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Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

Human-robot collaboration (HRC) in the manufacturing context aims to realise a shared workspace where humans can work side by side with robots in close proximity. In human-robot collaborative manufacturing, robots are required to adapt to human behaviours by dynamically changing their pre-planned tasks. However, the robots used today controlled by rigid native codes can no longer support effective human-robot collaboration. To address such challenges, programming-free and multimodal communication and control methods have been actively explored to facilitate the robust human-robot collaborative manufacturing. They can be applied as the solutions to the needs of the increased flexibility and adaptability, as well as higher effort on the conventional (re)programing of robots. These high-level multimodal commands include gesture and posture recognition, voice processing and sensorless haptic interaction for intuitive HRC in local and remote collaboration. Within the context, this paper presents an overview of HRC in manufacturing. Future research directions are also highlighted.

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References

  1. Wang, L., Gao, R., Váncza, J., Krüger, J., Wang, X.V., Makris, S., Chryssolouris, G.: Symbiotic human-robot collaborative assembly. CIRP Ann. 68(2), 701–726 (2019)

    Article  Google Scholar 

  2. Wang, X.V., Kemény, Z., Váncza, J., Wang, L.: Human–robot collaborative assembly in cyber-physical production: classification framework and implementation. CIRP Ann. Manuf. Technol. 66(1), 5–8 (2017)

    Article  Google Scholar 

  3. Schmidtler, J., Knott, V., Hölzel, C., Bengler, K.: Human centered assistance applications for the working environment of the future. Occup. Ergon. 12(3), 83–95 (2015)

    Article  Google Scholar 

  4. Wang, X.V., Seira, A., Wang, L.: Classification, personalised safety framework and strategy for human-robot collaboration. In: Proceedings of International Conference on Computers & Industrial Engineering, CIE 2018 December (2018)

    Google Scholar 

  5. ISO 10218-1:2011 Robots and robotic devices — Safety requirements for industrial robots — Part 1: Robots

    Google Scholar 

  6. Lien, T.K., Verl, A.: Cooperation of human and machines in assembly lines. CIRP Ann. Manuf. Technol. 58, 628–646 (2009)

    Article  Google Scholar 

  7. Yanco, H.A., Drury, J.: Classifying human-robot interaction: an updated taxonomy. In: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 2841–2846 (2004)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  9. Krüger, J., Lien, T.K., Verl, A.: Cooperation of human and machines in assembly lines. CIRP Ann. 58(2), 628–646 (2009)

    Article  Google Scholar 

  10. Pellegrinelli, S., Moro, F.L., Pedrocchi, N., Molinari Tosatti, L., Tolio, T.: A probabilistic approach to workspace sharing for human–robot cooperation in assembly tasks. CIRP Ann. 65(1), 57–60 (2016)

    Article  Google Scholar 

  11. Wang, L., Haghighi, A.: Combined strength of holons, agents and function blocks in cyber-physical systems. J. Manuf. Syst. 40, 25–34 (2016)

    Article  Google Scholar 

  12. Leitão, P.: Agent-based distributed manufacturing control: a state-of-the-art survey. Eng. Appl. Artif. Intell. 22(7), 979–991 (2009)

    Article  Google Scholar 

  13. Monostori, L., Váncza, J., Kumara, S.R.T.: Agent-based systems for manufacturing. CIRP Ann. 55(2), 697–720 (2006)

    Article  Google Scholar 

  14. Bi, Z.M., Wang, L., Lang, S.Y.T.: Current status of reconfigurable assembly systems. Int. J. Manuf. Res. 2(3), 303–328 (2007)

    Article  Google Scholar 

  15. Musić, S., Hirche, S.: Control sharing in human-robot team interaction. Annu. Rev. Control 44, 342–354 (2017)

    Article  Google Scholar 

  16. Janni, P.: Human-robot collaboration: a survey. Lingua Nostra 67(3–4), 122–124 (2006)

    Google Scholar 

  17. EU project: SYMBIO-TIC. http://www.symbio-tic.eu/

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

    Article  Google Scholar 

  19. Elena, R., Brian, A.: Levels of human and robot collaboration for automotive manufacturing. In: Proceedings of the Workshop on Performance Metrics for Intelligent Systems, March 2012 (2012)

    Google Scholar 

  20. Tan, J.T.C., Duan, F., Zhang, Y., Watanabe, K., Kato, R., Arai, T.: Human-robot collaboration in cellular manufacturing: design and development. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 29–34 (2009)

