Abstract

Delta robots are prominent examples of agile parallel kinematic machines (PKMs) designed for highly dynamic pick-and-place tasks. Optimized minimum time trajectories lead to dynamic load cycles, induce vibrations, and cause overshooting of the end effector (EE) due to the flexibility of the PKM. Crucial to mitigate these effects by means of model-based control is a dynamics model that accounts for the principal elastic compliance, such as gear stiffness and structural elasticities. However, robot manufacturers do not provide data on the structural stiffness. Also, established dynamics identification methods cannot determine stiffness and damping parameters. In this article, a two-step frequency domain identification method is proposed to identify elastic properties by examples of an industrial Delta robot. As a peculiarity of the Delta PKM, the identification is carried out when the platform is removed and for the complete PKM. This allows distinguishing elasticities of the gear-drive units and of the struts. The identified parameters are employed for motion correction to avoid overshooting. This correction does not interfere with the original planning and control function of the industrial robot. Three motion correction schemes (preloading of drives, quasistatic correction, flatness based) are compared. Laser tracker measurements of the EE confirm a drastic reduction of overshooting and thus an increase in the overall tracking accuracy.

References

1.
Wang
,
L.
,
Wu
,
X.
,
Gao
,
Y.
,
Chen
,
X.
, and
Wang
,
B.
,
2024
, “
Sensitivity Analysis of Performance Tests for Six-Degree-of-Freedom Serial Industrial Robots
,”
ASME J. Mech. Rob.
,
16
(
9
), p.
091009
.
2.
Spong
,
M. W.
,
1987
, “
Modeling and Control of Elastic Joint Robots
,”
ASME J. Dyn. Sys., Meas., Control
,
109
(
4
), pp.
310
318
.
3.
Khalil
,
W.
, and
Gautier
,
M.
,
2000
, “
Modeling of Mechanical Systems With Lumped Elasticity
,”
IEEE/RSJ International Conference on Intelligent Robots and Systems
,
San Francisco, CA
,
Apr. 24–28
, Vol. 4, IEEE, pp.
3964
3969
.
4.
Piras
,
G.
,
Cleghorn
,
W.
, and
Mills
,
J.
,
2005
, “
Dynamic Finite-Element Analysis of a Planar High-Speed, High-Precision Parallel Manipulator With Flexible Links
,”
Mech. Mach. Theory
,
40
(
7
), pp.
849
862
.
5.
Deblaise
,
D.
,
Hernot
,
X.
, and
Maurine
,
P.
,
2006
, “
A Systematic Analytical Method for PKM Stiffness Matrix Calculation
,”
IEEE/RSJ International Conference on Intelligent Robots and Systems
,
Orlando, FL
,
May 15–19
, pp.
4213
4219
.
6.
Zimmermann
,
S. A.
,
Berninger
,
T. F.
,
Derkx
,
J.
, and
Rixen
,
D. J.
,
2020
, “
Dynamic Modeling of Robotic Manipulators for Accuracy Evaluation
,”
IEEE International Conference on Robotics and Automation
,
Paris, France
,
May 31–Aug. 31
, pp.
8144
8150
.
7.
Isidori
,
A.
,
1985
,
Nonlinear Control Systems: An Introduction
,
Springer Berlin, Heidelberg
.
8.
De Luca
,
A.
, and
Lanari
,
L.
,
1995
, “
Robots With Elastic Joints Are Linearizable via Dynamic Feedback
,”
IEEE Conference on Decision and Control
,
New Orleans, LA
,
Dec. 13–15
, Vol. 4, pp.
3895
3897
.
9.
Pfeiffer
,
F.
, and
Kleemann
,
U.
,
1989
, “
Elasticity and Vibration Control for Manipulators
,”
ICCON IEEE International Conference on Control and Applications
,
Jerusalem, Israel
, pp.
49
55
.
10.
Höbarth
,
W.
,
Gattringer
,
H.
, and
Bremer
,
H.
,
2008
, “
Modelling and Control of an Articulated Robot With Flexible Links/Joints
,”
International Conference on Motion and Vibration Control
,
Munich, Germany
,
Sept. 15–18
.
11.
Sousa
,
C. D.
, and
Cortesão
,
R.
,
2013
, “
Physically Feasible Dynamic Parameter Identification of the 7-dof Wam Robot
,”
IEEE/RSJ International Conference on Intelligent Robots and Systems
,
Tokyo, Japan
,
Nov. 3–7
, pp.
2868
2873
.
12.
Traversaro
,
S.
,
Brossette
,
S.
,
Escande
,
A.
, and
Nori
,
F.
,
2016
, “
Identification of Fully Physical Consistent Inertial Parameters Using Optimization on Manifolds
