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A Two-Step Method for Dynamic Parameter Identification of Indy7 Collaborative Robot Manipulator.

Meseret Tadese¹, Nabih Pico^1,2, Sungwon Seo¹

¹Department of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea.

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|December 23, 2022

Summary

This summary is machine-generated.

This study introduces a novel dynamic friction model for collaborative robots, accounting for temperature effects to enhance model accuracy. The improved model significantly boosts performance in torque tracking, achieving up to 70.37% improvement.

Keywords:

dynamic parameters identification friction identification friction model robot dynamics

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Area of Science:

Robotics
Mechanical Engineering
Control Systems

Background:

Accurate dynamic models are crucial for collaborative robot performance in control and simulation.
Robot joint friction is a primary nonlinear element significantly impacting model accuracy.
Temperature fluctuations affect robot joint friction, necessitating a temperature-aware model.

Purpose of the Study:

To develop and validate a reliable dynamic friction model that incorporates temperature effects for collaborative robots.
To improve the accuracy of identified dynamic parameters by compensating for temperature-dependent friction.
To enhance the performance of model-based control and dynamic simulations.

Main Methods:

Investigated robot joint friction across a wide velocity range and temperatures (19°C to 51°C).
Employed an inverse dynamics identification model with weighted least squares regression for parameter identification.
Validated the proposed method experimentally on the Indy7 collaborative robot manipulator.

Main Results:

The identified friction model accurately captures the influence of temperature on robot joint friction.
Dynamic parameters identified using the proposed method led to a significant improvement in torque tracking accuracy.
A relative improvement of up to 70.37% in tracking measured torque was achieved compared to conventional methods.

Conclusions:

The proposed temperature-dependent dynamic friction model enhances the accuracy of identified dynamic parameters for collaborative robots.
This improved model leads to superior performance in applications requiring accurate torque tracking.
The method offers a practical approach to improving the reliability and performance of collaborative robot systems.