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On the development of a collaborative robotic system for industrial coating cells


For remaining competitive in the current industrial manufacturing markets, coating companies need to implement flexible production systems for dealing with mass customization and mass production workflows. The introduction of robotic manipulators capable of mimicking with accuracy the motions executed by highly skilled technicians is an important factor in enabling coating companies to cope with high customization. However, there are some limitations associated with the usage of a fully automated system for coating applications, especially when considering customized products of large dimensions and complex geometry. This paper addresses the development of a collaborative coating cell to increase the flexibility and efficiency of coating processes. The robot trajectory is taught with an intuitive programming by demonstration system, in which an icosahedron marker with multicoloured LEDs is attached to the coating tool for tracking its trajectories using a stereoscopic vision system. For avoiding the construction of fixtures and allowing the operator to freely place products within the coating work cell, a modular 3D perception system was developed, relying on principal component analysis for performing the initial point cloud alignment and on the iterative closest point algorithm for 6 DoF pose estimation. Furthermore, to enable safe and intuitive human-robot collaboration, a non-intrusive zone monitoring safety system was employed to track the position of the operator in the cell.

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  1. 1.

  2. 2.

  3. 3.

  4. 4.

  5. 5.

  6. 6.

  7. 7.

  8. 8.


  1. 1.

    Jazdi N (2014) Cyber physical systems in the context of industry 4.0. In: Automation, quality and testing, robotics, 2014, IEEE International Conference on IEEE, pp 1–4.

  2. 2.

    Cohen Y, Faccio M, Pilati F, Yao X (2019) Design and management of digital manufacturing and assembly systems in the Industry 4.0 era. Int J Adv Manuf Technol 105 (9):3565–3577.

    Article  Google Scholar 

  3. 3.

    Rojas RA, Rauch E (2019) From a literature review to a conceptual framework of enablers for smart manufacturing control. Int J Adv Manuf Technol 104(1-4):517–533.

    Article  Google Scholar 

  4. 4.

    Sethi P, Sarangi SR (2017) Internet of things: architectures, protocols, and applications. J Electric Comput Eng 2017.

  5. 5.

    Li K, Zhou T, Liu Bh (2020) Internet-based intelligent and sustainable manufacturing: developments and challenges. Int J Adv Manuf TechnoL.

  6. 6.

    Atzori L, Iera A, Morabito G (2010) The Internet of Things: a survey. Comput Netw 54(15):2787–2805.

    Article  Google Scholar 

  7. 7.

    Kagermann H, Lukas WD, Wahlster W (2011) Industrie 4.0: Mit dem internet der dinge auf dem weg zur 4. industriellen revolution, VDI nachrichten, 13(11)

  8. 8.

    Drath R, Horch A (2014) Industrie 4.0: hit or hype? [Industry Forum]. IEEE Ind Electron Mag 8(2):56–58.

    Article  Google Scholar 

  9. 9.

    Hermann M, Pentek T, Otto B (2016) Design principles for industrie 4.0 scenarios. In: Proceedings of the Annual Hawaii International Conference on System Sciences 2016-March, pp 3928–3937. DOI:10.1109/HICSS.2016.488,. arXiv:1011.1669v3

  10. 10.

    Keller M, Rosenberg M, Brettel M, Friederichsen N (2014) How virtualization, decentrazliation and network building change the manufacturing landscape: an Industry 4.0 perspective. Int J Mech Aerospace Ind Mechatron Manuf Eng 8(1):37–44

    Google Scholar 

  11. 11.

    Bocken NM, Short SW, Rana P, Evans S (2014) A literature and practice review to develop sustainable business model archetypes. J Clean Prod 65:42–56.

    Article  Google Scholar 

  12. 12.

    Frey CB, Osborne MA (2017) The future of employment: how susceptible are jobs to computerisation? Technol Forecast Soc Change 114:254–280.

    Article  Google Scholar 

  13. 13.

    Sha L, Gopalakrishnan S, Liu X, Wang Q (2008) Cyber-physical systems: a new frontier. In: 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC 2008), pp 1–9.

  14. 14.

    Lee J, Bagheri B, Kao HA (2015) A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manuf Lett 3:18–23.

    Article  Google Scholar 

  15. 15.

    Ferreira M, Costa P, Rocha L, Paulo Moreira A (2016) Stereo-based real-time 6-degrees of freedom work tool tracking for robot programing by demonstration. Int J Adv Manuf Technol 85(1-4):57–69.

    Article  Google Scholar 

  16. 16.

    Ong S, Yew A, Thanigaivel N, Nee A (2020) Augmented reality-assisted robot programming system for industrial applications. Robot Comput Integr Manuf 101820:61.

    Article  Google Scholar 

  17. 17.