    Google Scholar 

  21. Sadrfaridpour, B., Saeidi, H., Wang, Y.: An integrated framework for human-robot collaborative assembly in hybrid manufacturing cells. In: International Conference on Automation Science and Engineering, November 2016, pp. 462–467 (2016)

    Google Scholar 

  22. Wang, L.: From intelligence science to intelligent manufacturing. Engineering 5(4), 615–618 (2019)

    Article  MathSciNet  Google Scholar 

  23. Thomas, C., Matthias, B., Kuhlenkötter, B.: Human ‐ robot collaboration – new applications in industrial robotics. In: International Conference in Competitive Manufacturing, January, pp. 293–299 (2016)

    Google Scholar 

  24. Kardos, C., Kovács, A., Váncza, J.: Decomposition approach to optimal feature-based assembly planning. CIRP Ann. 66(1), 417–420 (2017)

    Article  Google Scholar 

  25. Kardos, C., Váncza, J.: Mixed-initiative assembly planning combining geometric reasoning and constrained optimization. CIRP Ann. 67(1), 463–466 (2018)

    Article  Google Scholar 

  26. Michalos, G., Makris, S., Tsarouchi, P., Guasch, T., Kontovrakis, D., Chryssolouris, G.: Design considerations for safe human-robot collaborative workplaces. Procedia CIRP 37, 248–253 (2015)

    Article  Google Scholar 

  27. Rosenthal, S., Biswas, J., Veloso, M.: An effective personal mobile robot agent through symbiotic human-robot interaction. In: Proceedings of International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, vol. 2, pp. 915–922 (2010)

    Google Scholar 

  28. Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer, O., Schuh, G., Sihn, W., Ueda, K.: Cyber-physical systems in manufacturing. CIRP Ann. 65(2), 621–641 (2016)

    Article  Google Scholar 

  29. Wang, L., Balasubramanian, S., Norrie, D.H,, Brennan, R.W.: Agent-based control system for next generation manufacturing (1998)

    Google Scholar 

  30. Shen, W., Wang, L., Hao, Q.: Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 36(4), 563–577 (2006)

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Ji, W., Yin, S., Wang, L.: A virtual training based programming-free automatic assembly approach for future industry. IEEE Access 6, 43865–43873 (2018)

    Article  Google Scholar 

  33. International Electrotechnical Commission: International Standard of Function Blocks – Part 1: Architecture, IEC 61499, pp. 1–111 (2005)

    Google Scholar 

  34. Tsarouchi, P., Michalos, G., Makris, S., Athanasatos, T., Dimoulas, K., Chryssolouris, G.: On a human–robot workplace design and task allocation system. Int. J. Comput. Integr. Manuf. 30(12), 1272–1279 (2017)

    Article  Google Scholar 

  35. Liu, S., Wang, Y., Wang, X.V., Wang, L.: Energy-efficient trajectory planning for an industrial robot using a multi-objective optimisation approach. Procedia Manuf. 25(August), 517–525 (2018)

    Article  Google Scholar 

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

    Article  Google Scholar 

  37. Tsarouchi, P., Makris, S., Chryssolouris, G.: Human–robot interaction review and challenges on task planning and programming. Int. J. Comput. Integr. Manuf. 29(8), 916–931 (2016)

    Article  Google Scholar 

  38. Tsarouchi, P., Makris, S., Chryssolouris, G.: On a human and dual-arm robot task planning method. Procedia CIRP 57, 551–555 (2016)

    Article  Google Scholar 

  39. Wang, L., Givehchi, M., Schmidt, B., Adamson, G.: Robotic assembly planning and control with enhanced adaptability. Procedia CIRP 3(1), 173–178 (2012)

    Article  Google Scholar 

  40. Wang, L., Schmidt, B., Givehchi, M., Adamson, G.: Robotic assembly planning and control with enhanced adaptability through function blocks. Int. J. Adv. Manuf. Technol. 77(1–4), 705–715 (2015)

    Google Scholar 

  41. ISO 10218-2:2011 Robots and robotic devices — Safety requirements for industrial robots — Part 2: Robot systems and integration

    Google Scholar 

  42. Lasota, P.A., Fong, T., Shah, J.A.: A survey of methods for safe human-robot interaction. Found. Trends Robot. 5(3), 261–349 (2017)

    Article  Google Scholar 

  43. Haddadin, S., De Luca, A., Albu-Schäffer, A.: Robot collisions: a survey on detection, isolation, and identification. IEEE Trans. Robot. 33(6), 1292–1312 (2017)

