The Dynamic Model of the UR10 Robot and Its ROS2 Integration

2025-02-17

IEEE Transactions on Industrial Informatics

AI Summary

This work addresses the lack of high-precision, configurable full-order dynamic models for the UR10 robot. We propose a three-stage collaborative identification framework: (1) linear parameter regression to estimate inertial and Coriolis terms; (2) Sigmoid-based modeling of joint nonlinear friction; and (3) data-driven construction of a current–torque–drive-gain mapping model using empirical measurements. The method is implemented in ROS2 to enable real-time, load-adaptive configuration and deployment. Experiments demonstrate that the proposed model reduces current prediction error to 22.6% of that achieved by conventional approaches, significantly improving motor gain estimation accuracy and overcoming limitations of single-model paradigms. An open-source ROS2 module supports dynamic reconfiguration and reuse under arbitrary end-effector loads. This framework establishes a reliable, high-fidelity dynamic foundation for precision motion control and trajectory planning.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Dynamic model of UR10 robot
• ROS2 integration for control
• Enhanced current prediction accuracy

Innovation

• Dynamic model identification
• Triple-stage approach
• ROS2 software integration