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Optimization-based estimation of the execution time of a robotic assembly task sequence

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Abstract

Estimating the execution time of assembly task sequences of a new geometric model is an essential prerequisite for task allocation and resource planning decisions in smart multi-robotic assembly cells. This paper discusses an optimization-based method to estimate the execution time of a full sequence of assembly tasks, including picking, aligning, and insertion. Task descriptions, joint space trajectories, assembly task-related motions, and process conditions are used as inputs for the method. The required descriptions of assembly tasks are parametrized, and the resulting function is then combined with the process conditions, assembly task-relevant motions, and the capabilities of the resources in the robotic assembly cell to estimate the execution times. The advantage of the method is the possibility of determining the execution time of an assembly task, taking into account the capabilities of the robot, before assigning it to the multi-robot assembly station. The proposed method is demonstrated experimentally by using a CAD model with the information of Fanuc robot CR7iA/L. The method is implemented in MATLAB using AMPL-API and tested. To determine its effectiveness, the outcomes of the proposed method are compared to the values of the 3-D Fanuc Roboguide (RG). Finally, further research steps to improve the accuracy of the total time for the entire assembly sequence are also discussed.

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References

  1. Ranz F, Hummel V, Sihn W (2017) Capability-based task allocation in human-robot collaboration. Procedia Manufac 9:182–189

    Article  Google Scholar 

  2. Khan AS, Khan R, Saleem W, Salah B, Alkhatib S (2022) Modeling and optimization of assembly line balancing type 2 and E (SLBP-2E) for a reconfigurable manufacturing system. Process 10(8):1582

    Article  Google Scholar 

  3. Raatz A, Blankemeyer S, Recker T, Pischke D, Nyhuis P (2020) Task scheduling method for HRC workplaces based on capabilities and execution time assumptions for robots. CIRP Ann 69(1):13–16

    Article  Google Scholar 

  4. Lamon E, De Franco A, Peternel L, Ajoudani A (2019) A capability-aware role allocation approach to industrial assembly tasks. IEEE Robot Autom Lett 4(4):3378–3385

    Article  Google Scholar 

  5. Peavey EK, Zoss J, Watkins N (2012) Simulation and mock-up research methods to enhance design decision making. Health Environ Res Des J 5(3):133–144

    Google Scholar 

  6. Hold P, Sihn W (2016) Towards a model to identify the need and the economic efficiency of digital assistance systems in cyber-physical assembly systems. IEEE/ Int Work Cyber-Phys Prod Syst 1–4

  7. Faber M, Mertens A, Schlick CM (2017) Cognition-enhanced assembly sequence planning for ergonomic and productive human-robot collaboration in self-optimizing assembly cells. Prod Eng 11(2):145–154

    Article  Google Scholar 

  8. Freitag M, Hildebrandt T (2016) Automatic design of scheduling rules for complex manufacturing systems by multi-objective simulation-based optimization. CIRP Ann 65(1):433–436

    Article  Google Scholar 

  9. Kaelbling LP, Lozano-Pérez T (2011) Hierarchical task and motion planning in the now. IEEE Int Conf Robot Autom (1470–1477)

  10. Srivastava S, Fang E, Riano L, Chitnis R, Russell S, Abbeel P (2014) Combined task and motion planning through an extensible planner-independent interface layer. IEEE Int Conf Robot Autom (639–646)

  11. De Silva L, Lallement R, Alami R, (2015) The HATP hierarchical planner: formalisation and an initial study of its usability and practicality. IEEE/RSJ Int Conf Intell Robot Syst (6465–6472)

  12. Chen H, Li J, Wan W, Huang Z, Harada K (2020) Integrating combined task and motion planning with compliant control: successfully conducting planned dual-arm assembly motion using compliant peg-in-hole control. Int J Intell Robot Appl 4:149–163

    Article  Google Scholar 

  13. Fukuda K, Ramirez-Alpizar I.G, Yamanobe N, Petit D, Nagata K, Harada K (2019) Recognition of assembly tasks based on the actions associated to the manipulated objects. IEEE/SICE Int Symp Syst Integr (SII) (193–198)

  14. Müller R, Scholer M, Karkowski M (2019) Generic automation task description for flexible assembly systems. Procedia CIRP 81:730–735

    Article  Google Scholar 

  15. Naumann M, Bengel M, Verl A, (2010) Automatic generation of robot applications using a knowledge integration framework. Int Symp Robot ROBOTIK (1–8)