,”
IEEE/RSJ International Conference on Intelligent Robots and Systems
,
Daejeon, South Korea
,
Oct. 9–14
, pp.
5446
5451
.
13.
Wensing
,
P. M.
,
Kim
,
S.
, and
Slotine
,
J. -J.
,
2018
, “
Linear Matrix Inequalities for Physically-Consistent Inertial Parameter Identification: A Statistical Perspective on the Mass Distribution
,”
IEEE Robot. Autom. Lett.
,
3
(
1
), pp.
60
67
.
14.
Gaz
,
C.
,
Cognetti
,
M.
,
Oliva
,
A.
,
Robuffo Giordano
,
P.
, and
De Luca
,
A.
,
2019
, “
Dynamic Identification of the Franka Emika Panda Robot With Retrieval of Feasible Parameters Using Penalty-Based Optimization
,”
IEEE Robot. Autom. Lett.
,
4
(
4
), pp.
4147
4154
.
15.
Briot
,
S.
,
Caro
,
S.
, and
Germain
,
C.
,
2017
, “
Design Procedure for a Fast and Accurate Parallel Manipulator
,”
ASME J. Mech. Rob.
,
9
(
6
), p.
061012
.
16.
Pham
,
M. T.
,
Yeo
,
S. H.
,
Teo
,
T. J.
,
Wang
,
P.
, and
Nai
,
M. L. S.
,
2019
, “
Design and Optimization of a Three Degrees-of-Freedom Spatial Motion Compliant Parallel Mechanism With Fully Decoupled Motion Characteristics
,”
ASME J. Mech. Rob.
,
11
(
5
), p.
051010
.
17.
Wernholt
,
E.
, and
Gunnarsson
,
S.
,
2006
, “
Detection and Estimation of Nonlinear Distortions in Industrial Robots
,”
IEEE Transactions on Instrumentation and Measurement Technical Conference
,
Sorrento, Italy
,
Apr. 24–27
, pp.
1913
1918
.
18.
Wernholt
,
E.
, and
Lofberg
,
J.
,
2007
, “
Experiment Design for Identification of Nonlinear Gray-Box Models With Application to Industrial Robots
,”
IEEE Conference on Decision and Control
,
New Orleans, LA
,
Dec. 12–14
, pp.
5110
5116
.
19.
Wernholt
,
E.
, and
Moberg
,
S.
,
2008
, “
Frequency-Domain Gray-Box Identification of Industrial Robots
,”
IFAC Proc. Vols.
,
41
(
2
), pp.
15372
15380
.
20.
Zimmermann
,
S. A.
,
Enqvist
,
M.
,
Gunnarsson
,
S.
,
Moberg
,
S.
, and
Norrlöf
,
M.
,
2022
, “
Improving Experiment Design for Frequency-Domain Identification of Industrial Robots
,”
IFAC-PapersOnLine
,
55
(
37
), pp.
475
480
.
21.
Ljung
,
L.
,
1999
,
System Identification: Theory for the User
,
Prentice Hall PTR
,
Hoboken, NJ
.
22.
Pintelon
,
R.
, and
Schoukens
,
J.
,
2012
,
System Identification: A Frequency Domain Approach
,
Wiley-IEEE Press
.
23.
Hardeman
,
T.
,
2008
, “
Modelling and Identification of Industrial Robots Including Drive and Joint Flexibilities
,” Ph.D. thesis,
University of Twente
,
Twente
.
24.
Neubauer
,
M.
,
Gattringer
,
H.
,
Müller
,
A.
,
Steinhauser
,
A.
, and
Höbarth
,
W.
,
2015
, “
A Two-Stage Calibration Method for Industrial Robots With Joint and Drive Flexibilities
,”
Mech. Sci.
,
6
(
2
), pp.
191
201
.
25.
Hernandez
,
J. M. E.
,
Chemori
,
A.
, and
Sierra
,
H. A.
,
2022
,
Modeling and Nonlinear Robust Control of Delta-Like Parallel Kinematic Manipulators
,
Elsevier
.
26.
Gnad
,
D.
,
Müller
,
A.
, and
Gattringer
,
H.
,
2024
, “
Dedicated Dynamic Parameter Identification for Delta-Like Robots
,”
IEEE Robot. Autom. Lett.
,
9
(
5
), pp.
4393
4400
.
27.
Bremer
,
H.
,
2008
,
Elastic Multibody Dynamics
,
Springer Netherlands
.
28.
Wernholt
,
E.
, and
Moberg
,
S.
,
2008
, “
Experimental Comparison of Methods for Multivariable Frequency Response Function Estimation
,”
IFAC Proc. Vols.
,
41
(
2
), pp.
15359
15366
.
29.
Cseppento
,
B.
,
Retzler
,
A.
, and
Kollar
,
Z.
,
2023
, “
Optimization of the Crest Factor for Complex-Valued Multisine Signals
,”
Radioengineering
,
32
(
2
), pp.
264
272
.
30.
Khalil
,
H.
,
2002
,
Nonlinear Systems
,
Prentice Hall
,
Upper Saddle River, NJ
.
31.
Gnad
,
D.
,
Gattringer
,
H.
,
Müller
,
A.
,
Höbarth
,
W.
,
Riepl
,
R.
, and
Messner
,
L.
,
2024
, “
Identification of Physically Consistent Dynamics Parameter of the ABB IRB 360-6/1600 Delta Robot and Its Use for Time-Optimal Motion Planning Under Consideration of Constraint Forces
,”
Rob. Auton. Syst.
,
182
, p.
104823
.
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