Accurate edge detection for robotic welding through tactile exploration
Shameek Ganguly, Oussama Khatib
Abstract
Programming paths for robotic welding conventionally requires precise positioning of workpieces, detailed 3D models and/or tedious teach pendant programming. A new method is introduced in this paper that enables an operator to teach the weld path to the robot through a haptic-visual interface. The operator teaches the path by guiding the tool tip to contact on the workpiece surface with force feedback through the haptic device, and drawing exploratory paths that intersect the edge to be welded as well as adjoining surfaces. Tool-tip positions in contact with the workpiece are recorded. A RANSAC-type algorithm is used to automatically estimate a piecewise parametric curve along the edge as well as geometric parameters of the adjoining surfaces. The required tool trajectory for the robot to weld along the workpiece edge is automatically generated. Experiments performed in simulation and on a physical KUKA IIWA7 robot demonstrate that the developed method can successfully detect workpiece edges within a maximum deviation of 1mm. Furthermore, the method is intuitive, and requires no knowledge of robot programming for an operator to program multi-segment weld paths quickly.
BibTeX
@inproceedings{iros2022_accurateedgedete,
title = {Accurate edge detection for robotic welding through tactile exploration},
author = {Shameek Ganguly and Oussama Khatib},
booktitle = {IROS 2022},
year = {2022}
}