Abstract
Advanced industrial robotic assembly requires the process parameters to be tuned to achieve high efficiency: short assembly cycle (AC) time and high first-time throughput (FTT) rate. This task is usually undertaken offline because of the difficulties in real-time modeling and the lack of efficient algorithms. This paper proposes a support vector regression (SVR)-enabled method to optimize the assembly process parameters without interrupting the normal production process. To reduce the risk of obtaining a local minimum, we consider the trade-off between exploration and exploitation and propose an adaptive optimization process to balance the production processes and the optimization outcome. The proposed methods have been verified using a typical peg-in-hole robotic assembly process, and the results are compared with design of experiment (DOE) methods and genetic algorithm (GA) method in terms of efficiency and accuracy. The experimental results show that our methods are able to maintain the high FTT rate when it drops below 99%, shorten the average AC time by 3.4%, and reduce the number of assembly trials to find the optimized process parameters by 99.6%.
Issue Section:
Research Papers
References
1.
Chen
, H.
, Wang
, J.
, Zhang
, G.
, Fuhlbrigge
, T.
, and Kock
, S.
, 2009
, “High-Precision Assembly Automation Based on Robot Compliance
,” Int. J. Adv. Manuf. Technol.
, 45
(9–10
), pp. 999
–1006
. 10.1007/s00170-009-2041-82.
Meyer
, F.
, Spröwitz
, A.
, and Berthouze
, L.
, 2006
, “Passive Compliance for a rc Servo-Controlled Bouncing Robot
,” Adv. Rob.
, 20
(8
), pp. 953
–961
. 10.1163/1568553067779514293.
Lee
, S.
, 2005
, “Development of a New Variable Remote Center Compliance (VRCC) With Modified Elastomer Shear Pad (ESP) for Robot Assembly
,” IEEE Trans. Autom. Sci. Eng.
, 2
(2
), pp. 193
–197
. 10.1109/TASE.2005.8444374.
Bang
, Y.-B.
, Lee
, K.-m.
, Kook
, J.
, Lee
, W.
, and Kim
, I.-S.
, 2005
, “Micro Parts Assembly System With Micro Gripper and Rcc Unit
,” IEEE Trans. Rob.
, 21
(3
), pp. 465
–470
. 10.1109/TRO.2004.8380285.
Brogårdh
, T.
, 2007
, “Present and Future Robot Control Development—An Industrial Perspective
,” Ann. Rev. Control
, 31
(1
), pp. 69
–79
. 10.1016/j.arcontrol.2007.01.0026.
Xu
, Q.
, 2013
, “Precision Position/Force Interaction Control of a Piezoelectric Multimorph Microgripper for Microassembly
,” IEEE Trans. Autom. Sci. Eng.
, 10
(3
), pp. 503
–514
. 10.1109/TASE.2013.22392887.
Kesner
, S.
, and Howe
, R.
, 2011
, “Design Principles for Rapid Prototyping Forces Sensors Using 3-d Printing
,” IEEE/ASME Trans. Mechatron.
, 16
(5
), pp. 866
–870
. 10.1109/TMECH.2011.21603538.
Chen
, H.
, Li
, B.
, Gravel
, D.
, Zhang
, G.
, and Zhang
, B.
, 2015
, “Robot Learning for Complex Manufacturing Process
,” 2015 IEEE International Conference on Industrial Technology (ICIT)
, Seville, Spain
, Mar. 17–19
, IEEE
, pp. 3207
–3211
.9.
Kim
, S.
, Kim
, J.-P.
, and Ryu
, J.
, 2014
, “Adaptive Energy-Bounding Approach for Robustly Stable Interaction Control of Impedance-Controlled Industrial Robot With Uncertain Environments
,” IEEE/ASME Trans. Mechatron.
, 19
(4
), pp. 1195
–1205
. 10.1109/TMECH.2013.227693510.
Nam
, K.
, Fujimoto
, H.
, and Hori
, Y.
, 2014
, “Advanced Motion Control of Electric Vehicles Based on Robust Lateral Tire Force Control via Active Front Steering
,” IEEE/ASME Trans. Mechatron.
, 19
(1
), pp. 289
–299
. 10.1109/TMECH.2012.223321011.
Li
, B.
, Cheng
, H.
, Chen
, H.
, and Jin
, T.
, 2014
, “Modeling Complex Robotic Assembly Process Using Gaussian Process Regression
,” 2014 IEEE 9th Conference on Industrial Electronics and Applications (ICIEA)
, Hangzhou, China
, June 9–11
, IEEE
, pp. 456
–461
.12.
Östring
, M.
