Abstract
The industrial sector today is experiencing its fourth industrial revolution, powered by technological advances in the fields of robotics, artificial intelligence and the Internet of Things (IoT). Customer demands for mass customisation have increased product variation. Moreover, products are manufactured faster and with higher consistency. In this new context, machines alone do not seem agile enough to keep pace with the growing demand for customised products and shorter cycle times. As digital technology is becoming a pervasive feature of production systems, conventional workstations, equipped with modern embedded systems and ICT, are gradually transforming into smart workstations, in which humans and robots may work and carry out their tasks together. In such a smart environment the human factor is expected to be seamlessly supported by automation systems. To achieve this, several subsystems should be integrated and orchestrated, evaluating key collaboration aspects while ensuring human safety and operational efficiency. However, the increased complexity of such a system of systems requires sophisticated control approaches to maintain high levels of adaptability to unforeseen events that may occur in runtime. For this purpose, a software platform is presented in the following sections, facilitating human–robot collaboration in the context of an assembly workstation. The platform, namely a planning and control cockpit, enables human–robot collaborative assembly, acting as the portal for planning, scheduling, and integration of all required systems to execute, control and adapt the production process to possible events. Key integrated functionalities supporting the collaboration of human operators with industrial manipulators include subsystems in charge of human safety, robot execution control, high-level planning. The platform serves as the main symbiotic orchestrator capable of adaptive assembly planning and execution control of a hybrid assembly station. A prototype implementation is described. The cockpit is tested and validated in a use case related to the automotive industry and human–robot collaborative assembly in a workstation.
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References
D. Mourtzis, Challenges and future perspectives for the life cycle of manufacturing networks in the mass customisation era. Logist. Res. 9(1), 1–20 (2016). https://doi.org/10.1007/s12159-015-0129-0
D. Mourtzis, Design of customised products and manufacturing networks: towards frugal innovation. Int. J. Comput. Integr. Manuf. 31(12), 1161–1173 (2018). https://doi.org/10.1080/0951192X.2018.1509131
K. Jackson, K. Efthymiou, J. Borton, Digital Manufacturing and Flexible Assembly Technologies for Reconfigurable Aerospace Production Systems. Procedia CIRP 52, 274–279 (2016). https://doi.org/10.1016/j.procir.2016.07.054
S. Wang, J. Wan, D. Zhang, D. Li, C. Zhang, Towards smart factory for industry 4.0: A self-organized multi-agent system with big data based feedback and coordination. Comput. Networks 101, 158–168 (2016). https://doi.org/10.1016/j.comnet.2015.12.017
R. Patel, M. Hedelind, and P. Lozan-Villegas, “Enabling robots in small-part assembly lines: The ROSETTA approach—an industrial perspective,” Proc. of German Conf. on Robotics (ROBOTIK), 2012. https://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6309522.
I. Maurtua, A. Ibarguren, J. Kildal, L. Susperregi, B. Sierra, Human–robot collaboration in industrial applications: Safety, interaction and trust. Int. J. Adv. Robot. Syst. 14(4), 1–10 (2017). https://doi.org/10.1177/1729881417716010
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.
A. Pichler, S. Chandra Akkaladevi, M. Ikeda, M. Hofmann, M. Plasch, C. Wögerer, G. Fritz, “Towards Shared Autonomy for Robotic Tasks in Manufacturing,” Procedia Manuf., vol. 11, pp. 72–82, 2017, doi: https://doi.org/10.1016/j.promfg.2017.07.139.
