Abstract

During manufacturing processes, such as clamping and drilling of elastic structures, it is essential to maintain tool–workpiece normality to minimize shear forces and torques, thereby preventing damage to the tool or the workpiece. The challenge arises in making precise model-based predictions of the relatively large deformations that occur as the applied normal force (e.g., clamping force) is increased. However, precision deformation predictions are essential for selecting the optimal robot pose that maintains force normality. Therefore, recent works have employed force–displacement measurements at each work location to determine the robot pose for maintaining tool normality. Nevertheless, this approach, which relies on local measurements at each work location and at each gradual increment of the applied normal force, can be slow and consequently time prohibitive. The main contributions of this work are: (i) to use Gaussian process (GP) methods to learn the robot-pose map for force normality at unmeasured workpiece locations; and (ii) to use active learning to optimally select and minimize the number of measurement locations needed for accurate learning of the robot-pose map. Experimental results show that the number of data points needed with active learning is 77.8% less than the case with a benchmark linear positioning learning for the same level of model precision. Additionally, the learned robot-pose map enables a rapid increase of the normal force at unmeasured locations on the workpiece, reaching force-increment rates up to eight times faster than the original force-increment rate when the robot is learning the correct pose.

References

1.
Gao
,
Y.
,
Wu
,
D.
,
Nan
,
C.
,
Ma
,
X.
, and
Chen
,
K.
,
2014
, “
Optimization Design for Normal Direction Measurement in Robotic Drilling
,”
ASME
Paper No. IMECE2014-36496.10.1115/IMECE2014-36496
2.
Diaz Posada
,
J. R.
,
Schneider
,
U.
,
Pidan
,
S.
,
Geravand
,
M.
,
Stelzer
,
P.
, and
Verl
,
A.
,
2016
, “
High Accurate Robotic Drilling With External Sensor and Compliance Model-Based Compensation
,” 2016 IEEE International Conference on Robotics and Automation (
ICRA
), Stockholm, Sweden, May 16–21, pp.
3901
3907
.10.1109/ICRA.2016.7487579
3.
Verl
,
A.
,
Valente
,
A.
,
Melkote
,
S.
,
Brecher
,
C.
,
Ozturk
,
E.
, and
Tunc
,
L. T.
,
2019
, “
Robots in Machining
,”
CIRP Ann.
,
68
(
2
), pp.
799
822
.10.1016/j.cirp.2019.05.009
4.
Costa
,
M.
,
Gouveia
,
R.
,
Silva
,
F.
, and
Campilho
,
R.
,
2018
, “
How to Solve Quality Problems by Advanced Fully-Automated Manufacturing Systems
,”
Int. J. Adv. Manuf. Technol.
,
94
(
9–12
), pp.
3041
3063
.10.1007/s00170-017-0158-8
5.
Pereira
,
B.
,
Griffiths
,
C. A.
,
Birch
,
B.
, and
Rees
,
A.
,
2022
, “
Optimization of an Autonomous Robotic Drilling System for the Machining of Aluminum Aerospace Alloys
,”
Int. J. Adv. Manuf. Technol.
, 119(3–4), pp.
2429
2444
.10.1007/s00170-021-08483-4
6.
van Duin
,
S.
, and
Kihlman
,
H.
,
2005
, “
Robotic Normalizing Force Feedback
,”
SAE
Paper No. 2005-01-3291.10.4271/2005-01-3291
7.
Guo
,
Y.
,
Dong
,
H.
, and
Ke
,
Y.
,
2015
, “
Stiffness-Oriented Posture Optimization in Robotic Machining Applications
,”
Rob. Comput.-Integr. Manuf.
,
35
, pp.
69
76
.10.1016/j.rcim.2015.02.006
8.
Bu
,
Y.
,
Liao
,
W.
,
Tian
,
W.
,
Zhang
,
J.
, and
Zhang
,
L.
,
2017
, “
Stiffness Analysis and Optimization in Robotic Drilling Application
,”
Precis. Eng.
