Abstract
An intelligent system for spatial visual feedback is presented, which enables the robot's autonomy for a range of robotic assembly tasks, in particular for arc welding, in an unstructured and “fixtureless” environment. The robot's autonomy is empowered by an embedded inductive inference-based machine learning module which learns a welded object's structural properties in the form of geometrical properties. In particular, the system tries to recognize line segments, using a spatial (three-dimensional) visual sensor in order to autonomously execute the objective task. The innovative result is that the recognition of the geometric primitives is done without a predefined Computer-Aided Design (CAD) model, significantly improving the system's autonomy and robustness. The system is validated on real-world welding tasks.
References
1.
Pires
, J. N.
, Loureiro
, A.
, and Bölmsjo
, G.
, 2006
, Welding Robots; Technology, System Issues and Applications
, Springer-Verlag
, London
.2.
Lanzetta
, M.
, Santochi
, M.
, and Tantussi
, G.
, 2001
, “On-Line Control of Robotized Gas Metal Arc Welding
,” CIRP Ann. Manuf. Technol.
, 50
(1
), pp. 13
–16
. 3.
Zou
, Y.
, Zhu
, M.
, and Chen
, X.
, 2021
, “A Robust Detector for Automated Welding Seam Tracking System
,” ASME J. Dyn. Syst. Meas. Control
, 143
(7
), p. 071001
. 4.
Wang
, Z.
, and Xu
, Y.
, 2020
, “Vision-Based Seam Tracking in Robotic Welding: A Review of Recent Research,” Transactions on Intelligent Welding Manufacturing. Transactions on Intelligent Welding Manufacturing
, S.
Chen
, Y.
Zhang
, and Z.
Feng
, eds., Springer
, Singapore
.5.
Naso
, D.
, Turchiano
, B.
, and Pantaleo
, P.
, 2005
, “A Fuzzy-Logic Based Optical Sensor for Online Weld Defect-Detection
,” IEEE Trans. Ind. Inform.
, 1
(4
), pp. 259
–273
. 6.
You
, D.
, Gao
, X.
, and Katayama
, K.
, 2014
, “Multisensor Fusion System for Monitoring High-Power Disk Laser Welding Using Support Vector Machine
,” IEEE Trans. Ind. Inform.
, 10
(2
), pp. 1285
–2955
. 7.
Jaeschke
, B.
, 2015
, “Seam Tracking With Arc Sensors Using Welding Power Sources for Gas Metal Arc Welding (GMAW) With Consumable Electrodes
,” Technical Documentation No.: Y00.0110.1-00, Lorch Schweißtechnik GmbH, Auenwald, Germany.8.
Jovane
, F.
, Koren
, Y.
, and Boër
, C. R.
, 2003
, “Present and Future of Flexible Automation: Towards New Paradigms
,” CIRP Ann. Manuf. Technol.
, 52
(2
), pp. 543
–560
. 9.
Westkämper
, E.
, 2014
, Towards the Re-Industrialization of Europe—A Concept for Manufacturing for 2030
, Springer Verlag
, Berlin/Heidelberg
. 10.
Schraft
, R. D.
, and Meyer
, C.
, 2006
, “The Need for an Intuitive Teaching Method for Small and Medium Enterprises
,” Proceedings of the Joint Conference on Robotics ISR 2006—ROBOTIK
, Munich, Germany
, May 15–17
, p. 10
, (CD-ROM), Abstract p. 95, (VDI-Berichte 1956).11.
Takata
, S.
, and Hirano
, T.
, 2011
, “Human and Robot Allocation Method for Hybrid Assembly Systems
,” CIRP Ann. Manuf. Technol.
, 60
(1
), pp. 9
–12
. 12.
Perzylo
, A.
, et al, 2019
, “SMErobotics: Smart Robots for Flexible Manufacturing
,” IEEE Robot. Autom. Mag.
, 26
(1
), pp. 78
–90
. 13.
European Commission
, n.d.
, The European Robotics Initiative for Strengthening the Competitiveness of SMEs in Manufacturing by Integrating Aspects of Cognitive Systems. https://cordis.europa.eu/project/id/28778714.
Ott
, C.
, 2008
, Cartesian Impedance Control of Redundant and Flexible-Joint Robots
, Springer Verlag
, Berling/Heidelberg
.15.
Albu-Schäffer
, A.
, Ott
, C.
, and Hirzinger
, G.
, 2007
, “A Unified Passivity-Based Control Framework for Position, Torque and Impedance Control of Flexible Joint Robots
,” Int. J. Robot. Res.
, 26
(1
), pp. 23
–39
. 16.
Angluin
, D.
, 1982
, “Inference of Reversible Languages
,” J. ACM
, 29
(3
), pp. 741
–765
. 17.
Hopcroft
, J. E.
, and Ullman
, J. D.
, 1979
, Introduction to Automata Theory, Languages and Computation
, Addison-Wesley
, Reading, MA
.18.
Aichernig
, B. K.
, and Tappler
, M.
, 2018
, “Efficient Active Automata Learning via Mutation Testing
,” J. Autom. Reason.
, 63
(4
), pp. 1103
–1134
. 19.
Krichen
, M.
, 2019
, “Improving Formal Verification and Testing Techniques for Internet of Things and Smart Cities
,” Mobile Netw. Appl.
, 28
(2
), pp. 732
–743
. 20.
Vongbunyong
, S.
, Kara
, S.
, and Pagnucco
, M.
, 2013
, “Application of Cognitive Robotics in Disassembly of Products
,” CIRP Ann. Manuf. Technol.
, 62
(1
), pp. 31
–34
. 21.
Shapovalov
, R.
, Vetrov
, D.
, and Kohli
, P.
, 2013
, “Spatial Inference Machines
,” Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
, Portland, OR
, June 23–28
, IEEE Computer Society 2013, pp. 2985
–2992
.22.
Rodger
, R. S.
, and Rosebrugh
, R. D.
, 1979
, “Computing a Grammar for Sequences of Behavioural Acts
,” Anim. Behav.
, 27
(3
), pp. 737
–749
. 23.
Richetin
, M.
, and Vernadat
, F.
, 1984
, “Efficient Regular Grammatical Inference for Pattern Recognition
,” Pattern Recognit.
, 17
(2
), pp. 245
–250
. 24.
Miclet
, L.
, 1987
, “Grammatical Inference,” Syntactic and Structural Pattern Recognition, Theory and Applications
, H.
Bunke
, and A.
Sanfeliu
, eds., World Scientific
, Singapore
. Series in Computer Science—Vol. 7.25.
Shah
, V.
, and Putnik
, G. D.
, 2016
, “Software Tools for Understanding Grammatical Inference Algorithms: Part I—Tools for Regular Grammars and Finite-State Automata
,” FME Trans. Fac. Mech. Eng. Belgr.
, 44
(1
), pp. 83
–91
. 26.
Danilov
, I.
, Petrovic
, P. B.
, Korac
, F.
, and Lukic
, N.
, 2015
, “Stratified Visual 3D Feedback for Adaptive Robotic Arc-Welding
,” Proceedings of 2nd International Conference on Electrical, Electronic and Computing Engineering, IcETRAN
, Silver Lake, Serbia
, June 8–11
, p. ROI4.3 1–6
.27.
Gold
, E. M.
, 1967
, “Language Identification in the Limit
,” Inf. Control
, 10
(5
), pp. 447
–474
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