, 2002
, “Identification, Diagnosis, and Control of a Flexible Robot Arm
,” Division of Automatic Control, Department of Electrical Engineering, Linköpings universitet
, Linköping, Sweden
.13.
Marvel
, J.
, and Newman
, W.
, 2009
, “Accelerating Robotic Assembly Parameter Optimization Through the Generation of Internal Models
,” IEEE International Conference on Technologies for Practical Robot Applications (TePRA)
, Woburn, MA
, Nov. 9–10
, pp. 42
–47
.14.
Marvel
, J. A.
, and Newman
, W. S.
, 2011
, “Model-Assisted Stochastic Learning for Robotic Applications
,” IEEE Trans. Autom. Sci. Eng.
, 8
(4
), pp. 835
–845
. 10.1109/TASE.2011.215970815.
Marvel
, J. A.
, Newman
, W. S.
, Gravel
, D. P.
, Zhang
, G.
, Wang
, J.
, and Fuhlbrigge
, T.
, 2009
, “Automated Learning for Parameter Optimization of Robotic Assembly Tasks Utilizing Genetic Algorithms
,” IEEE International Conference on Robotics and Biomimetics
, Bangkok, Thailand
, Feb. 22–25
, pp. 179
–184
.16.
Gravel
, D.
, Zhang
, G.
, Bell
, A.
, and Zhang
, B.
, 2009
, “Objective Metric Study for DOE-Based Parameter Optimization in Robotic Torque Converter Assembly
,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
, St. Louis, MO
, Oct. 10–15
, pp. 3832
–3837
.17.
Zhang
, B.
, Gravel
, D.
, Wang
, J.
, and Bell
, A.
, 2011
, “Robotic Force Control Assembly Parameter Optimization for Adaptive Production
,” 2011 IEEE International Conference on Robotics and Automation (ICRA)
, Shanghai, China
, May 9–13
, pp. 464
–469
.18.
Takahashi
, J.
, Fukukawa
, T.
, and Fukuda
, T.
, 2016
, “Passive Alignment Principle for Robotic Assembly Between a Ring and a Shaft With Extremely Narrow Clearance
,” IEEE/ASME Trans. Mechatron.
, 21
(1
), pp. 196
–204
. 10.1109/TMECH.2015.244863919.
Whitney
, D. E.
, 1982
, “Quasi-Static Assembly of Compliantly Supported Rigid Parts
,” J. Dyn. Syst., Meas., Control
, 104
(1
), pp. 65
–77
. 10.1115/1.314963420.
Thomas
, U.
, Molkenstruck
, S.
, Iser
, R.
, and Wahl
, F. M.
, 2007
, “Multi Sensor Fusion in Robot Assembly Using Particle Filters
,” 2007 IEEE International Conference on Robotics and Automation
, Roma, Italy
, Apr. 10–14
, IEEE
, pp. 3837
–3843
.21.
Jasim
, I. F.
, Plapper
, P. W.
, and Voos
, H.
, 2014
, “Position Identification in Force-Guided Robotic Peg-in-Hole Assembly Tasks
,” Procedia Cirp
, 23
(1
), pp. 217
–222
. 10.1016/j.procir.2014.10.07722.
Vapnik
, V.
, Golowich
, S. E.
, and Smola
, A.
, 1997
, “Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing
,” Adv. Neural Inf. Process. Syst.
, 9
(1
), pp. 281
–287
.23.
Salcedo-Sanz
, S.
, Ortiz-Garcıa
, E. G.
, Pérez-Bellido
, Á. M.
, Portilla-Figueras
, A.
, and Prieto
, L.
, 2011
, “Short Term Wind Speed Prediction Based on Evolutionary Support Vector Regression Algorithms
,” Expert Syst. Appl.
, 38
(4
), pp. 4052
–4057
. 10.1016/j.eswa.2010.09.06724.
Wang
, F.-K.
, and Du
, T.
, 2014
, “Implementing Support Vector Regression With Differential Evolution to Forecast Motherboard Shipments
,” Expert Syst. Appl.
, 41
(8
), pp. 3850
–3855
. 10.1016/j.eswa.2013.12.02225.
Yu
, P.-S.
, Chen
, S.-T.
, and Chang
, I.-F.
, 2006
, “Support Vector Regression for Real-Time Flood Stage Forecasting
,” J. Hydrol.
, 328
(3
), pp. 704
–716
. 10.1016/j.jhydrol.2006.01.02126.
Abtahi
, M.
, Pendar
, H.
, Alasty
, A.
, and Vossoughi
, G. R.
, 2009
, “Calibration of Parallel Kinematic Machine Tools Using Mobility Constraint on the Tool Center Point
,” Int. J. Adv. Manuf. Technol.
