nach oben

Erschienen in:

2021 | OriginalPaper | Buchkapitel

8. Dynamic Assembly Planning and Task Assignment

verfasst von : Konstantinos Sipsas, Nikolaos Nikolakis, Sotiris Makris

Erschienen in: Advanced Human-Robot Collaboration in Manufacturing

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Human–robot collaboration comes as an evolution to manual production. An increasing number of robots is gradually incorporated in today’s production lines. The introduction of automation systems may complement the human factor and skills, and alleviate operators’ daily workload. The production process can be executed in collaboration between humans and robotic manipulators achieving improved production efficiency as well as reduced injuries, caused by cumulative stress. Nevertheless, and beyond safety issues that arise from human–robot collaboration, a major challenge remains in how to efficiently break down a manufacturing process to allow for collaborative execution. Different capabilities and constraints need to be considered both in planning the assembly of a product, and its execution. Changes in the operational status of either robots or operators may require modifications to planning as well as the execution steps of a manufacturing process. Such changes supporting human–robot teamwork should be considered and dynamically addressed to maintain efficiency and smooth human–robot collaboration. In this context, this chapter discusses the steps towards semi-automated feature-based assembly planning. A set of evaluation criteria is used to evaluate alternative assembly sequences generated from a product CAD model, which are then mapped onto tasks. The task sequence can then be assigned to production site manufacturing resources for execution. A multi-criteria decision-making model is used for mapping assembly sequences to tasks, while a hierarchical workload model supporting a common level of breakdown is responsible for task assignment to operators, robots or both. A lower level of the model is used for the robot to map a generic description onto commands by the executable robot controller. IEC 61499 function blocks are used as a common data model. The scheduled activities are wrapped into function blocks and dispatched for execution. Runtime events are evaluated and determined if and how to modify the existing plan and/or schedule. As a result, dynamic planning and scheduling are facilitated concerning human–robot collaborative manufacturing. The proposed methods have been implemented in software prototypes to validate their feasibility. Finally, this chapter concludes with challenges identified as well as future research directions.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Resource Availability and Capability Monitoring

Nächstes Kapitel Cockpit: A Portal for Symbiotic Human–Robot Collaborative Assembly

P. Scranton, Diversity in Diversity: Flexible Production and American Industrialization, 1880–1930. Bus. Hist. Rev. 65(1), 27–90 (Apr. 1991). https://doi.org/10.2307/3116904CrossRef

S. J. Winter and S. Lynne Taylor, The Role of IT in the Transformation of Work: A Comparison of Post-industrial, Industrial, and Proto-industrial Organization, Information Systems Research, vol. 7, no. 1. INFORMS Inst. for Operations Res. and the Management Sciences, pp. 5–21, 1996, doi: https://doi.org/10.1287/isre.7.1.5.

T. B. Sheridan, Human–Robot Interaction, Human Factors, vol. 58, no. 4. SAGE Publications Inc., pp. 525–532, 2016, doi: https://doi.org/10.1177/0018720816644364.

F. Demoly, X.T. Yan, B. Eynard, L. Rivest, S. Gomes, An assembly oriented design framework for product structure engineering and assembly sequence planning. Robot. Comput. Integr. Manuf. 27(1), 33–46 (Feb. 2011). https://doi.org/10.1016/j.rcim.2010.05.010CrossRef

X. Zhu, S.J. Hu, Y. Koren, N. Huang, A complexity model for sequence planning in mixed-model assembly lines. J. Manuf. Syst. 31(2), 121–130 (Apr. 2012). https://doi.org/10.1016/j.jmsy.2011.07.006CrossRef

E. Gruhier, F. Demoly, O. Dutartre, S. Abboudi, S. Gomes, A formal ontology-based spatiotemporal mereotopology for integrated product design and assembly sequence planning. Adv. Eng. Informatics 29(3), 495–512 (Aug. 2015). https://doi.org/10.1016/j.aei.2015.04.004CrossRef

W. Hui, X. Dong, D. Guanghong, Z. Linxuan, Assembly planning based on semantic modeling approach. Comput. Ind. 58(3), 227–239 (2007). https://doi.org/10.1016/j.compind.2006.05.002CrossRef

H. Lu, H. Zhen, W. Mi, Y. Huang, A physically based approach with human–machine cooperation concept to generate assembly sequences. Comput. Ind. Eng. 89, 213–225 (Nov. 2015). https://doi.org/10.1016/j.cie.2015.04.032CrossRef

M. Wu, V. Prabhu, X. Li, Knowledge-based approach to assembly sequence planning. J. Algorithms Comput. Technol. 5(1), 57–70 (Mar. 2011). https://doi.org/10.1260/1748-3018.5.1.57CrossRef

10.