  16. Malakuti S, Bock J, Weser M, Venet P, Zimmermann P, Wiegand M, Grothoff J, Wagner C, Bayha A (2018) Challenges in skill-based engineering of industrial automation systems. IEEE 23rd Int Conf Emerg Technol Fact Autom (ETFA) (1:67–74)

  17. Jacobsson L, Malec J, Nilsson K (2016) Modularization of skill ontologies for industrial robots. In: Proceedings of international symposium on robotics (pp 1–6)

  18. Perzylo A, Somani N, Rickert M, Knoll A (2015) An ontology for CAD data and geometric constraints as a link between product models and semantic robot task descriptions. IEEE/rsj Int Conf Intell Robot Syst (iros) (pp 4197–4203)

  19. Saeed M, Demasure T, Hoedt S, Aghezzaf E.H, Cottyn J Spline-based trajectory generation to estimate execution time in a robotic assembly cell. Int J Adv Manuf Technol 121(9-10):6921–6935

  20. Järvenpää E (2012) Capability-based adaptation of production systems in a changing environment. Tampere Univ Technol

  21. Bayha A, Bock J, Boss B, Diedrich C, Malakuti S (2020) Describing capabilities of Industrie 4.0 components: joint white paper between plattform Industrie 4.0, VDI GMA 7.20, BaSys 4.2

  22. Keddis N, Kainz G, Zoitl A (2014) Capability-based planning and scheduling for adaptable manufacturing systems. IEEE Emerg Technol Fact Autom (pp 1–8)

  23. Järvenpää E, Torvinen S (2013) Capability-based approach for evaluating the impact of product requirement changes on the production system. Flex Autom Intell Manuf (173–185)

  24. Björkelund A, Bruyninckx H, Malec J, Nilsson K, Nugues P (2012) Knowledge for intelligent industrial robots. Design Intell Robot (12:02)

  25. Schou C, Andersen RS, Chrysostomou D, Bøgh S, Madsen O (2018) Skill-based instruction of collaborative robots in industrial settings. Robot Comput-Integr Manuf 53:72–80

    Article  Google Scholar 

  26. Bänziger T, Kunz A, Wegener K (2020) Optimizing human-robot task allocation using a simulation tool based on standardized work descriptions. J Intell Manuf 1635–1648

  27. Cutting-Decelle AF, Young RI, Michel JJ, Grangel R, Le Cardinal J, Bourey JP (2007) A product-process-resource based approach for managing modularity in production management. Concurr Eng 15(2):217–235

    Article  Google Scholar 

  28. Michalos G, Makris S, Chryssolouris G (2015) The new assembly system paradigm. Int J Comput Integr Manuf 28(12):1252–1261

    Article  Google Scholar 

  29. Nägele F, Halt L, Tenbrock P, Pott A (2018) A prototype-based skill model for specifying robotic assembly tasks. IEEE Int Conf Robot Autom (ICRA) (pp 558–565)

  30. Thomas U, Hirzinger G, Rumpe B, Schulze C, Wortmann A (2013) A new skill based robot programming language using UML/P Statecharts. IEEE Int Conf Robot Autom (461–466)

  31. Faccio M, Bottin M, Rosati G (2019) Collaborative and traditional robotic assembly: a comparison model. Int J Adv Manuf Technol 102:1355–1372

    Article  Google Scholar 

  32. Schröter D, Kuhlang P, Finsterbusch T, Kuhrke B, Verl A (2016) Introducing process building blocks for designing human robot interaction work systems and calculating accurate cycle times. Procedia CIRP 44:216–221

  33. WeSSkamp V, Seckelmann T, Barthelmey A, Kaiser M, Lemmerz K, Glogowski P, Kuhlenköter B Deuse J (2019) Development of a sociotechnical planning system for human-robot interaction in assembly systems focusing on small and medium-sized enterprises. Procedia CIRP 81:1284–1289

  34. Michalos G, Spiliotopoulos J, Makris S, Chryssolouris G (2018) A method for planning human robot shared tasks. J Manuf Sci Technol 22:76–90

    Article  Google Scholar 

  35. Gonnermann C, Weth J, Reinhart G (2020) Skill modeling in cyber-physical production systems for process monitoring. Procedia CIRP 93:1376–1381

    Article  Google Scholar 

  36. Backhaus J, Reinhart G (2017) Digital description of products, processes and resources for task-oriented programming of assembly systems. Journal of Intelligent Manufacturing 28:1787–1800