    Wojtynek M, Steil J, Wrede S (2019) Plug, plan and produce as enabler for easy workcell setup and collaborative robot programming in smart factories. KI - Künstliche Intelligenz.

  18. 18.

    Krueger V, Rovida F, Grossmann B, Petrick R, Crosby M, Charzoule A, Garcia G, Behnke S, Toscano C, Veiga G (2019) Testing the vertical and cyber-physical integration of cognitive robots in manufacturing. Robot Comput Integr Manuf 57:213–229.

    Article  Google Scholar 

  19. 19.

    Marvel JA (2017) Sensors for safe, collaborative robots in smart manufacturing. In: 2017 IEEE sensors, pp 1–3.

  20. 20.

    Teke B, Lanz M, Kämäräinen J, Hietanen A (2018). In: 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), pp 1–6.

  21. 21.

    Samadikhoshkho Z, Zareinia K, Janabi-Sharifi F (2019) A brief review on robotic grippers classifications. In: 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), pp 1–4.

  22. 22.

    Villani V, Pini F, Leali F, Secchi C (2018) Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications. Mechatronics 55:248–266.

    Article  Google Scholar 

  23. 23.

    Gervasi R, Mastrogiacomo L, Franceschini F (2020) A conceptual framework to evaluate human-robot collaboration. Int J Adv Manuf 108(3):841–865.

    Article  Google Scholar 

  24. 24.

    Eimontaite I, Gwilt I, Cameron D, Aitken JM, Rolph J, Mokaram S, Law J (2019) Language-free graphical signage improves human performance and reduces anxiety when working collaboratively with robots. Int J Adv Manuf 100(1):55–73.

    Article  Google Scholar 

  25. 25.

    Pérez L, Rodríguez-Jiménez S, Rodríguez N, Usamentiaga R, García D, Wang L (2020) Symbiotic human–robot collaborative approach for increased productivity and enhanced safety in the aerospace manufacturing industry. Int J Adv Manuf 106(3):851–863.

    Article  Google Scholar 

  26. 26.

    Matúsová M, Bucányová M, Hrusková E (2019) The future of industry with collaborative robots. MATEC Web Conf 299:02008.

    Article  Google Scholar 

  27. 27.

    Technical Committee: ISO/TC 299 Robotics (2011a) ISO 10218-1:2011, Robots and robotic devices — Safety requirements for industrial robots— Part 1: Robots. Tech. rep., International Organization for Standardization

  28. 28.

    Technical Committee: ISO/TC 299 Robotics (2011b) ISO 10218-2:2011, Robots and robotic devices — Safety requirements for industrial robots— Part 2: Robot systems and integration. Tech. rep., International Organization for Standardization

  29. 29.

    Technical Committee: ISO/TC 299 Robotics (2016) ISO/TS 15066:2016, Robots and robotic devices — Collaborative robots. Tech. rep., International Organization for Standardization

  30. 30.

    Villani V, Pini F, Leali F, Secchi C (2018) Survey on human–robot collaboration in industrial settings: safety, intuitive interfaces and applications. Mechatronics 55(June 2017):248–266.

    Article  Google Scholar 

  31. 31.

    De Santis A, Siciliano B, De Luca A, Bicchi A (2008) An atlas of physical human-robot interaction. Mech Mach Theory 43(3):253–270.

    Article  MATH  Google Scholar 

  32. 32.

    Pilz, Ria and Robotic Industries Association and others (2008) The first safe camera system safetyeye opens up new horizons for safety & security

  33. 33.

    Magrini E, Ferraguti F, Ronga AJ, Pini F, Luca AD, Leali F (2020) Human-robot coexistence and interaction in open industrial cells. Robot Comput Integr Manuf 101846:61.

    Article  Google Scholar 

  34. 34.

    Tsarouchi P, Matthaiakis AS, Makris S, Chryssolouris G (2017) On a human-robot collaboration in an assembly cell. Int J Comput Integr Manuf 30(6):580–589.

    Article  Google Scholar 

  35. 35.

    Neto P, Simão M, Mendes N, Safeea M (2019) Gesture-based human-robot interaction for human assistance in manufacturing. Int J Adv Manuf 101(1):119–135.

    Article  Google Scholar 

  36. 36.

    Tavares P, Costa C, Rocha L, Malaca P, Costa P, Moreira A, Sousa A, Veiga G (2019) Collaborative welding system using BIM for robotic reprogramming and spatial augmented reality. Autom Constr 106:102825.

    Article  Google Scholar 

  37. 37.

    Krueger V, Rovida F, Grossmann B, Petrick R, Crosby M, Charzoule A, Garcia GM, Behnke S, Toscano C, Veiga G (2019) Testing the vertical and cyber-physical integration of cognitive robots in manufacturing. Robot Comput Integr Manuf 57:213–229.

