PIROS: Cooperative, Safe and Reconfigurable Robotic Companion for CNC Pallets Load/Unload Stations

  • Federico Vicentini
  • Nicola Pedrocchi
  • Manuel BeschiEmail author
  • Matteo Giussani
  • Niccolò Iannacci
  • Paolo Magnoni
  • Stefania Pellegrinelli
  • Loris Roveda
  • Enrico Villagrossi
  • Mehrnoosh Askarpour
  • Inaki Maurtua
  • Alberto Tellaeche
  • Francesco Becchi
  • Giovanni Stellin
  • Giuseppe Fogliazza
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 136)


Handling and assembling applications with small batch size and high production mix require requires high adaptability, reconfigurability and flexibility. Thus, human-robot collaboration could be an effective solution to ensure production performance and operator satisfaction. This scenario requires human-awareness in different levels of the software framework, from the robot control to the task planning. The goal is to assign high added value activities to the human as much as possible, while the robot has to be able to substitute the human when needed. Team PIROS faces this goal by designing a IEC 61499/ROS-based architecture which integrate safety assessment, advanced force control, human-aware motion planning, gesture recognition, and task scheduling.


Human-robot collaboration Manipulation Human awareness 


  1. 1.
    Krüger, J., Wang, L., Verl, A., Bauernhansl, T., Carpanzano, E., Makris, S., Fleischer, J., Reinhart, G., Franke, J., Pellegrinelli, S.: Innovative control of assembly systems and lines. CIRP Ann. 66(2), 707–730 (2017)CrossRefGoogle Scholar
  2. 2.
    Chryssolouris, G.: Manufacturing Systems: Theory and Practice. Springer Science & Business Media (2013)Google Scholar
  3. 3.
    Tolio, T.: Design of Flexible Production Systems. Springer (2008)Google Scholar
  4. 4.
    Brettel, M., Klein, M., Friederichsen, N.: The relevance of manufacturing flexibility in the context of industrie 4.0. Proc. CIRP 41, 105–110 (2016). Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future - Proceedings of the 48th CIRP Conference on Manufacturing SystemsGoogle Scholar
  5. 5.
    ElMaraghy, Hoda A.: Flexible and reconfigurable manufacturing systems paradigms. Int. J. Flex. Manuf. Syst. 17(4), 261–276 (2005). OctCrossRefGoogle Scholar
  6. 6.
    Roveda, L., Vicentini, F., Tosatti, L.M.: Deformation-tracking impedance control in interaction with uncertain environments. In: 2013 IEEE/RSJ International Confererence on Intelligent Robots and Systems (IROS), pp. 1992–1997. IEEE (2013)Google Scholar
  7. 7.
    Siciliano, Bruno, Villani, Luigi: Robot force control, 1st edn. Kluwer Academic Publishers, Norwell, MA, USA (2000)zbMATHGoogle Scholar
  8. 8.
    Geerinck, T., Colon, E., Berrabah, S.A., Cauwerts, K., Sahli, H.: Tele-robot with shared autonomy: distributed navigation development framework. Integr. Comput.-Aided Eng. 13(4), 329–345 (2006)CrossRefGoogle Scholar
  9. 9.
    Hu, X., Zeigler, B.P.: A simulation-based virtual environment to study cooperative robotic systems. Integ. Comput.-Aided Eng. 12(4), 353–367 (2005)CrossRefGoogle Scholar
  10. 10.
    Tsarouchi, Panagiota, Makris, Sotiris, Chryssolouris, George: On a human and dual-arm robot task planning method. Proc. CIRP 57, 551–555 (2016)CrossRefGoogle Scholar
  11. 11.
    Srivastava, S., Fang, E., Riano, L., Chitnis, R., Russell, S., Abbeel, P.: Combined task and motion planning through an extensible planner-independent interface layer. In: 2014 IEEE international conference on robotics and automation (ICRA), pp. 639–646. IEEE (2014)Google Scholar
  12. 12.
    De Silva, L., Lallement, R., Alami, R.: The hatp hierarchical planner: formalisation and an initial study of its usability and practicality. In: 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 6465–6472. IEEE (2015)Google Scholar
  13. 13.
    Pellegrinelli, S., Moro, F.L., Pedrocchi, N., Tosatti, L.M., Tolio, T.: A probabilistic approach to workspace sharing for human—robot cooperation in assembly tasks. CIRP Ann. 65(1), 57–60 (2016)CrossRefGoogle Scholar
  14. 14.
    Pellegrinelli, Stefania, Pedrocchi, Nicola: Estimation of robot execution time for close proximity human-robot collaboration. Integ. Comput.-Aided Eng. 25(1), 81–96 (2017). DecCrossRefGoogle Scholar
