Abstract
Human–robot collaboration is enabled by the digitization of production and has become a key technology for the factory of the future. It combines the strengths of both the human worker and the assistant robot and allows the implementation of an varying degree of automation in workplaces in order to meet the increasing demand of flexibility of manufacturing systems. Intelligent planning and control algorithms are needed for the organization of the work in hybrid teams of humans and robots. This paper introduces an approach to use standardized work description for automated procedure generation of mobile assistant robots. A simulation tool is developed that implements the procedure model and is therefore capable of calculating different objective parameters like production time or ergonomics during a production cycle as a function of the human–robot task allocation. The simulation is validated with an existing workplace in an assembly line at the Volkswagen plant in Wolfsburg, Germany. Furthermore, a new method is presented to optimize the task allocation in human–robot teams for a given workplace, using the simulation as fitness function in a genetic algorithm. The advantage of this new approach is the possibility to evaluate different distributions of the tasks, while considering the dynamics of the interaction between the worker and the robot in their shared workplace. Using the presented approach for a given workplace, an optimized human–robot task allocation is found, in which the tasks are allocated in an intelligent and comprehensible way.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Angerer, S., Strassmair, C., Staehr, M., Roettenbacher, M., & Robertson, N. M. (2012). Give me a hand: The potential of mobile assistive robots in automotive logistics and assembly applications. In Proceedings of the IEEE conference on technologies for practical robot applications (pp. 111–116). https://doi.org/10.1109/TePRA.2012.6215663.
Bänziger, T., Kunz, A., & Wegener, K. (2018). Simulation of the human–robot cooperation in automotive assembly lines based on MTM. https://doi.org/10.6084/m9.figshare.5743314. https://figshare.com/s/35b5a5d311f6930ec3f3. Accessed January 1, 2018.
Bochmann, L., Bänziger, T., Kunz, A., & Wegener, K. (2017). Human–robot collaboration in decentralized manufacturing systems: An approach for simulation-based evaluation of future intelligent production. In CIRP conference on intelligent computation in manufacturing engineering (Vol. 62, pp. 624–629). https://doi.org/10.1016/j.procir.2016.06.021.
Chen, F., Sekiyama, K., Cannella, F., & Fukuda, T. (2014). Optimal subtask allocation for human and robot collaboration within hybrid assembly system. In Proceedings of the IEEE transactions on automation science and engineering (pp. 1065–1075). https://doi.org/10.1109/TASE.2013.2274099.
Chen, F., Sekiyama, K., & Fukuda, T. (2012). A genetic algorithm for subtask allocation within human and robot coordinated assembly. In International symposium on micro-nanomechatronics and human science (MHS) (pp. 507–511). https://doi.org/10.1109/MHS.2012.6492504.
Chen, F., Sekiyama, K., Sasaki, H., Huang, J., Sun, B., & Fukuda, T. (2011). Assembly strategy modeling and selection for human and robot coordinated cell assembly. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 4670–4675). https://doi.org/10.1109/IROS.2011.6094715.
Chen, H. G., & Hector, G. (1992). A rule-based robot scheduling system for flexible manufacturing cells. Journal of Intelligent Manufacturing, 3, 285–296. https://doi.org/10.1007/BF01577270.
Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., & Fraisse, P. (2016). Collaborative manufacturing with physical human–robot interaction. Robotics and Computer-Integrated Manufacturing, 40, 1–13. https://doi.org/10.1016/j.rcim.2015.12.007.
Cui, X., & Shi, H. (2012). An overview of pathfinding innavigation mesh. International Journal of Computer Science and Network Security, 12(12), 48–51.
Dahl, T. S., Matarić, M., & Sukhatme, G. S. (2009). Multi-robot task allocation through vacancy chain scheduling. Robotics and Autonomous Systems, 57(6–7), 674–687. https://doi.org/10.1016/j.robot.2008.12.001.