,
49
, pp.
388
400
.10.1016/j.precisioneng.2017.04.001
9.
Cvitanic
,
T.
,
Nguyen
,
V.
, and
Melkote
,
S. N.
,
2020
, “
Pose Optimization in Robotic Machining Using Static and Dynamic Stiffness Models
,”
Rob. Comput.-Integr. Manuf.
,
66
, p.
101992
.10.1016/j.rcim.2020.101992
10.
Olsson
,
T.
,
Haage
,
M.
,
Kihlman
,
H.
,
Johansson
,
R.
,
Nilsson
,
K.
,
Robertsson
,
A.
,
Björkman
,
M.
,
Isaksson
,
R.
,
Ossbahr
,
G.
, and
Brogårdh
,
T.
,
2010
, “
Cost-Efficient Drilling Using Industrial Robots With High-Bandwidth Force Feedback
,”
Rob. Comput.-Integr. Manuf.
,
26
(
1
), pp.
24
38
.10.1016/j.rcim.2009.01.002
11.
Frommknecht
,
A.
,
Kuehnle
,
J.
,
Effenberger
,
I.
, and
Pidan
,
S.
,
2017
, “
Multi-Sensor Measurement System for Robotic Drilling
,”
Rob. Comput.-Integr. Manuf.
,
47
, pp.
4
10
.10.1016/j.rcim.2017.01.002
12.
Zhang
,
L.
,
Dhupia
,
J.
, and
Wu
,
M.
,
2018
, “
Analysis and Comparison of Control Strategies for Normal Adjustment of a Robotic Drilling End-Effector
,”
J. Vibroeng.
,
20
(
7
), pp.
2651
2667
.10.21595/jve.2018.19892
13.
Hou
,
Z.-S.
, and
Wang
,
Z.
,
2013
, “
From Model-Based Control to Data-Driven Control: Survey, Classification and Perspective
,”
Inf. Sci.
,
235
, pp.
3
35
.10.1016/j.ins.2012.07.014
14.
Ji
,
W.
, and
Wang
,
L.
,
2019
, “
Industrial Robotic Machining: A Review
,”
Int. J. Adv. Manuf. Technol.
,
103
(
1–4
), pp.
1239
1255
.10.1007/s00170-019-03403-z
15.
Wang
,
Z.
,
Zhang
,
R.
, and
Keogh
,
P.
,
2020
, “
Real-Time Laser Tracker Compensation of Robotic Drilling and Machining
,”
J. Manuf. Mater. Process.
,
4
(
3
), p.
79
.10.3390/jmmp4030079
16.
Schneider
,
U.
,
Drust
,
M.
,
Ansaloni
,
M.
,
Lehmann
,
C.
,
Pellicciari
,
M.
,
Leali
,
F.
,
Gunnink
,
J. W.
, and
Verl
,
A.
,
2016
, “
Improving Robotic Machining Accuracy Through Experimental Error Investigation and Modular Compensation
,”
Int. J. Adv. Manuf. Technol.
,
85
(
1–4
), pp.
3
15
.10.1007/s00170-014-6021-2
17.
Enebuse
,
I.
,
Foo
,
M.
,
Ibrahim
,
B. S. K. K.
,
Ahmed
,
H.
,
Supmak
,
F.
, and
Eyobu
,
O. S.
,
2021
, “
A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots
,”
IEEE Access
,
9
, pp.
113143
113155
.10.1109/ACCESS.2021.3104514
18.
Hsiao
,
J.-C.
,
Shivam
,
K.
,
Lu
,
I.-F.
, and
Kam
,
T.-Y.
,
2020
, “
Positioning Accuracy Improvement of Industrial Robots Considering Configuration and Payload Effects Via a Hybrid Calibration Approach
,”
IEEE Access
,
8
, pp.
228992
229005
.10.1109/ACCESS.2020.3045598
19.
Enebuse
,
I.