V. Vyatkin, IEC 61499 as enabler of distributed and intelligent automation: State-of-the-art review. IEEE Trans. Ind. Informatics 7(4), 768–781 (2011). https://doi.org/10.1109/TII.2011.2166785
S. Wang, C. Zhang, D. Jia, Improvement of type declaration of the IEC 61499 basic function block for developing applications of cyber-physical system. Microprocess. Microsyst. 39(8), 1255–1261 (2015). https://doi.org/10.1016/j.micpro.2015.07.004
T.C. Yang, Networked control system: A brief survey. IEE Proc. Control Theory Appl. 153(4), 403–412 (2006). https://doi.org/10.1049/ip-cta:20050178
A. Sajid, H. Abbas, and K. Saleem, “Cloud-Assisted IoT-Based SCADA Systems Security: A Review of the State of the Art and Future Challenges,” IEEE Access, vol. 4. Institute of Electrical and Electronics Engineers Inc., pp. 1375–1384, 2016. https://doi.org/10.1109/ACCESS.2016.2549047.
R. Hunzinger, “SCADA fundamentals and applications in the IoT,” in Internet of Things and Data Analytics Handbook, Hoboken, NJ, USA: wiley, 2017, pp. 283–293.
R. Scattolini, Architectures for distributed and hierarchical Model Predictive Control—A review. J. Process Control 19(5), 723–731 (2009). https://doi.org/10.1016/j.jprocont.2009.02.003
M. Dotoli, A. Fay, M. Miśkowicz, C. Seatzu, Advanced control in factory automation: a survey. Int. J. Prod. Res. 55(5), 1243–1259 (2017). https://doi.org/10.1080/00207543.2016.1173259
C. Wang, Z. Bi, L. Da Xu, IoT and cloud computing in automation of assembly modeling systems. IEEE Trans. Ind. Informatics 10(2), 1426–1434 (2014). https://doi.org/10.1109/TII.2014.2300346
B. Hayes, Cloud Computing. Commun. ACM 51(7), 9–11 (2008). https://doi.org/10.1145/1364782.1364786
L. Zhang, Y. Luo, F. Tao, B.H. Li, L. Ren, X. Zhang, H. Guo, Y. Cheng, A. Hu, Y. Liu, Cloud manufacturing: a new manufacturing paradigm. Enterp. Inf. Syst. 8(2), 167–187 (2014). https://doi.org/10.1080/17517575.2012.683812
X.V. Wang, L. Wang, A. Mohammed, M. Givehchi, Ubiquitous manufacturing system based on Cloud: A robotics application. Robot. Comput. Integr. Manuf. 45, 116–125 (2017). https://doi.org/10.1016/j.rcim.2016.01.007
A.W. Colombo, R. Schoop, R. Neubert, An agent-based intelligent control platform for industrial holonic manufacturing systems. IEEE Trans. Ind. Electron. 53(1), 322–337 (2006). https://doi.org/10.1109/TIE.2005.862210
X. V. Wang, Zs. Kemény, J. Váncza, and L. Wang, “Human–robot collaborative assembly in cyber-physical production: Classification framework and implementation,” CIRP Ann.—Manuf. Technol., vol. 66, no. 1, pp. 5–8, 2017, doi: https://doi.org/10.1016/j.cirp.2017.04.101.
A.-W. Scheer, “Industrie 4.0 – Wie sehen Produktionsprozesse im Jahr 2020 aus?,” IMC Verlag, 2013.
P. Zheng, Z. Sang, R.Y. Zhong, Y. Liu, C. Liu, K. Mubarok, S. Yu, X. Xu., “Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives,” Front. Mech. Eng., vol. 13, no. 2, pp. 137–150, 2018, doi: https://doi.org/10.1007/s11465-018-0499-5.