    Article  Google Scholar 

  38. 38.

    Lenz C, Nair S, Rickert M, Knoll A, Rosel W, Gast J, Bannat A, Wallhoff F (2008) Joint-action for humans and industrial robots for assembly tasks. In: RO-MAN 2008 - The 17th IEEE International Symposium on Robot and Human Interactive Communication, pp 130–135.

  39. 39.

    Fast-Berglund Å, Romero D (2019) Strategies for implementing collaborative robot applications for the operator 4.0. In: Production Management for the Factory of the Future. APMS 2019. IFIP Advances in Information and Communication Technology, IFIP International Federation for Information Processing, pp 682–689.

  40. 40.

    Cherubini A, Passama R, Navarro B, Sorour M, Khelloufi A, Mazhar O, Tarbouriech S, Zhu J, Tempier O, Crosnier A, Fraisse P, Ramdani S (2019) A collaborative robot for the factory of the future: Bazar. Int J Adv Manuf 105(9):3643–3659.

    Article  Google Scholar 

  41. 41.

    Johannsmeier L, Haddadin S (2017) A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes. IEEE Robot Autom Lett 2(1):41–48.

    Article  Google Scholar 

  42. 42.

    Makrini IE, Merckaert K, Lefeber D, Vanderborght B (2017) Design of a collaborative architecture for human-robot assembly tasks. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 1624–1629.

  43. 43.

    Martínez S, Carvajal A, Loza D, Ibarra A, Segura L (2017) Collaborative two-arm robotic torso for the development of an assembly process

  44. 44.

    Zeng F, Xiao JY, Liu H (2019) Force/torque sensorless compliant control strategy for assembly tasks using a 6-DoF collaborative robot. IEEE Access 7:108795–108805.

    Article  Google Scholar 

  45. 45.

    Sadrfaridpour B, Wang Y (2018) Collaborative assembly in hybrid manufacturing cells: an integrated framework for human–robot interaction. IEEE Trans Autom Sci Eng 15(3):1178–1192.

    Article  Google Scholar 

  46. 46.

    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 

  47. 47.

    Accorsi R, Tufano A, Gallo A, Gajhlizia F, Cocchi G, Ronzoni M, Abbate A, Manzini R (2019) An application of collaborative robots in a food production facility. 29th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2019), June 24-28, 2019, Limerick, Ireland, Beyond Industry 4.0: Industrial Advances, Engineering Education and Intelligent Manufacturing, vol 38, pp 341–348, DOI

  48. 48.

    Siciliano B, Khatib O (2007) Springer handbook of robotics. Springer, Berlin

    MATH  Google Scholar 

  49. 49.

    Chen H, Xi N (2008) Automated tool trajectory planning of industrial robots for painting composite surfaces. Int J Adv Manuf 35(7-8):680–696

    Article  Google Scholar 

  50. 50.

    Wang Z, Fan J, Jing F, Liu Z, Tan M (2019) A pose estimation system based on deep neural network and ICP registration for robotic spray painting application. Int J Adv Manuf 104(1):285–299.

    Article  Google Scholar 

  51. 51.

    Asadi E, Li B, Chen I (2018) Pictobot: a cooperative painting robot for interior finishing of industrial developments. IEEE Robot Autom Mag 25(2):82–94.

    Article  Google Scholar 

  52. 52.

    Object Management Group (2011) Business process model and notation 2.0 specification.

  53. 53.

    Silver B (2011) Bpmn method and style, with bpmn implementer’s guide: a structured approach for business process modeling. sl: Cody

  54. 54.

    List B, Korherr B (2006) An evaluation of conceptual business process modelling languages. In: Proceedings of the 2006 ACM Symposium on Applied Computing, pp 1532–1539.

  55. 55.

    Dumas M, La Rosa M, Mendling J, Reijers HA et al (2013) Fundamentals of business process management, vol 1. Springer, Berlin.

    Book  Google Scholar 

  56. 56.

    Erasmus J, Vanderfeesten I, Traganos K, Jie-A-Looi X, Kleingeld A, Grefen P (2018) A method to enable ability-based human resource allocation in business process management systems. In: IFIP Working Conference on the Practice of Enterprise Modeling, pp 37–52, Springer.

  57. 57.

    Vanderfeesten I, Erasmus J, Traganos K, Bouklis P, Garbi A, Boultadakis G, Dijkman R, Grefen P (2019) Developing process execution support for high-tech manufacturing processes. In: Empirical studies on the development of executable business processes. Springer, pp 113–142.

  58. 58.

    Grefen P, Vanderfeesten I, Boultadakis G (2018) Developing a cyber-physical system for hybrid manufacturing in an internet-of-things context. In: Protocols and applications for the industrial internet of things. IGI Global, pp 35–63.