    Article  Google Scholar 

  44. Schmidt, B., Wang, L.: Active collision avoidance for human-robot collaborative manufacturing. In: The 5th International Swedish Production Symposium, pp. 81–86 (2012)

    Google Scholar 

  45. Vasic, M., Billard, A.: Safety issues in human-robot interactions. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 197–204 (2013)

    Google Scholar 

  46. Carlson, J., Murphy, R.R.: How UGVs physically fail in the field. IEEE Trans. Robot. 21(3), 423–437 (2005)

    Article  Google Scholar 

  47. Schmidt, B., Wang, L.: Depth camera based collision avoidance via active robot control. J. Manuf. Syst. 33(4), 711–718 (2014)

    Article  Google Scholar 

  48. KUKA System Software 8.3 Operating and Programming Instructions for End Users (2019)

    Google Scholar 

  49. ISO 13855:2010 Safety of machinery — Positioning of safeguards with respect to the approach speeds of parts of the human body

    Google Scholar 

  50. Kosuge, K., Yoshida, H., Taguchi, D., Fukuda, T., Hariki, K., Kanitani, K., Sakai, M.: Robot-human collaboration for new robotic applications. In: Proceedings of IECON - Industrial Electronics Conference, vol. 2, pp. 713–718 (1994)

    Google Scholar 

  51. ISO/TS 15066:2016 Robots and robotic devices — Collaborative robots

    Google Scholar 

  52. Pfitzner, C., Antal, W., Hess, P., May, S., Merkl, C., Koch, P., Koch, R., Wagner, M.: 3D multi-sensor data fusion for object localization in industrial applications. In: ISR/Robotik 2014; 41st International Symposium on Robotics Proceedings, pp. 1–6. VDE (2014)

    Google Scholar 

  53. Michalos, G., Kousi, N., Karagiannis, P., Gkournelos, C., Dimoulas, K., Koukas, S., Mparis, K., Papavasileiou, A., Makris, S.: Seamless human robot collaborative assembly – an automotive case study. Mechatronics 55, 194–211 (2018)

    Article  Google Scholar 

  54. Rocco, P., Zanchettin, A.M., Matthias, B., Ding, H., Ceriani, N.M.: Safety in human-robot collaborative manufacturing environments: metrics and control. IEEE Trans. Autom. Sci. Eng. 13(2), 882–893 (2015)

    Google Scholar 

  55. Heinzmann, J., Zelinsky, A.: Quantitative safety guarantees for physical human-robot interaction. Int. J. Robot. Res. 22(7-8 Special Issue), 479–504 (2003)

    Google Scholar 

  56. Calinon, S., Sardellitti, I., Caldwell, D.G.: Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies. In: IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings, pp. 249–254 (2010)

    Google Scholar 

  57. Laffranchi, M., Tsagarakis, N.G., Caldwell, D.G.: Safe human robot interaction via energy regulation control. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 35–41 (2009)

    Google Scholar 

  58. Geravand, M., Shahriari, E., De Luca, A., Peer, A.: Port-based modeling of human-robot collaboration towards safety-enhancing energy shaping control. In: Proceedings - IEEE International Conference on Robotics and Automation, June 2016, pp. 3075–3082 (2016)

    Google Scholar 

  59. Meguenani, A., Padois, V., Da Silva, J., Hoarau, A., Bidaud, P.: Energy based control for safe human-robot physical interaction (2017). https://doi.org/10.1007/978-3-319-50115-4

  60. Yamada, Y., Hirasawa, Y., Huang, S., Umetani, Y., Suita, K.: Human-robot contact in the safeguarding space. IEEE/ASME Trans. Mechatron. 2(4), 230–236 (1997)

    Article  Google Scholar 

  61. Suita, K., Yamada ,Y., Tsuchida, N., Imai, K., Sugimoto, N.: A failure-to-safety “Kyozon” system with simple contact detection and stop capabilities for safe human-autonomous robot coexistence, pp. 3089–3096 (1965)

    Google Scholar 

  62. Wang, L., Wang, X.V., Wang, L., Wang, X.V.: Safety in human-robot collaborative assembly. Cloud-Based Cyber-Phys. Syst. Manuf. (2018). https://doi.org/10.1007/978-3-319-67693-7_9

    Article  Google Scholar 

  63. Dombrowski, U., Stefanak, T., Reimer, A.: Simulation of human-robot collaboration by means of power and force limiting. Procedia Manuf. 17, 134–141 (2018)