Das, G. P., McGinnity, T. M., Coleman, S. A., & Behera, L. (2015). A distributed task allocation algorithm for a multi-robot system in healthcare facilities. Journal of Intelligent & Robotic Systems, 80(1), 33–58. https://doi.org/10.1007/s10846-014-0154-2.
de Gea Fernández, J., Mronga, D., Günther, M., Knobloch, T., Wirkus, M., Schröer, M., et al. (2017). Multimodal sensor-based whole-body control for human–robot collaboration in industrial settings. Robotics and Autonomous Systems, 94, 102–119. https://doi.org/10.1016/j.robot.2017.04.007.
de Gea Fernández, J., Mronga, D., Günther, M., Wirkus, M., Schröer, M., Stiene, S., et al. (2017). iMRK: Demonstrator for intelligent and intuitive human-robot collaboration in industrial manufacturing. KI: Künstliche Intelligenz, 31(2), 203–207. https://doi.org/10.1007/s13218-016-0481-5.
Ding, H., Schipper, M., & Matthias, B. (2014). Optimized task distribution for industrial assembly in mixed human-robot environments—case study on IO module assembly. In Proceedings of the IEEE international conference on automation science and engineering (pp. 19–24). https://doi.org/10.1109/CoASE.2014.6899298.
Evans, P., & Annunziata, M. (2012). Industrial internet: Pushing the boundaries of minds and machines. https://www.ge.com/docs/chapters/Industrial_Internet.pdf. Accessed November 26, 2012.
German Organization for Standardization. (2008). DIN EN ISO 13849: Safety of machinery—Safety-related parts of control systems—Part 1: General principles for design.
German Organization for Standardization. (2010). DIN EN ISO 13855: Safety of machinery—Positioning of safeguards with respect to the approach speeds of parts of the human body.
German Organization for Standardization. (2011). DIN EN ISO 12100: Safety of machinery—General principles for design—Risk assessment and risk reduction.
German Organization for Standardization. (2012). DIN EN ISO 10218: Robots and robotic devices—Safety requirements for industrial robots.
Ghosh, B. K., & Helander, M. G. (1986). A systems approach to task allocation of human–robot interaction in manufacturing. Journal of Manufacturing Systems, 5(1), 41–49. https://doi.org/10.1016/0278-6125(86)90066-X.
Haddadin, S., Parusel, S., Belder, R., Albu-Schaffer, A., & Hirzinger, G. (2011). Safe acting and manipulation in human environments: A key concept for robots in our society. In Proceedings of the IEEE workshop on advanced robotics and its social impacts (pp. 72–75). https://doi.org/10.1109/ARSO.2011.6301962.
Hengstebeck, A. (2015). Formal modelling of manual work processes for the application of industrial service robotics. In Proceedings of the CIRP conference on manufacturing systems (pp. 364–369). https://doi.org/10.1016/j.procir.2015.12.013.
International Electrotechnical Commission. (2010). IEC 61508: Functional safety of electrical/electronic/programmable electronic safety-related systems—Part 1: General requirements.
International Organization for Standardization. (2016). ISO TS 15066: Robots and robotic devices—Collaborative robots.
Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research, 5, 90–98. https://doi.org/10.1109/ROBOT.1985.1087247.
Koren, Y. (2010). The global manufacturing revolution: Product–process–business integration and reconfigurable systems. Hoboken, NJ: Wiley.
Krüger, J., Lien, T. K., & Verl, A. (2009). Cooperation of human and machines in assembly lines. CIRP Annals: Manufacturing Technology, 58(2), 628–646. https://doi.org/10.1016/j.cirp.2009.09.009.
Kusiak, A. (2017). Smart manufacturing must embrace big data. Nature, 544, 23–25. https://doi.org/10.1038/544023a.
Loredo-Flores, A., Gonzalez-Galvan, E. J., Cervantes-Sanchez, J. J., & Martinez-Soto, A. (2008). Optimization of industrial, vision-based, intuitively generated robot point-allocating tasks using genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(4), 600–608. https://doi.org/10.1109/TSMCC.2008.923886.
Malvankar-Mehta, M. S., & Mehta, S. S. (2015). Optimal task allocation in multi-human multi-robot interaction. Optimization Letters, 9(8), 1787–1803. https://doi.org/10.1007/s11590-015-0890-7.