M. Givehchi, A. Haghighi, L. Wang, Generic machining process sequencing through a revised enriched machining feature concept. J. Manuf. Syst. 37, 564–575 (Oct. 2015). https://doi.org/10.1016/j.jmsy.2015.04.004CrossRef

11.

J. Zhang, Z. Xu, Y. Li, S. Jiang, Framework for the integration of assembly modeling and simulation based on assembly feature pair. Int. J. Adv. Manuf. Technol. 78(5–8), 765–780 (May 2015). https://doi.org/10.1007/s00170-014-6671-0CrossRef

12.

G. Chryssolouris, Manufacturing Systems: Theory and Practice, 2nd edn. (Springer-Verlag, New York, 2006).

13.

L. Wang, M. Givehchi, B. Schmidt, G. Adamson, Robotic assembly planning and control with enhanced adaptability. Procedia CIRP 3(1), 173–178 (2012). https://doi.org/10.1016/j.procir.2012.07.031CrossRef

14.

S. J. Hu, J. Ko, L. Weyand, H.A. ElMaraghy, T.K. Lien, Y. Koren, H. Bley, G. Chryssolouris, N. Nasr, M. Shpitalni, Assembly system design and operations for product variety, CIRP Ann.—Manuf. Technol., vol. 60, no. 2, pp. 715–733, 2011, doi: https://doi.org/10.1016/j.cirp.2011.05.004.

15.

L. Wang, S. Keshavarzmanesh, H.Y. Feng, A function block based approach for increasing adaptability of assembly planning and control. Int. J. Prod. Res. 49(16), 4903–4924 (Aug. 2011). https://doi.org/10.1080/00207543.2010.501827CrossRef

16.

W. van Holland and W. F. Bronsvoort, Assembly features and sequence planning, 1997, pp. 275–284.

17.

M.C.Leu, H.A. ElMaraghy, A.Y.C. Nee, S.K. Ong, M. Lanzetta, M. Putz, W. Zhu, A. Bernard, CAD model based virtual assembly simulation, planning and training, CIRP Ann.—Manuf. Technol., vol. 62, no. 2, pp. 799–822, 2013, doi: https://doi.org/10.1016/j.cirp.2013.05.005.

18.

Q. Yang, D.L. Wu, H.M. Zhu, J.S. Bao, Z.H. Wei, Assembly operation process planning by mapping a virtual assembly simulation to real operation. Comput. Ind. 64(7), 869–879 (Sep. 2013). https://doi.org/10.1016/j.compind.2013.06.001CrossRef

19.

S. Angerer, C. Strassmair, M. Staehr, M. Roettenbacher, and N. M. Robertson, Give me a hand—The potential of mobile assistive robots in automotive logistics and assembly applications, in 2012 IEEE Conference on Technologies for Practical Robot Applications, TePRA 2012, 2012, pp. 111–116, doi: https://doi.org/10.1109/TePRA.2012.6215663.

20.

J. Krüger, T. K. Lien, and A. Verl, Cooperation of human and machines in assembly lines, CIRP Ann.—Manuf. Technol., vol. 58, no. 2, pp. 628–646, Jan. 2009, doi: https://doi.org/10.1016/j.cirp.2009.09.009.

21.

H. Ding, M. Schipper, B. Matthias, Optimized task distribution for industrial assembly in mixed human–robot environments—Case study on IO module assembly, in IEEE International Conference on Automation Science and Engineering, 2014, vol. 2014 January, pp. 19–24, doi: https://doi.org/10.1109/CoASE.2014.6899298.

22.

M.A. Goodrich, D. Yi, Toward Task-Based Mental Models of Human–Robot Teaming: A Bayesian Approach (Springer, Berlin, Heidelberg, 2013), pp. 267–276

23.

L. Wang, R. X. Gao, J. Váncza, J. Krüger, X. V. Wang, S. Makris and G. Chryssolouris, Symbiotic Human–Robot Collaborative Assembly, CIRP Annals—Manufacturing Technology, Vol.68, No.2, pp. 701–726, 2019.

Titel: Dynamic Assembly Planning and Task Assignment
verfasst von: Konstantinos Sipsas
Nikolaos Nikolakis
Sotiris Makris
Verlag: Springer International Publishing
Buch: Advanced Human-Robot Collaboration in Manufacturing
Print ISBN: 978-3-030-69177-6

Electronic ISBN: 978-3-030-69178-3

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-69178-3_8

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht