industrial control system (version CS8) of the manufacturer. This version allows to have access to joint positions, velocities and torques. The methods presented are based on solving linear system of equations using weighted least squares solution.

Exciting trajectories for the identification of base inertial parameters of robots

A common way to identify the inertial parameters of robots is to use a linear model in relation to the parameters and standard least squares (LS) techniques. The authors present a method to generate exciting identification trajectories in order to minimize the effect of noise and error modeling on the LS solution. Using nonlinear optimization techniques, the condition number of a matrix W obtained from the energy model is minimized and the scaling of its terms is carried out. An example of a 3 degree of freedom robot is presented.<<ETX>>