  15. 15.
    Ding, H., Schipper, M., Matthias, B.: Collaborative behavior design of industrial robots for multiple human-robot collaboration. In: IEEE ISR 2013, pp. 1–6. IEEE (2013)Google Scholar
  16. 16.
    Ding, H., Heyn, J., Matthias, B., Staab, H.: Structured collaborative behavior of industrial robots in mixed human-robot environments. In: 2013 IEEE international conference on automation science and engineering (CASE), pp. 1101–1106. IEEE (2013)Google Scholar
  17. 17.
    Weitschat, R., Ehrensperger, J., Maier, M., Aschemann, H.: Safe and efficient human-robot collaboration part i: estimation of human arm motions. In: 2018 IEEE international conference on robotics and automation (ICRA), pp. 1993–1999. IEEE (2018)Google Scholar
  18. 18.
    Nicolis, D., Zanchettin, A.M., Rocco, P.: Human intention estimation based on neural networks for enhanced collaboration with robots. In: 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 1326–1333. IEEE (2018)Google Scholar
  19. 19.
    Thomas, U., Hirzinger, G., Rumpe, B., Schulze, C., Wortmann, A.: A new skill based robot programming language using uml/p statecharts. In: 2013 IEEE international conference on robotics and automation (ICRA), pp. 461–466. IEEE (2013)Google Scholar
  20. 20.
    Steck, A., Schlegel, C.: Managing execution variants in task coordination by exploiting design-time models at run-time. In: 2011 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 2064–2069. IEEE (2011)Google Scholar
  21. 21.
    Brugali, D., Scandurra, P.: Component-based robotic engineering (part i)[tutorial]. IEEE Robotics Autom. Mag. 16(4), 84–96 (2009)CrossRefGoogle Scholar
  22. 22.
    Brugali, Davide, Shakhimardanov, Azamat: Component-based robotic engineering (part ii). IEEE Robotics Autom. Mag. 17(1), 100–112 (2010)CrossRefGoogle Scholar
  23. 23.
    Lewis, R.: Modelling Control Systems Using IEC 61499: Applying Function Blocks to Distributed Systems. Number 59. IET (2001)Google Scholar
  24. 24.
    Vyatkin, Valeriy: The IEC 61499 standard and its semantics. IEEE Ind. Electron. Mag. 3(4), 40–48 (2009)CrossRefGoogle Scholar
  25. 25.
    Strasser, T., Rooker, M., Ebenhofer, G., Zoitl, A., Sunder, C., Valentini, A., Martel, A.: Structuring of large scale distributed control programs with IEC 61499 subapplications and a hierarchical plant structure model. In: 2008 IEEE international conference on emerging technologies and factory automation, pp. 934–941. IEEE (2008)Google Scholar
  26. 26.
    Sunder, C., Zoitl, A., Strasser, T., Favre-Bulle, B.: Intuitive control engineering for mechatronic components in distributed automation systems based on the reference model of IEC 61499. In: INDIN’05. 2005 3rd IEEE international conference on industrial informatics, 2005, pp. 50–55. IEEE (2005)Google Scholar
  27. 27.
    Wenger, M., Eisenmenger, W., Neugschwandtner, G., Schneider, B., Zoitl, A.: A model based engineering tool for ros component compositioning, configuration and generation of deployment information. In: 2016 IEEE 21st international conference on emerging technologies and factory automation (ETFA), pp. 1–8. IEEE (2016)Google Scholar
  28. 28.
    Zander, S., Heppner, G., Neugschwandtner, G., Awad, R., Essinger, M., Ahmed, N.: A model-driven engineering approach for ROS using ontological semantics. arXiv preprint arXiv:1601.03998 (2016)
  29. 29.
    Allen, J.F.: Maintaining knowledge about temporal intervals. In: Readings in qualitative reasoning about physical systems, pp. 361–372. Elsevier (1990)Google Scholar
  30. 30.
    Iannacci, N., Giussani, M., Vicentini, F., Tosatti, L.M.: Robotic cell work-flow management through an IEC 61499-ROS architecture. In: 2016 IEEE 21st international conference on emerging technologies and factory automation (ETFA), pp. 1–7. IEEE (2016)Google Scholar
  31. 31.
    Marvel, J.A., Falco, J., Marstio, I.: Characterizing task-based human—robot collaboration safety in manufacturing. IEEE Trans. Syst. Man Cybern Syst 45(2), 260–275 (2014)CrossRefGoogle Scholar
  32. 32.
    Mayer, M.C.,Orlandini, A., Umbrico, A.: Planning and execution with flexible timelines: a formal account. Acta Inform. 53(6–8), 649–680 (2016)Google Scholar