    Article  Google Scholar 

  64. Kokkalis, K., Michalos, G., Aivaliotis, P., Makris, S.: An approach for implementing power and force limiting in sensorless industrial robots. Procedia CIRP 76, 138–143 (2018)

    Article  Google Scholar 

  65. Takakura, S., Murakami, T., Ohnishi, K.: Approach to collision detection and recovery motion in industrial robot. In: Proceedings of IECON - Industrial Electronics Conference, vol. 2, pp. 421–426 (1989)

    Google Scholar 

  66. De Luca, A., Flacco, F.: Integrated control for pHRI: collision avoidance, detection, reaction and collaboration. In: Proceedings IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 288–295 (2012)

    Google Scholar 

  67. De Luca, A., Mattone, R.: Sensorless robot collision detection and hybrid force/motion control. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 999–1004 (2005)

    Google Scholar 

  68. Kosuge, K., Matsumoto, T., Morinaga, S.: Collision detection system for manipulator based on adaptive control scheme. Trans. Soc. Instrum. Control Eng. 39(6), 552–558 (2003)

    Article  Google Scholar 

  69. Wu, D., Liu, Q., Xu, W., Liu, A., Zhou, Z., Pham, D.T.: External force detection for physical human-robot interaction using dynamic model identification. In: International Conference on Intelligent Robotics and Applications, October, vol. 10462, pp. 581–592 (2017)

    Google Scholar 

  70. Krüger, J., Nickolay, B., Heyer, P., Seliger, G.: Image based 3D surveillance for flexible man-robot-cooperation. CIRP Ann. 54(1), 19–22 (2005)

    Article  Google Scholar 

  71. Corrales, J.A., Candelas, F.A., Torres, F.: Safe human–robot interaction based on dynamic sphere-swept line bounding volumes. Robot. Comput. Integr. Manuf. 27(1), 177–185 (2011)

    Article  Google Scholar 

  72. Ebert, D.M., Henrich, D.D.: Safe human-robot-cooperation: image-based collision detection for industrial robots. In: IEEE International Conference on Intelligent Robots and Systems, October, vol. 2, pp. 1826–1831 (2002)

    Google Scholar 

  73. Henrich, D., Gecks, T.: Multi-camera collision detection between known and unknown objects. In: 2008 2nd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2008 (2008). https://doi.org/10.1109/icdsc.2008.4635717

  74. Vogel, C., Walter, C., Elkmann, N.: A projection-based sensor system for ensuring safety while grasping and transporting objects by an industrial robot. In: 2015 IEEE International Symposium on Robotics and Intelligent Sensors, pp. 271–277 (2016)

    Google Scholar 

  75. Vogel, C., Walter, C., Elkmann, N.: A projection-based sensor system for safe physical human-robot collaboration. In: IEEE International Conference on Intelligent Robots and Systems, pp. 5359–5364 (2013)

    Google Scholar 

  76. Tan, J.T.C., Arai, T.: Triple stereo vision system for safety monitoring of human-robot collaboration in cellular manufacturing. In: Proceedings - 2011 IEEE International Symposium on Assembly and Manufacturing, ISAM 2011, pp. 1–6 (2011)

    Google Scholar 

  77. Schiavi, R., Bicchi, A., Flacco, F.: Integration of active and passive compliance control for safe human-robot coexistence. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 259–264 (2009)

    Google Scholar 

  78. Fischer, M., Henrich, D.: 3D collision detection for industrial robots and unknown obstacles using multiple depth images. In: Advances in Robotics Research: Theory, Implementation, Application, pp. 111–122 (2009)

    Google Scholar 

  79. Rybski, P., Anderson-Sprecher, P., Huber, D., Niessl, C., Simmons, R.: Sensor fusion for human safety in industrial workcells. In: IEEE International Conference on Intelligent Robots and Systems, pp. 3612–3619 (2012)

    Google Scholar 

  80. Dániel, B., Korondi, P., Thomessen, T.: Joint level collision avoidance for industrial robots. IFAC Proc. 45(22), 655–658 (2012)

    Article  Google Scholar 

  81. Morato, C., Kaipa, K.N., Zhao, B., Gupta, S.K.: Toward safe human robot collaboration by using multiple kinects based real-time human tracking. J. Comput. Inf. Sci. Eng. (2014). https://doi.org/10.1115/1.4025810

  82. Mohammed, A., Schmidt, B., Wang, L.: Active collision avoidance for human–robot collaboration driven by vision sensors. Int. J. Comput. Integr. Manuf. 30(9), 970–980 (2017)

