Graphical Abstract Figure
Graphical Abstract Figure
Close modal

Abstract

A significant amount of manufacturing is performed by small to medium enterprises (SMEs), but these manufacturers often have lower adoption rates of automation. The cost and complexity associated with traditional robot systems slow the adoption of robotic welding operations for SMEs. The recent increase in collaborative robotic welding systems is bridging the gap, however, by reducing the complexity of installing, maintaining, and training operators to perform weld operations with collaborative robotics. These systems add flexibility in the range of operator use and ease of deployment. These systems however still rely on kinematic registration between the robot-mounted torch and workpiece on any open-loop weld. This requires precise placement of the workpiece prior to performing a weld. This work will discuss a method for part identification and registration in a welding task as a step toward automated weld path generation. The method is based on lower-resolution 3D cameras (RGBD cameras) using a combination of color and depth information. This information is used to both identify the workpiece within a workspace that may have other, non-workpiece items, and then provide registration or localization information of the workpiece within a resolution that could allow follow-on near-position strategies to achieve final weld path identification.

References

1.
Mahajan
,
A.
, and
Figueroa
,
F.
,
1997
, “
Intelligent Seam Tracking Using Ultrasonic Sensors for Robotic Welding
,”
Robotica
,
15
(
3
), pp.
275
281
.
2.
Putnick
,
G.
,
Petrovic
,
P.
, and
Shah
,
V.
,
2024
, “
Spatial Visual Feedback for Robotic Arc-Welding Enforced by Inductive Machine Learning
,”
ASME J. Manuf. Sci. Eng.
,
146
(
4
), p.
040902
.
3.
Galin
,
R.
,
Meshcheryakov
,
R.
,
Kamesheva
,
S.
, and
Samoshina
,
A.
,
2020
, “
Cobots and the Benefits of Their Implementation in Intelligent Manufacturing
,”
IOP Conference Series: Materials Science and Engineering
,
862
, p.
032075
.
4.
Shah
,
H.
,
Sulaiman
,
M.
,
Shukor
,
A.
,
Jamaluddin
,
M.
, and
Rashid
,
M.
,
2016
, “
A Review Paper on Vision Based Identification, Detection and Tracking of Weld Seams Path in Welding Robot Environment
,”
Modern Appl. Sci.
,
10
(
2
), pp.
83
89
.
5.
Schleth
,
G.
,
Kuss
,
A.
, and
Kraus
,
W.
,
2018
, “
Workpiece Localization Methods for Robotic Welding—A Review
,”
ISR 2018: 50th International Symposium on Robotics
,
Munich, Germany
,
June 20–21
.
6.
Kuss
,
A.
,
Diaz
,
J.
,
Hollmann
,
R.
,
Dietz
,
T.
, and
Hagele
,
M.
,
2016
, “
Manufacturing Knowledge for Industrial Robot Systems: Review and Synthesis of Model Architecture
,”
2016 IEEE International Conference on Automation Science and Engineering (CASE)
.
7.
Gao
,
J.
,
Li
,
F.
,
Zhang
,
C.
,
He
,
W.
,
He
,
J.
, and
Chen
,
X.
,
2021
, “
A Method of D-Type Weld Seam Extraction Based on Point Clouds
,”
IEEE Access
,
9
(
1
), pp.
65401
65410
.
8.
Manzoor
,
S.
,
Joo
,
S.
,
Kim
,
E.
,
Bae
,
S.
,
In
,
G.
,
Pyo
,
J.
, and
Kuc
,
T.
,
2021
, “
3D Recognition Based on Sensor Modalities for Robotic Systems: A Survey
,”
Sensors
,
21
(
21
), p.
7120
.
9.
Schreiber
,
L.-T.
, and
Gosselin
,
C.
,
2022
, “
Determination of the Inverse Kinematics Brances of Solution Based on Joint Coordinates for Universal Robots-Like Serial Robot Architecture
,”
ASME J. Mech. Rob.
,
14
(
3
), p.
034501
.
10.
Canfield
,
S. L.
,
Owens
,
J. S.
, and
Zuccaro
,
S. G.
,
2021
, “
Zero Moment Control for Lead-Through Teach Programming and Process Monitoring of a Collaborative Welding Robot
,”
ASME J. Mech. Rob.
,
13
(
3
), p.
031016
.
11.
Hill
,
T.
,
Canfield
,
S.
, and
Shelton
,
R.
,
2022
, “
Automated Weld Path Generation Using Random Sample Consensus and Iterative Closest Point Workpiece Localization
,”
ASME International Design Engineering Technical Conferences
,
St. Louis, MO
,
Aug. 14–17
.
12.
Li
,
J.
,
Zhou
,
Q.
,
Li
,
X.
,
Chen
,
R.
, and
Ni
,
K.
,
2019
, “
An Improved Low-Noise Processing Methodology Combined With PCL for Industry Inspection Based on Laser Line Scanner
,”
Sensors
,
19
(
15
), p.
3398
.
13.
Zhan
,
Q.
,
Liang
,
Y.
, and
Xiao
,
Y.
,
2009
, “
Color-Based Segmentation of Point Clouds
,”
Laser Scanning 2009, IAPRS
,
Paris, France
,
Sept. 1–2
.
14.
Zhang
,
J.
,
Yao
,
Y.
, and
Deng
,
B.
,
2020
, “
Fast and Robust Iterative Closest Point
,”
IEEE Trans. Pattern Anal. Mach. Intell.
,
44
, pp.
3450
3466
. https://api.semanticscholar.org/CorpusID:229934524
You do not currently have access to this content.