,
Ibrahim
,
B. K. S. M. K.
,
Foo
,
M.
,
Matharu
,
R. S.
, and
Ahmed
,
H.
,
2022
, “
Accuracy Evaluation of Hand-Eye Calibration Techniques for Vision-Guided Robots
,”
PLoS One
,
17
(
10
), p.
e0273261
.10.1371/journal.pone.0273261
20.
McCann
,
L.
,
Lee
,
C.-N.
,
Gombo
,
Y.
,
Garbini
,
J.
, and
Devasia
,
S.
,
2019
, “
Data-Based Learning for Control of Elastic Interactions Between Robot and Workpiece
,”
ASME
Paper No. DSCC2019-9200.10.1115/DSCC2019-9200
21.
McCann
,
L.
,
Gombo
,
Y.
,
Tiwari
,
A.
,
Garbini
,
J.
, and
Devasia
,
S.
,
2023
, “
Data-Based Stiffness Estimation for Control of Robot–Workpiece Elastic Interactions
,”
ASME Lett. Dyn. Syst. Control
,
3
(
3
), p.
031003
.10.1115/1.4063606
22.
Rasmussen
,
C. E.
, and
Williams
,
C. K. I.
,
2006
,
Gaussian Processes for Machine Learning
,
The MIT Press
,
Cambridge, MA
.
23.
Chen
,
S.-F.
, and
Kao
,
I.
,
2000
, “
Conservative Congruence Transformation for Joint and Cartesian Stiffness Matrices of Robotic Hands and Fingers
,”
Int. J. Rob. Res.
,
19
(
9
), pp.
835
847
.10.1177/02783640022067201
24.
Settles
,
B.
,
2009
, “
Active Learning Literature Survey
,” University of Wisconsin– Madison, Madison, WI, Report No. 1648.
25.
Gong
,
X.
, and
Pan
,
Y.
,
2022
, “
Discussion: ‘Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions’ (Pandita, P., Bilionis, I., and Panchal, J., 2019, ASME J. Mech. Des., 141(10), p. 101404)
,”
ASME J. Mech. Des.
,
144
(
5
), p.
055501
.10.1115/1.4053112
26.
Wu
,
D.
,
2019
, “
Pool-Based Sequential Active Learning for Regression
,”
IEEE Trans. Neural Networks Learn. Syst.
,
30
(
5
), pp.
1348
1359
.10.1109/TNNLS.2018.2868649
27.
Chin
,
R.
,
Maass
,
A. I.
,
Ulapane
,
N.
,
Manzie
,
C.
,
Shames
,
I.
,
Nešić
,
D.
,
Rowe
,
J. E.
, and
Nakada
,
H.
,
2020
, “
Active Learning for Linear Parameter-Varying System Identification
,”
IFAC-PapersOnLine
,
53
(
2
), pp.
989
994
.10.1016/j.ifacol.2020.12.1274
28.
Shahriari
,
B.
,
Swersky
,
K.
,
Wang
,
Z.
,
Adams
,
R. P.
, and
De Freitas
,
N.
,
2016
, “
Taking the Human Out of the Loop: A Review of Bayesian Optimization
,”
Proc. IEEE
,
104
(
1
), pp.
148
175
.10.1109/JPROC.2015.2494218
29.
Zhang
,
Q.
, and
Zhao
,
M.-Y.
,
2016
, “
Minimum Time Path Planning of Robotic Manipulator in Drilling/Spot Welding Tasks
,”
J. Comput. Des. Eng.
,
3
(
2
), pp.
132
139
.10.1016/j.jcde.2015.10.004
30.
Dewil
,
R.
,
Küçükoğlu
,
İ.
,
Luteyn
,
C.
, and
Cattrysse
,
D.
,
2019
, “
A Critical Review of Multi-Hole Drilling Path Optimization
,”
Arch. Comput. Methods Eng.
,
26
(
2
), pp.
449
459
.10.1007/s11831-018-9251-x
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