    Article  Google Scholar 

  83. Wang, L., Schmidt, B., Nee, A.Y.C.: Vision-guided active collision avoidance for human-robot collaborations. Manuf. Lett. 1(1), 5–8 (2013)

    Article  Google Scholar 

  84. Graham, J.H.: A fuzzy logic approach for safety and collision avoidance in robotic systems. Int. J. Hum. Factors Manuf. 5(4), 447–457 (1995)

    Article  Google Scholar 

  85. Haddadin, S., Albu-sch, A., De Luca, A., Hirzinger, G.: Collision detection and reaction: a contribution to safe physical human-robot interaction, pp. 22–26 (2008)

    Google Scholar 

  86. De Luca, A., Ferrajoli, L.: Exploiting robot redundancy in collision detection and reaction. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pp. 3299–3305 (2008)

    Google Scholar 

  87. Parusel, S., Haddadin, S., Albu-Schäffer, A.: Modular state-based behavior control for safe human-robot interaction: a lightweight control architecture for a lightweight robot. In: Proceedings - International Conference on Robotics and Automation, pp. 4298–4305 (2011)

    Google Scholar 

  88. Flacco, F., Kroeger, T., De Luca, A., Khatib, O.: A depth space approach for evaluating distance to objects: with application to human-robot collision avoidance. J. Intell. Robot. Syst. Theory. Appl. 80, 7–22 (2015)

    Article  Google Scholar 

  89. Balan, L., Bone, G.M.: Real-time 3D collision avoidance method for safe human and robot coexistence, pp. 276–282 (2006)

    Google Scholar 

  90. Seto, F., Kosuge, K., Hirata, Y.: Self-collision avoidance motion control for human robot cooperation system using RoBE. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pp. 50–55 (2005)

    Google Scholar 

  91. Ratsamee, P., Mae, Y., Ohara, K., Takubo, T., Arai, T.: Human-robot collision avoidance using a modified social force model with body pose and face orientation. Int. J. Humanoid Robot. 10(1), 1–24 (2013)

    Article  Google Scholar 

  92. Polverini, M.P., Zanchettin, A.M., Rocco, P.: Real-time collision avoidance in human-robot interaction based on kinetostatic safety field. In: IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 4136–4141 (2014)

    Google Scholar 

  93. Tamura, Y., Fukuzawa, T., Asama, H.: Smooth collision avoidance in human-robot coexisting environment. In: IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings, pp. 3887–3892 (2010)

    Google Scholar 

  94. Wang, L., Törngren, M., Onori, M.: Current status and advancement of cyber-physical systems in manufacturing. J. Manuf. Syst. 37, 517–527 (2015)

    Article  Google Scholar 

  95. Wang, L., Wang, X.V., Wang, L., Wang, X.V.: Latest advancement in CPS and IoT applications. In: Cloud-Based Cyber-Physical Systems in Manufacturing, pp. 33–61. Springer, Cham (2018)

    Google Scholar 

  96. Zhang, Y., Liu, S., Liu, Y., Yang, H., Li, M., Huisingh, D., Wang, L.: The ‘Internet of Things’ enabled real-time scheduling for remanufacturing of automobile engines. J. Clean. Prod. 185, 562–575 (2018). https://doi.org/10.1016/j.jclepro.2018.02.061

    Article  Google Scholar 

  97. Liu, S., Zhang, Y., Liu, Y., Wang, L., Wang, X.V.: An ‘Internet of Things’ enabled dynamic optimization method for smart vehicles and logistics tasks. J. Clean. Prod. 215, 806–820 (2019)

    Google Scholar 

  98. Liu, S., Zhang, G., Wang, L.: IoT-enabled dynamic optimisation for sustainable reverse logistics. Procedia CIRP 69, 662–667 (2018)

    Google Scholar 

  99. Wang, L., Wang, X.V., Wang, L., Wang, X.V.: Cloud robotics towards a CPS assembly system. In: Cloud-Based Cyber-Physical Systems in Manufacturing, pp. 243–259. Springer, Cham (2018)

    Google Scholar 

  100. Weidner, R., Kong, N., Wulfsberg, J.P.: Human hybrid robot: a new concept for supporting manual assembly tasks. Prod. Eng. 7(6), 675–684 (2013)

    Article  Google Scholar 

  101. Adamson, G., Wang, L., Moore, P.: Feature-based control and information framework for adaptive and distributed manufacturing in cyber physical systems. J. Manuf. Syst. 43, 305–315 (2017)