  33. 33.
    Alami, R., Clodic, A., Montreuil, V., Sisbot, E.A., Chatila, R.: Task planning for human-robot interaction. In: Proceedings of the 2005 joint conference on smart objects and ambient intelligence: innovative context-aware services: usages and technologies, pp. 81–85. ACM (2005)Google Scholar
  34. 34.
    Cesta, A., Orlandini, A., Bernardi, G., Umbrico, A.: Towards a planning-based framework for symbiotic human-robot collaboration. In: 2016 IEEE 21st international conference on emerging technologies and factory automation (ETFA), pp. 1–8. IEEE (2016)Google Scholar
  35. 35.
    Furia, C.A., Mandrioli, D., Morzenti, A., Rossi, M.: Modeling Time in Computing. Monographs in Theoretical Computer Science. An EATCS Series. Springer (2012)Google Scholar
  36. 36.
    PoliMi.: Zot: a bounded satisfiability checker. (2012)
  37. 37.
    Pradella, M., Morzenti, M., San Pietro, P.: Bounded satisfiability checking of metric temporal logic specifications. ACM TOSEM 22(3), 20, 1–20, 54 (2013)CrossRefGoogle Scholar
  38. 38.
    Baresi, L., Pourhashem Kallehbasti, M.M., Rossi, M.: Efficient scalable verification of LTL specifications. In: Proceedings of the 37th international conference on software engineering, pp. 711–721. IEEE Press (2015)Google Scholar
  39. 39.
    Furia, C.A., Mandrioli, D., Morzenti, A., Rossi, M.: Modeling time in computing: a taxonomy and a comparative survey. ACM Comput. Surv. 42(2), 6, 1–6, 59 (2010)CrossRefGoogle Scholar
  40. 40.
    Askarpour, M., Mandrioli, D., Rossi, M., Vicentini, F.: Modeling operator behavior in the safety analysis of collaborative robotic applications. In: SAFECOMP, pp. 89–104 (2017)Google Scholar
  41. 41.
    Askarpour, Mehrnoosh, Mandrioli, Dino, Rossi, Matteo, Vicentini, Federico: Formal model of human erroneous behavior for safety analysis in collaborative robotics. Robotics Comput.-Integ. Manuf. 57, 465–476 (2019)CrossRefGoogle Scholar
  42. 42.
    ISO/TS 15066.: Robots and Robotic Devices—Collaborative Robots. International Organization for Standardization, Geneva, Switzerland (2016)Google Scholar
  43. 43.
    ISO 12100.: Safety of Machinery—General Principles for Design—Risk Assessment and Risk Reduction. International Organization for Standardization, Geneva, Switzerland (2010)Google Scholar
  44. 44.
    ISO/TR 14121-2.: Safety of Machinery—Risk Assessment—Part 2: Practical Guidance and Examples of Methods. International Organization for Standardization, Geneva, Switzerland (2012)Google Scholar
  45. 45.
    Askarpour, M.: Safer-HRC: a methodology for safety assessment through formal verification in human-robot collaboration. Doctoral dissertation, Politecnico di Milano (2018)Google Scholar
  46. 46.
    Lestingi, Livia, Longoni, Samuele: HRC-TEAM: A Model-driven Approach to Formal Verification and Deployment of Collaborative Robotic Applications. Msc, Politecnico di Milano (2017)Google Scholar
  47. 47.
    H2020 EC.: Sharework: safe and effective human-robot cooperation towards a better competiveness on current automation lack manufacturing processes. Founded from the european union’s horizon 2020 research and innovation programme under grant agreement no. 820807. (2018)
  48. 48.
    “Assuring Autonomy”. Recoll: Safety of reconfigurable collaborative robots for flexible manufacturing systems. Founded from “assuring autonomy” international program (by lloyds foundation and university of york). (2018)

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Federico Vicentini
    • 1
  • Nicola Pedrocchi
    • 1
  • Manuel Beschi
    • 1
    Email author
  • Matteo Giussani
    • 1
  • Niccolò Iannacci
    • 1
  • Paolo Magnoni
    • 1
  • Stefania Pellegrinelli
    • 1
  • Loris Roveda
    • 1
  • Enrico Villagrossi
    • 1
  • Mehrnoosh Askarpour
    • 1
    • 2
  • Inaki Maurtua
    • 3
  • Alberto Tellaeche
    • 3
  • Francesco Becchi
    • 4
  • Giovanni Stellin
    • 4
  • Giuseppe Fogliazza
    • 5
  1. 1.National Research Council of Italy (CNR-STIIMA)MilanItaly
  2. 2.Politecnico di MilanoMilanItaly
  3. 3.Fundation TeknikerEibarSpain
  4. 4.Telerobots LabsGenoaItaly
  5. 5.MCM SpAVigolzone (PC)Italy

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