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

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.