D. Mourtzis, E. Vlachou, A cloud-based cyber-physical system for adaptive shop-floor scheduling and condition-based maintenance. J. Manuf. Syst. 47, 179–198 (2018). https://doi.org/10.1016/j.jmsy.2018.05.008
P. Senra, I. Lopes, J.A. Oliveira, Supporting Maintenance Scheduling: A Case Study. Procedia Manuf. 11, 2123–2130 (2017). https://doi.org/10.1016/j.promfg.2017.07.342
O.F. Valilai, M. Houshmand, A collaborative and integrated platform to support distributed manufacturing system using a service-oriented approach based on cloud computing paradigm. Robot. Comput. Integr. Manuf. 29(1), 110–127 (2013). https://doi.org/10.1016/j.rcim.2012.07.009
Y. Zhang, G. Zhang, Y. Liu, D. Hu, Research on services encapsulation and virtualization access model of machine for cloud manufacturing. J. Intell. Manuf. 28(5), 1109–1123 (2017). https://doi.org/10.1007/s10845-015-1064-2
A. Brant, M.M. Sundaram, A novel system for cloud-based micro additive manufacturing of metal structures. J. Manuf. Process. 20, 478–484 (2015). https://doi.org/10.1016/j.jmapro.2015.06.020
T. Chen, Y.C. Lin, A digital equipment identifier system. J. Intell. Manuf. 28(5), 1159–1169 (2017). https://doi.org/10.1007/s10845-015-1071-3
F. Xiang, Y. Hu, Y. Yu, H. Wu, QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system. Cent. Eur. J. Oper. Res. 22(4), 663–685 (2014). https://doi.org/10.1007/s10100-013-0293-8
L. Wang, X.V. Wang, “Cloud Robotics Towards a CPS Assembly System”, in Cloud-Based Cyber-Physical Systems in Manufacturing (Springer International Publishing, Cham, 2018), pp. 243–259
ISA95, Enterprise-Control System Integration—ISA. Retrieved March 30, 2020, from https://www.isa.org/isa95/
L. Wang, “Cyber Manufacturing: Research and Applications,” TMCE, no. May 2014, pp. 39–49, 2014.
X. Wang, X. Xu, Virtualise manufacturing capabilities in the cloud: Requirements, architecture and implementation. Int. J. Manuf. Res. 9(4), 348–368 (2014). https://doi.org/10.1504/IJMR.2014.066665
N. Nikolakis, R. Senington, K. Sipsas, A. Syberfeldt, and S. Makris, “On a containerized approach for the dynamic planning and control of a cyber-physical production system,” Robot. Comput. Integr. Manuf., 64(Aug. 2020), 101919, 2020, doi: https://doi.org/10.1016/j.rcim.2019.101919.
Cs. Kardos, A. Kovács, and J. Váncza, “Decomposition approach to optimal feature-based assembly planning,” CIRP Ann.—Manuf. Technol., vol. 66, no. 1, pp. 417–420, Jan. 2017, doi: https://doi.org/10.1016/j.cirp.2017.04.002.
C. Bikas, A. Argyrou, G. Pintzos, C. Giannoulis, K. Sipsas, N. Papakostas, G. Chryssolouris, An Automated Assembly Process Planning System. Procedia CIRP 44, 222–227 (2016). https://doi.org/10.1016/j.procir.2016.02.085
G. Chryssolouris, K. Dicke, M. Lee, An approach to short interval scheduling for discrete parts manufacturing. Int. J. Comput. Integr. Manuf. 4(3), 157–168 (1991). https://doi.org/10.1080/09511929108944491
K. Efthymiou, A. Pagoropoulos, and D. Mourtzis, “Intelligent scheduling for manufacturing systems: A case study,” in Lecture Notes in Mechanical Engineering, vol. 7, 2013, pp. 1153–1164.
N. Nikolakis, K. Sipsas, S. Makris, A cyber-physical context-aware system for coordinating human–robot collaboration. Procedia CIRP 72, 27–32 (2018). https://doi.org/10.1016/j.procir.2018.03.033
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Nikolakis, N., Sipsas, K., Makris, S. (2021). Cockpit: A Portal for Symbiotic Human–Robot Collaborative Assembly. In: Wang, L., Wang, X.V., Váncza, J., Kemény, Z. (eds) Advanced Human-Robot Collaboration in Manufacturing. Springer, Cham. https://doi.org/10.1007/978-3-030-69178-3_9
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