    Article  Google Scholar 

  102. Wang, X.V., Wang, L., Mohammed, A., Givehchi, M.: Ubiquitous manufacturing system based on Cloud: a robotics application. Robot. Comput. Integr. Manuf. 45, 116–125 (2017)

    Article  Google Scholar 

  103. Liu, H., Wang, L.: Remote human-robot collaboration: a cyber-physical system application for hazard manufacturing environment. J. Manuf. Syst. 54, 24–34 (2019)

    Article  Google Scholar 

  104. Kebria, P.M., Al-Wais, S., Abdi, H., Nahavandi, S.: Kinematic and dynamic modelling of UR5 manipulator. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, pp. 4229–4234 (2017)

    Google Scholar 

  105. Product specification IRB 120

    Google Scholar 

  106. Hashimoto, S., Ishida, A., Inami, M., Igarash, T.: TouchMe: an augmented reality based remote robot manipulation. In: 21st International Conference on Artificial Reality and Telexistence, pp. 1–6 (2011)

    Google Scholar 

  107. Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society (Figure 1), pp. 4754–4759 (2015)

    Google Scholar 

  108. Hernandez, S., Fernando, J., Cotarelo, H.: Multi-master ROS systems (2015)

    Google Scholar 

  109. Pan, Z., Polden, J., Larkin, N., Van Duin, S., Norrish, J.: Recent progress on programming methods for industrial robots. In: Joint International Symposium on Robotics and 6th German Conference on Robotics 2010, ISR/ROBOTIK 2010, vol. 1, pp. 619–626 (2010)

    Google Scholar 

  110. Denkena, B., Denkena, B., Wörn, H., Apitz, R., Bischoff, R., Hein, B., Kowalski, P., Mages, D., Schuler, H.: Roboterprogrammierung in der Fertigung, vol. 9, pp. 656–60. Springer (2005)

    Google Scholar 

  111. Liu, H., Fang, T., Zhou, T., Wang, Y., Wang, L.: Deep learning-based multimodal control interface for human-robot collaboration. Procedia CIRP 72, 3–8 (2018)

    Article  Google Scholar 

  112. Perzylo, A., Somani, N., Profanter, S., Rickert, M., Knoll, A.: Toward efficient robot teach-in and semantic process descriptions for small lot sizes. In: Proceedings of Robotics: Science and Systems (RSS), Workshop on Combining AI Reasoning and Cognitive Science with Robotics, pp. 1–7 (2015)

    Google Scholar 

  113. Cevzar, M., Petrič, T., Babič, J.: Sensor-based loops and branches for playback-programmed robot systems. Mech. Mach. Sci. 49(Raad), 797–804 (2018)

    Google Scholar 

  114. Nippun Kumaar, A.A., Sudarshan, T.S.B.: Mobile robot programming by demonstration. In: International Conference on Emerging Trends on Engineering Science, Technology, ICETET, pp. 206–209 (2011)

    Google Scholar 

  115. Akan, B., Ameri, A., Cürüklü, B., Asplund, L.: Intuitive industrial robot programming through incremental multimodal language and augmented reality. In: Proceedings – IEEE International Conference on Robotics and Automation, pp. 3934–3939 (2011)

    Google Scholar 

  116. Gustavsson, P., Syberfeldt, A., Brewster, R., Wang, L.: Human-robot collaboration demonstrator combining speech recognition and haptic control. Procedia CIRP 63, 396–401 (2017)

    Article  Google Scholar 

  117. Box, P.O., Izoellner, I.: Using gesture and speech control for commanding a robot assistant, pp. 454–459 (2002)

    Google Scholar 

  118. Albu-Schäffer, A., Hirzinger, G.: Cartesian impedance control techniques for torque controlled light-weight robots. In: Proceedings – IEEE International Conference on Robotics and Automation, May, vol. 1, pp. 657–663 (2002)

    Google Scholar 

  119. Grunwald, G., Schreiber, G., Hirzinger, G.: Touch: the direct type of human interaction with a redundant service robot, pp. 347–352 (2001)

    Google Scholar 

  120. Perzanowski, D., Schultz, A.C., Adams, W., Marsh, E., Bugajska, M.: Building a multimodal human-robot interface. IEEE Intell. Syst. Their Appl. 16(1), 16–21 (2001)

    Article  Google Scholar 

  121. Neto, P., Norberto Pires, J., Paulo Moreira, A.: High-level programming and control for industrial robotics: using a hand-held accelerometer-based input device for gesture and posture recognition. Ind. Rob. 37(2), 137–147 (2010)

