Digital twin of dynamic error of a collaborative robot

Charles Walker, Xichun Luo, Abhilash P M, Qi Liu, Rajeshkumar Madarkar, Erfu Yang

Research output: Contribution to conferenceProceedingpeer-review

29 Downloads (Pure)

Abstract

This paper proposed a new digital twin method to effectively, accurately and in real-time in-situ track machine dynamic error using accelerometer data. The digital twin tracked the positioning data measured by its built-in encoders and superimposes it with displacement data obtained from the accelerometers for more accurate positioning, resulting in micrometre level improvements. In this paper, the digital twin dynamic error tracking approach was implemented on a collaborative robot. Ball-bar tests were conducted to evaluate the effectiveness of the proposed digital twin dynamic error tracking approach. The results show a significantly improved position tracking accuracy of up to 75%, compared with using the collaborative robot’s built-in encoders. The digital twin provides a cost-effective solution to track machine dynamic errors. This method could also be expanded to work on other CNC machines and robots, making it a universal solution for improving machine dynamic measurement accuracy.
Original languageEnglish
Number of pages4
Publication statusPublished - 12 Jun 2023
EventEuspen's 23rd International Conference & Exhibition, Copenhagen, Denmark - Copenhagen, Denmark
Duration: 12 Jun 202316 Jun 2023

Conference

ConferenceEuspen's 23rd International Conference & Exhibition, Copenhagen, Denmark
Country/TerritoryDenmark
CityCopenhagen
Period12/06/2316/06/23

Keywords

  • digital twin
  • dynamic error
  • accelerometer

Fingerprint

Dive into the research topics of 'Digital twin of dynamic error of a collaborative robot'. Together they form a unique fingerprint.

Cite this