  59. 59.

    Kruchten PB (1995) The 4 + 1 view model of architecture. IEEE Softw 12(6):42–50.

    Article  Google Scholar 

  60. 60.

    Grefen P, Eshuis R, Mehandjiev N, Kouvas G, Weichhart G (2016) Business information system architecture fall 2016 edition. Eindhoven University of Technology, Eindhoven

    Google Scholar 

  61. 61.

    Erasmus J, Vanderfeesten I, Traganos K, Grefen P (2018) The case for unified process management in smart manufacturing. In: 2018 IEEE 22nd International Enterprise Distributed Object Computing Conference (EDOC). IEEE, pp 218–227

  62. 62.

    Grefen P, Vanderfeesten I, Boultadakis G (2015) D2. 2a-complete system design-public version, Tech. rep., Eindhoven University of Technology

  63. 63.

    Erasmus J, Grefen P, Vanderfeesten I, Traganos K (2018) Smart hybrid manufacturing control using cloud computing and the internet-of-things. Machines 6(4):62.

    Article  Google Scholar 

  64. 64.

    International Electrotechnical Commission (2003) International electrotechnical commission 62264-1 enterprise-control system integration–part 1: models and terminology. IEC, Genf

  65. 65.

    Camunda Services GmbH (2020) Camunda: Process Automation Reinvented for the Digital Enterprise.

  66. 66.

    Crick C, Jay G, Osentoski S, Pitzer B, Jenkins OC (2017) Rosbridge: Ros for non-ros users. In: Robotics research. Springer, pp 493–504.

  67. 67.

    Argall BD, Chernova S, Veloso M, Browning B (2009) A survey of robot learning from demonstration. Rob Auton Syst 57(5):469–483.

    Article  Google Scholar 

  68. 68.

    Costa CM, Sobreira HM, Sousa AJ, Veiga GM (2016) Robust 3/6 dof self-localization system with selective map update for mobile robot platforms. Robot Auton Syst 76:113–140.

    Article  Google Scholar 

  69. 69.

    Sahin C, Garcia-Hernando G, Sock J, Kim TK (2020) A review on object pose recovery: from 3d bounding box detectors to full 6d pose estimators. Image Vis Comput 103898:96.

    Article  Google Scholar 

  70. 70.

    Rusu RB, Blodow N, Beetz M (2009) Fast point feature histograms (fpfh) for 3d registration. In: 2009 IEEE International Conference on Robotics and Automation, pp 3212–3217

  71. 71.

    Filipe S, Alexandre LA (2014) A comparative evaluation of 3d keypoint detectors in a rgb-d object dataset. In: 2014 International conference on computer vision theory and applications (VISAPP), vol 1, pp 476–483

  72. 72.

    Rusu RB, Bradski G, Thibaux R, Hsu J (2010) Fast 3d recognition and pose using the viewpoint feature histogram. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 2155–2162

  73. 73.

    Bellekens B, Spruyt V, Berkvens R, Penne R, Weyn M (2015) A benchmark survey of rigid 3d point cloud registration algorithms. Int J Adv Intell Syst 1

  74. 74.

    Yuan C, Yu X, Luo Z (2016) 3d point cloud matching based on principal component analysis and iterative closest point algorithm. In: International Conference on Audio, Language and Image Processing (ICALIP), pp 404–408

  75. 75.

    Li F, Stoddart D, Hitchens C (2017) Method to automatically register scattered point clouds based on principal pose estimation. Opt Eng 56(4):1–10.

    Article  Google Scholar 

  76. 76.

    Besl PJ, McKay ND (1992) A method for registration of 3-d shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256

    Article  Google Scholar 

  77. 77.

    Hedberg J, Söderberg A, Tegehall J (2011) How to design safe machine control systems: a guideline to en iso 13849-1

  78. 78.

    Bessler J, Schaake L, Bidard C, Buurke JH, Lassen AE, Nielsen K, Saenz J, Vicentini F (2018) Covr–towards simplified evaluation and validation of collaborative robotics applications across a wide range of domains based on robot safety skills. In: International Symposium on Wearable Robotics. Springer, pp 123–126.

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The research leading to these results has received funding from the European Union’s Horizon 2020 - The EU Framework Programme for Research and Innovation 2014-2020, under grant agreement no. 680734. This work has also been financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project UIDB/50014/2020.

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Correspondence to Rafael Arrais.

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Arrais, R., Costa, C.M., Ribeiro, P. et al. On the development of a collaborative robotic system for industrial coating cells. Int J Adv Manuf Technol 115, 853–871 (2021).

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  • Collaborative robotics
  • Safety
  • Flexible robotics
  • Smart manufacturing
  • Industry 4.0