    Article  Google Scholar 

  122. Kardos, C., Kemény, Z., Kovács, A., Pataki, B.E., Váncza, J.: Context-dependent multimodal communication in human-robot collaboration. Procedia CIRP 72, 15–20 (2018)

    Article  Google Scholar 

  123. Liu, H., Wang, L.: Gesture recognition for human-robot collaboration: a review. Int. J. Ind. Ergon. 68, 355–367 (2018)

    Article  Google Scholar 

  124. Pavlovic, V.I., Sharma, R., Huang, T.S.: Visual interpretation of hand gestures for human-computer interaction a review. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 677–695 (1997)

    Article  Google Scholar 

  125. 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)

    Article  Google Scholar 

  126. Reed, K.B., Peshkin, M.A.: Physical collaboration of human-human and human-robot teams. IEEE Trans. Haptics 1(2), 108–120 (2008)

    Article  Google Scholar 

  127. Huang, S., Ishikawa, M., Yamakawa, Y.: An active assistant robotic system based on high-speed vision and haptic feedback for human-robot collaboration. In: Proceedings IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, vol. 1, pp. 3649–3654 (2018)

    Google Scholar 

  128. Feth, D., Groten, R., Peer, A., Buss, M.: Haptic human-robot collaboration: Comparison of robot partner implementations in terms of human-likeness and task performance. Presence Teleoperators Virtual Environ. 20(2), 173–189 (2011)

    Article  Google Scholar 

  129. Surdilovic, D., Yakut, Y., Nguyen, T.M., Pham, X.B., Vick, A., Martin Martin, R.: Compliance control with dual-arm humanoid robots: design, planning and programming. In: 2010 10th IEEE-RAS International Conference on Humanoid Robots, pp. 275–281 (2010)

    Google Scholar 

  130. Makris, S., Tsarouchi, P., Surdilovic, D., Krüger, J.: Intuitive dual arm robot programming for assembly operations. CIRP Ann. 63(1), 13–16 (2014)

    Article  Google Scholar 

  131. Kousi, N., Michalos, G., Aivaliotis, S., Makris, S.: An outlook on future assembly systems introducing robotic mobile dual arm workers. Procedia CIRP 72, 33–38 (2018)

    Article  Google Scholar 

  132. Ong, S.K., Chong, J.W.S., Nee, A.Y.C.: Methodologies for immersive robot programming in an augmented reality environment. In: Proceedings - Graph 2006 International Conference on Computer Graphics and Interactive Techniques in Australasia and South East Asia, January, pp. 237–244 (2006)

    Google Scholar 

  133. Chong, J.W.S., Ong, S.K., Nee, A.Y.C., Youcef-Youmi, K.: Robot programming using augmented reality: an interactive method for planning collision-free paths. Robot. Comput. Integr. Manuf. 25(3), 689–701 (2009)

    Article  Google Scholar 

  134. Lambrecht, J., Kleinsorge, M., Rosenstrauch, M., Krüger, J.: Spatial programming for industrial robots through task demonstration. Int. J. Adv. Robot. Syst. (2013). https://doi.org/10.5772/55640

    Article  Google Scholar 

  135. Lambrecht, J., Kruger, J.: Spatial programming for industrial robots based on gestures and Augmented Reality. In: IEEE International Conference on Intelligent Robots and Systems, pp. 466–472 (2012)

    Google Scholar 

  136. Ji, W., Wang, Y., Liu, H., Wang, L.: Interface architecture design for minimum programming in human-robot collaboration. Procedia CIRP 72, 129–134 (2018)

    Article  Google Scholar 

  137. Tsarouchi, P., Athanasatos, A., Makris, S., Chatzigeorgiou, X., Chryssolouris, G.: High level robot programming using body and hand gestures. Procedia CIRP 55, 1–5 (2016)

    Article  Google Scholar 

  138. Rozo, L., Calinon, S., Caldwell, D.G., Jiménez, P., Torras, C.: Learning physical collaborative robot behaviors from human demonstrations. IEEE Trans. Robot. 32(3), 513–527 (2016)

    Article  Google Scholar 

  139. Lamy, X., Collédani, F., Geffard, F., Measson, Y., Morel, G.: Human force amplification with industrial robot: study of dynamic limitations. In: IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings, pp. 2487–2494 (2010)

    Google Scholar 

  140. Yao, B., Zhou, Z., Wang, L., Xu, W., Liu, Q., Liu, A.: Sensorless and adaptive admittance control of industrial robot in physical human−robot interaction. In: Robotics and Computer-Integrated Manufacturing, November 2017, vol. 51, pp. 158–168 (2018)