Michalos, G., Makris, S., Spiliotopoulos, J., Misios, I., Tsarouchi, P., & Chryssolouris, G. (2014). ROBO-PARTNER: Seamless human-robot cooperation for intelligent, flexible and safe operations in the assembly factories of the future. Procedia CIRP, 23, 71–76. https://doi.org/10.1016/j.procir.2014.10.079.
Morioka, M., & Sakakibara, S. (2010). A new cell production assembly system with human–robot cooperation. CIRP Annals: Manufacturing Technology, 59(1), 9–12. https://doi.org/10.1016/j.cirp.2010.03.044.
Müller, R., Vette, M., & Scholer, M. (2014). Inspector robot—A new collaborative testing system designed for the automotive final assembly line. Procedia CIRP, 23, 59–64. https://doi.org/10.1016/j.procir.2014.10.093.
Nagarajan, T., & Thondiyath, A. (2014). An algorithm for cooperative task allocation in scalable, constrained multiple robot systems. Intelligent Service Robotics, 7(4), 221–233. https://doi.org/10.1007/s11370-014-0154-x.
Nielsen, I., Dang, Q. V., Bocewicz, G., & Banaszak, Z. (2017). A methodology for implementation of mobile robot in adaptive manufacturing environments. Journal of Intelligent Manufacturing, 28(5), 1171–1188. https://doi.org/10.1007/s10845-015-1072-2.
Shen, Y., Reinhart, G., & Tseng, M. M. (2015). A design approach for incorporating task coordination for human-robot-coexistence within assembly systems. In Proceedings of the annual IEEE international systems conference (Vol. 9, pp. 426–431). https://doi.org/10.1109/SYSCON.2015.7116788.
Shi, Z., Wei, J., Wei, X., Tan, K., & Wang, Z. (2010). Task allocation model based on reputation for the heterogeneous multi-robot collaboration system. World Congress on Intelligent Control and Automation (WCICA), 8, 6642–6647. https://doi.org/10.1109/WCICA.2010.5554165.
Stenmark, M., & Malec, J. (2014). Describing constraint-based assembly tasks in unstructured natural language. In Proceedings of the 19th world congress of the international federation of automatic control (Vol. 19, pp. 3056–3061). https://doi.org/10.3182/20140824-6-ZA-1003.02062.
Takata, S., & Hirano, T. (2011). Human and robot allocation method for hybrid assembly systems. CIRP Annals: Manufacturing Technology, 60(1), 9–12. https://doi.org/10.1016/j.cirp.2011.03.128.
Tao, F., & Qi, Q. (2017). New it driven service-oriented smart manufacturing: Framework and characteristics. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 99, 1–11. https://doi.org/10.1109/TSMC.2017.2723764.
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems,. https://doi.org/10.1016/j.jmsy.2018.01.006.
Teiwes, J., Bänziger, T., Kunz, A., & Wegener, K. (2016). Identifying the potential of human–robot collaboration in automotive assembly lines using a standardised work description. IEEE International Conference on Automation and Computing (ICAC), 22, 78–83. https://doi.org/10.1109/IConAC.2016.7604898.
The Industrial Engineer: MTM—Universal Analysis System (UAS). (2016). http://www.der-wirtschaftsingenieur.de/index.php/mtm-universelles-analysier-system/. Accessed January 7, 2016.
Volkswagen, A. G. (2016). Golf production at Wolfsburg plant: People and robots work hand-in-hand. http://www.volkswagen-media-services.com/en/detailpage/-/detail/Golf-production-at-Wolfsburg-plant-people-and-robots-work-hand-in-hand/view/3613525/. Accessed August 1, 2016.
Zacharia, P., & Aspragathos, N. A. (2005). Optimal robot task scheduling based on genetic algorithms. Robotics and Computer-Integrated Manufacturing, 21(1), 67–79. https://doi.org/10.1016/j.rcim.2004.04.003.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Bänziger, T., Kunz, A. & Wegener, K. Optimizing human–robot task allocation using a simulation tool based on standardized work descriptions. J Intell Manuf 31, 1635–1648 (2020). https://doi.org/10.1007/s10845-018-1411-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-018-1411-1