On Flange-based 3D Hand-Eye Calibration for Soft Robotic Tactile Welding

TL;DR

This paper presents a flange-based 3D hand-eye calibration method using point cloud data from 3D scanners, achieving high accuracy and enabling real-time adaptive welding with soft tactile sensing.

Contribution

It introduces an iterative point cloud processing method for flange-based calibration and demonstrates its effectiveness across multiple collaborative robots.

Findings

Calibration errors less than 0.28 mm and 0.25 degrees.

Achieved hardware-limited calibration accuracy.

Enabled real-time adaptive welding with tactile sensing.

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 toward 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.