A comparative study of network sensor and laser tracker in establishing digital twin for robotic manufacturing

Zhaosheng Li, Francesco Giorgio-Serchi, Nicholas Southon, Andrew Brown, Nan Yu

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

This study employs network sensors and laser trackers to track a robot's end-effector and assess the performance of network sensors through comparative experimentation. The establishment of a digital twin for robots using network sensors contributes to enhancing the robot's global accuracy. The novel network sensor, IONA, is capable of providing real-time 6DOF data to the robot, thus assisting in improving the robot's global accuracy. To evaluate the tracking capabilities of the network sensor two sets of experiments with different robot motion modes are designed, with a laser tracker serving as the reference benchmark. These experiments encompass linear and circular motions executed by the robot, each repeated multiple time. The robot's motion speed varies across three orthogonal directions, ranging from 0.5 m/s to 0.01 m/s, encompassing six distinct speed levels. The analysis of the collected experimental data sets indicates that the network sensor exhibits a dynamic tracking accuracy of 0.45 mm when the target motion speed is below 0.5 m/s.
Original languageEnglish
Number of pages5
Publication statusE-pub ahead of print - 10 Jun 2024
Eventeuspen’s 24th International Conference & Exhibition, Dublin, IE, June 2024 -
Duration: 10 Jun 202414 Jun 2024

Conference

Conferenceeuspen’s 24th International Conference & Exhibition, Dublin, IE, June 2024
Period10/06/2414/06/24

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