, 45
(5–6
), pp. 531
–539
. 10.1007/s00170-009-1994-y27.
Chen
, H.
, Zhang
, B.
, Fuhlbrigge
, T.
, and Zhang
, G.
, 2017
, “Balancing Different Performance Indices in Complex Robotic Assembly Processes
,” 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)
, Macau, China
, Dec. 5–8
, pp. 1708
–1712
.28.
Chen
, L.
, Zhou
, M.
, Wu
, M.
, She
, J.
, Liu
, Z.
, Dong
, F.
, and Hirota
, K.
, 2018
, “Three-Layer Weighted Fuzzy Support Vector Regression for Emotional Intention Understanding in Human-Robot Interaction
,” IEEE Trans. Fuzzy Syst.
, 26
(5
), pp. 2524
–2538
.29.
Purwins
, H.
, Barak
, B.
, Nagi
, A.
, Engel
, R.
, Hockele
, U.
, Kyek
, A.
, Cherla
, S.
, Lenz
, B.
, Pfeifer
, G.
, and Weinzierl
, K.
, 2014
, “Regression Methods for Virtual Metrology of Layer Thickness in Chemical Vapor Deposition
,” IEEE/ASME Trans. Mechatron.
, 19
(1
), pp. 1
–8
. 10.1109/TMECH.2013.227343530.
Hua
, S.
, and Sun
, Z.
, 2001
, “Support Vector Machine Approach for Protein Subcellular Localization Prediction
,” Bioinformatics
, 17
(8
), pp. 721
–728
. 10.1093/bioinformatics/17.8.72131.
Li
, B.
, Chen
, H.
, and Jin
, T.
, 2014
, “Industrial Robotic Assembly Process Modeling Using Support Vector Regression
,” 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014)
, Chicago, IL
, Sept. 14–18
, IEEE
, pp. 4334
–4339
.32.
Thornton
, C.
, Hutter
, F.
, Hoos
, H. H.
, and Leyton-Brown
, K.
, 2013
, “Auto-Weka: Combined Selection and Hyperparameter Optimization of Classification Algorithms
,” Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, Chicago, IL
, Aug. 11–14
, ACM
, pp. 847
–855
.33.
Bergstra
, J.
, Yamins
, D.
, and Cox
, D. D.
, 2013
, “Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures
,” Proceedings of the 30th International Conference on Machine Learning, JMLR
, Atlanta, GA
, June 16–21
.34.
Schölkopf
, B.
, Burges
, C. J.
, and Smola
, A. J.
, 1999
, Advances in Kernel Methods: Support Vector Learning
, MIT Press
, Cambridge, MA
.35.
Pontes
, F. J.
, Amorim
, G.
, Balestrassi
, P. P.
, Paiva
, A.
, and Ferreira
, J. R.
, 2016
, “Design of Experiments and Focused Grid Search for Neural Network Parameter Optimization
,” Neurocomputing
, 186
(1
), pp. 22
–34
. 10.1016/j.neucom.2015.12.06136.
Bergstra
, J.
, and Bengio
, Y.
, 2012
, “Random Search for Hyper-Parameter Optimization
,” J. Mach. Learn. Res.
, 13
(1
), pp. 281
–305
.37.
Mattera
, D.
, and Haykin
, S.
, 1999
, Advances in Kernel Methods
, MIT Press
, Cambridge, MA
, pp. 211
–241
.38.
Cherkassky
, V.
, and Ma
, Y.
, 2004
, “Practical Selection of SVM Parameters and Noise Estimation for SVM Regression
,” Neural Networks
, 17
(1
), pp. 113
–126
. 10.1016/S0893-6080(03)00169-239.
March
, J. G.
, 1991
, “Exploration and Exploitation in Organizational Learning
,” Organ. Sci.
, 2
(1
), pp. 71
–87
. 10.1287/orsc.2.1.7140.
Wang
, L.
, Chen
, X.
, Zhao
, Z.
, and Zhang
, H.
, 2011
, “Exploration vs Exploitation for Distributed Channel Access in Cognitive Radio Networks: A Multi-User Case Study
,” 11th International Symposium on Communications and Information Technologies (ISCIT)
, pp. 360
–365
.41.
Lai
, L.
, El-Gamal
, H.
, Jiang
, H.
, and Poor
, H.
, 2011
, “Cognitive Medium Access: Exploration, Exploitation, and Competition
,” IEEE Trans. Mobile Comput.
, 10
(2
), pp. 239
–253
. 10.1109/TMC.2010.65Copyright © 2019 by ASME
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