The Dynamic Model of the UR10 Robot and its ROS2 Integration

TL;DR

This paper develops a comprehensive dynamic model of the UR10 robot, incorporating a three-stage identification process, and provides ROS2 software for control and planning with improved accuracy and adaptability.

Contribution

The paper introduces a novel three-stage identification method for the UR10's dynamics and integrates it into ROS2 software for enhanced control and planning.

Findings

Achieved up to 4.43 times higher current prediction accuracy.

Validated the model with experimental trajectories.

Provided adaptable ROS2 software for various payloads.

Abstract

This paper presents the full dynamic model of the UR10 industrial robot. A triple-stage identification approach is adopted to estimate the manipulator's dynamic coefficients. First, linear parameters are computed using a standard linear regression algorithm. Subsequently, nonlinear friction parameters are estimated according to a sigmoidal model. Lastly, motor drive gains are devised to map estimated joint currents to torques. The overall identified model can be used for both control and planning purposes, as the accompanied ROS2 software can be easily reconfigured to account for a generic payload. The estimated robot model is experimentally validated against a set of exciting trajectories and compared to the state-of-the-art model for the same manipulator, achieving higher current prediction accuracy (up to a factor of 4.43) and more precise motor gains. The related software is available at https://codeocean.com/capsule/8515919/tree/v2.