Abstract
This paper investigates the direct application of standardized designs on the robot for conducting robot hand–eye calibration by employing 3D scanners with collaborative robots. The well-established geometric features of the robot flange are exploited by directly capturing its point cloud data. In particular, an iterative method is proposed to facilitate point cloud processing towards a refined calibration outcome. Several extensive experiments are conducted over a range of collaborative robots, including Universal Robots UR5 & UR10 e-series, Franka Emika, and AUBO i5 using an industrial-grade 3D scanner Photoneo Phoxi S & M and a commercial-grade 3D scanner Microsoft Azure Kinect DK. Experimental results show that translational and rotational errors converge efficiently to less than 0.28 mm and 0.25 degrees, respectively, achieving a hand–eye calibration accuracy as high as the camera’s resolution, probing the hardware limit. A welding seam tracking system is presented, combining the flange-based calibration method with soft tactile sensing. The experiment results show that the system enables the robot to adjust its motion in real-time, ensuring consistent weld quality and paving the way for more efficient and adaptable manufacturing processes.
Links
BibTeX (Download)
@article{Han2024OnFlange,
title = {On Flange-Based 3D Hand-Eye Calibration for Soft Robotic Tactile Welding},
author = {Xudong Han and Ning Guo and Yu Jie and He Wang and Fang Wan and Chaoyang Song},
doi = {10.1016/j.measurement.2024.115376},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
journal = {Measurement},
volume = {238},
issue = {October},
pages = {115376},
abstract = {This paper investigates the direct application of standardized designs on the robot for conducting robot hand–eye calibration by employing 3D scanners with collaborative robots. The well-established geometric features of the robot flange are exploited by directly capturing its point cloud data. In particular, an iterative method is proposed to facilitate point cloud processing towards a refined calibration outcome. Several extensive experiments are conducted over a range of collaborative robots, including Universal Robots UR5 & UR10 e-series, Franka Emika, and AUBO i5 using an industrial-grade 3D scanner Photoneo Phoxi S & M and a commercial-grade 3D scanner Microsoft Azure Kinect DK. Experimental results show that translational and rotational errors converge efficiently to less than 0.28 mm and 0.25 degrees, respectively, achieving a hand–eye calibration accuracy as high as the camera’s resolution, probing the hardware limit. A welding seam tracking system is presented, combining the flange-based calibration method with soft tactile sensing. The experiment results show that the system enables the robot to adjust its motion in real-time, ensuring consistent weld quality and paving the way for more efficient and adaptable manufacturing processes.},
keywords = {Corresponding Author, JCR Q1, Measurement},
pubstate = {published},
tppubtype = {article}
}
