Identification of a UR5 collaborative robot dynamic parameters

The present paper describes an algorithm for the identification of the dynamic parameters of an industrial robot. This approach is based on the possibility to write robot dynamics in a linear form with respect to a specific set of dynamic parameters. To properly detect them, the coefficients of a 5th order Fast Fourier Series (FFS) trajectory have been optimized using a genetic algorithm. Such identification trajectory has been then commanded to a UR5 collaborative robot from Universal Robots and experimental joints torques have been recorded at a frequency of 125 Hz. Base dynamic parameters were identified using least square errors optimization reaching low standard deviations. The algorithm has been validated with a second persistent trajectory with good results. Temperature effects on friction coefficients have been analyzed by running two identification processes: one just after the first power-up of the robot and the other one after a half an hour warm-up

Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design

As the foundation of model control, robot dynamics is crucial. However, a robot is a complex multi-input–multi-output system. System noise seriously affects parameter identification results, thereby inevitably requiring us to conduct signal processing to extract useful signals from chaotic noise. In this research, the dynamic parameters were identified on the basis of the proposed multi-criteria embedded optimization design method, to obtain the optimal excitation signal and then use maximum likelihood estimation for parameter identification. Considering the movement coupling characteristics of the multi-axis, experiments were based on a two degrees-of-freedom manipulator with joint torque sensors. Simulation and experimental results showed that the proposed method can reasonably resolve the problem of mutual opposition within a single criterion and improve the identification robustness in comparison with other optimization criteria. The mean relative standard deviation was 0.04 and 0.3 lower in the identified parameters than in F1 and F3, respectively, thus signifying that noise is effectively alleviated. In addition, validation experimental curves were close to the estimation model, and the average of root mean square (RMS) is 0.038, thereby confirming the accuracy of the proposed method