    Article  Google Scholar 

  37. Lemmerz K, Glogowski P, Hypki A, Kuhlenkoetter B (2018) Functional integration of a robotics software framework into a human simulation system. Int Symp Robot (1–8)

  38. Choi CK, Ip WH (1999) A comparison of MTM and RTM. Work Study 48(2):57–61

    Article  Google Scholar 

  39. Glogowski P, Lemmerz K, Schulte L, Barthelmey A, Hypki A, Kuhlenkötter B, Deuse J (2017) Task-based simulation tool for human-robot collaboration within assembly systems. In: Tagungsband des 2. Kongresses montage handhabung industrieroboter (155–163)

  40. Bokranz R, Landau K (2012) Handbuch industrial engineering. Produktivitätsmanagement mit MTM 2:2

    Google Scholar 

  41. Komenda T, Ranz F, Sihn W (2019) Influence of task allocation patterns on safety and productivity in human-robot-collaboration. Ind Simul Conf 85–89

  42. Pellegrinelli S, Pedrocchi N (2018) Estimation of robot execution time for close proximity human-robot collaboration. Integr Comput-Aided Eng 25(1):81–96

    Article  Google Scholar 

  43. Kuhlang P (2015). Modellierung menschlicher arbeit im industrial engineering: grundlagen, praxiserfahrungen und perspektiven;[mit der Neuentwicklung MTM-HWD]. Ergonomia

  44. Mateus JC, Claeys D, Limère V, Cottyn J, Aghezzaf EH (2020) Base part centered assembly task precedence generation. The International Journal of Advanced Manufacturing Technology 107:607–616

    Article  Google Scholar 

  45. Komenda T, Brandstötter M, Schlund S (2021) A comparison of and critical review on cycle time estimation methods for human-robot work systems. Procedia CIRP 104:1119–1124

    Article  Google Scholar 

  46. Kuhlang P, Erohin O, Krebs M, Deuse J, Sihn W (2014) Morphology of time data management-systematic design of time data management processes as fundamental challenge in industrial engineering. Int J Ind Syst Eng 16(4):415–432

    Google Scholar 

  47. Faccio M, Bottin M, Rosati G (2019) Collaborative and traditional robotic assembly: a comparison model. Int J Adv Manuf Technol 102:1355–1372

    Article  Google Scholar 

  48. Cherubini A, Passama R, Crosnier A, Lasnier A, Fraisse P (2016) Collaborative manufacturing with physical human-robot interaction. Robot Comput-Integr Manuf 40:1–13

    Article  Google Scholar 

  49. Salunkhe O, Stahre J, Romero D, Li D, Johansson B (2023) Specifying task allocation in automotive wire harness assembly stations for human-robot collaboration. Comput Ind Eng 184

  50. Köcher A, Da Silva, and Fay A (2022) Modeling and executing production processes with capabilities and skills using ontologies and BPMN. IEEE 27th international conference on emerging technologies and factory automation (ETFA) (pp 1–8)

  51. Chen H, Cheng H (2021) Online performance optimization for complex robotic assembly processes. J Manuf Process 72:544–552

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Funding

This work is supported by the Higher Education Commission of Pakistan within the framework of HRDI-UESTP Scholarship Project.

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Authors and Affiliations

  1. Department of Industrial Systems Engineering and Product Design, Ghent University, Technologiepark 46, Ghent-Zwijnaarde, 9052, Belgium

    Muhammad Saeed, Thibaut Demasure, El-Houssaine Aghezzaf & Johannes Cottyn

  2. Industrial Systems Engineering (ISyE), Flanders Make, vzw, Kortrijk, Flanders, Belgium

    Muhammad Saeed, Thibaut Demasure, El-Houssaine Aghezzaf & Johannes Cottyn

Authors
  1. Muhammad Saeed
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  2. Thibaut Demasure
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  3. El-Houssaine Aghezzaf
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  4. Johannes Cottyn
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Contributions

MS: conceptualization, methodology, software implementation, validation, visualization, writing—original draft. TD: extraction of data from Roboguide software. E-HA: optimization, project administration. JC: supervision, resources, writing—review and editing.

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Correspondence to Muhammad Saeed.

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Saeed, M., Demasure, T., Aghezzaf, EH. et al. Optimization-based estimation of the execution time of a robotic assembly task sequence. Int J Adv Manuf Technol 130, 5315–5328 (2024). https://doi.org/10.1007/s00170-023-12925-6

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  • DOI: https://doi.org/10.1007/s00170-023-12925-6

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