    Google Scholar 

  141. Sanfilippo, F., Hatledal, L.I., Zhang, H., Fago, M., Pettersen, K.Y.: Controlling Kuka industrial robots: flexible communication interface JOpenShowVar. IEEE Robot. Autom. Mag. 22(4), 96–109 (2015)

    Article  Google Scholar 

  142. Landi, C.T., Ferraguti, F., Sabattini, L., Secchi, C., Fantuzzi, C.: Admittance control parameter adaptation for physical human-robot interaction, pp. 2911–2916 (2017)

    Google Scholar 

  143. Cherubini, A., Passama, R., Meline, A., Crosnier, A., Fraisse, P.: Multimodal control for human-robot cooperation. In: IEEE International Conference on Intelligent Robots and Systems, pp. 2202–2207 (2013)

    Google Scholar 

  144. Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  145. Silver, D., Schrittwieser, J., Simonyan, K., et al.: Mastering the game of Go without human knowledge. Nature 550(7676), 354–359 (2017)

    Article  Google Scholar 

  146. Deng, L., Yu, D., Dahl, G.E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., Kingsbury, B.: IEEE Signal Process. Mag. 24(99), c1–c1 (2008)

    Google Scholar 

  147. Gonzalez, T.F.: ImageNet classification with deep convolutional neural networks. In: Handbook of Approximation Algorithms and Metaheuristics, pp. 1–1432 (2007)

    Google Scholar 

  148. Hershey, S., Chaudhuri, S., Ellis, D.P.W., et al.: CNN architectures for large-scale audio classification. In: ICASSP, IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, pp. 131–135 (2017)

    Google Scholar 

  149. Abdel-Hamid, O., Mohamed, A.R., Jiang, H., Penn, G.: Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. In: ICASSP, IEEE International Conference on Acoustics, Speech, and Signal Processing – Proceedings, July 2015, pp. 4277–4280 (2012)

    Google Scholar 

  150. Hochreiter, S.: Long short-term. Memory 1780, 1735–1780 (1997)

    Google Scholar 

  151. Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling. In: 13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012, vol. 1, pp. 194–197 (2012)

    Google Scholar 

  152. Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y., Shen, L., Xie, X.: Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks. In: 30th AAAI Conference on Artificial Intelligence, AAAI 2016, vol. ii, pp. 3697–3703 (2016)

    Google Scholar 

  153. Liu, H., Fang, T., Zhou, T., Wang, L.: Towards robust human-robot collaborative manufacturing: multimodal fusion. IEEE Access 6, 74762–74771 (2018)

    Article  Google Scholar 

  154. Zhang, H., McLoughlin, I., Song, Y.: Robust sound event recognition using convolutional neural networks. In: ICASSP, IEEE International Conference on Acoustics, Speech, and Signal Processing – Proceedings, 2015 August, pp. 559–563 (2015)

    Google Scholar 

  155. Jarrett, K., Kavukcuoglu, K., Ranzato, M., LeCun, Y.: What is the best multi-stage architecture for object recognition? In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2146–2153 (2009)

    Google Scholar 

  156. Boureau, Y., Bach, F.: Learning mid-level features for recognition (2010)

    Google Scholar 

  157. van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 1, 1–48 (2008)

    MATH  Google Scholar 

  158. Weichert, F., Bachmann, D., Rudak, B., Fisseler, D.: Analysis of the accuracy and robustness of the Leap Motion Controller. Sensors (Switzerland) 13(5), 6380–6393 (2013)

    Article  Google Scholar 

  159. Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 1717–1724 (2004)

    Google Scholar 

  160. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)

    Article  Google Scholar 

  161. Wang, P., Liu, H., Wang, L., Gao, R.X.: Deep learning-based human motion recognition for predictive context-aware human-robot collaboration. CIRP Ann. 67(1), 17–20 (2018)

    Article  Google Scholar 

  162. Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: DeCAF: a deep convolutional activation feature for generic visual recognition. 31st International Conference on Machine Learning, ICML 2014, pp. 988–996 (2014)

    Google Scholar 

  163. Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: Proceedings 28th International Conference on Machine Learning, ICML 2011, pp. 689–696 (2011)

    Google Scholar 

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Wang, L., Liu, S., Liu, H., Wang, X.V. (2020). Overview of Human-Robot Collaboration in Manufacturing. In: Wang, L., Majstorovic, V., Mourtzis, D., Carpanzano, E., Moroni, G., Galantucci, L. (eds) Proceedings of 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-46212-3_2

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