Human–robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017

References

• Krüger J, Lien TK, Verl A. Cooperation of human and machines in assembly lines. CIRP Ann Manuf Technol. 2009;58(2):628–646.
   Web of Science ®Google Scholar
• Angerer A, Hoffmann A, Schierl A, et al. Robotics API: object-oriented software development for industrial robots. J Softw Eng Robot. 2013;4(1):1–22.
   Google Scholar
• Hvilshøj M, Bøgh S, Madsen O, et al. The mobile robot ‘little helper’: concepts, ideas and working principles. 2009 IEEE Conference on Emerging Technologies & Factory Automation. ETFA 2009. IEEE; 2009. p. 1–4.
   Google Scholar
• Hvilshøj M, Bøgh S, Nielsen OS, et al. Autonomous industrial mobile manipulation (AIMM): past, present and future. Ind Robot Int J. 2012;39(2):120–135.
   Web of Science ®Google Scholar
• Akli I, Bouzouia B. Time-dependent trajectory generation for tele-operated mobile manipulator. 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT). IEEE; 2015. p. 1–5.
   Google Scholar
• Duffy BR. Anthropomorphism and the social robot. Rob Auton Syst. 2003;42(3–4):177–190.
   Web of Science ®Google Scholar
• Schmidtler J, Knott V, Hölzel C, et al. Human centered assistance applications for the working environment of the future. Occup Ergon. 2015;12(3):83–95.
   Google Scholar
• Bortot DF. Ergonomic human-robot coexistence in the branch of production [PhD thesis]. Technische Universität München; 2014.
   Google Scholar
• Fang H, Ong S, Nee A. A novel augmented reality-based interface for robot path planning. Int J Interact Des Manuf (IJIDeM). 2014;8(1):33–42.
   Google Scholar
• Chandrasekaran B, Conrad JM. Human-robot collaboration: a survey. SoutheastCon 2015. IEEE; 2015. p. 1–8.
   Google Scholar
• Vaughan B, Han JG, Kilmartin E, et al. Designing and implementing a platform for collecting multi-modal data of human-robot interaction. Acta Polytech Hung. 2012;9(1):7–17.
   Web of Science ®Google Scholar
• Stadler S, Weiss A, Mirnig N, et al. Anthropomorphism in the factory: a paradigm change? Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction. IEEE Press; 2013. p. 231–232.
   Google Scholar
• Solvang B, Sziebig G. On industrial robots and cognitive info-communication. 2012 IEEE 3rd International Conference on Cognitive Infocommunications (CogInfoCom). IEEE; 2012. p. 459–464.
   Google Scholar
• Dániel B, Korondi P, Sziebig G, et al. Evaluation of flexible graphical user interface for intuitive human robot interactions. Acta Polytech Hung. 2014;11(1):135–151.
   Web of Science ®Google Scholar
• Bengler K, Zimmermann M, Bortot D, et al. Interaction principles for cooperative human-machine systems. IT-Inform Technol. 2012;54(4):157–164.
   Google Scholar
• Bicchi A, Peshkin MA, Colgate JE. Safety for physical human–robot interaction. In: Siciliano B, Khatib O, editor. Springer handbook of robotics. Heidelberg: Springer; 2008. p. 1335–1348.
   Google Scholar
• Andrisano AO, Leali F, Pellicciari M, et al. Hybrid reconfigurable system design and optimization through virtual prototyping and digital manufacturing tools. Int J Interact Des Manuf (IJIDeM). 2012;6(1):17–27.
   Google Scholar
• Schiavi R, Bicchi A, Flacco F. Integration of active and passive compliance control for safe human-robot coexistence. 2009 IEEE International Conference on Robotics and Automation. IEEE; 2009. p. 259–264.
   Google Scholar
• Shen Y, Reinhart G, Tseng M. A design approach for incorporating task coordination for human-robot-coexistence within assembly systems. 2015 Annual IEEE Systems Conference (SysCon) Proceedings. IEEE; 2015. p. 426–431.
   Google Scholar
• Gaz C, Magrini E, De Luca A. A model-based residual approach for human-robot collaboration during manual polishing operations. Mechatronics. 2018;55:234–247.
   Web of Science ®Google Scholar
• De Luca A, Flacco F. Integrated control for PHRI: collision avoidance, detection, reaction and collaboration. 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob). IEEE; 2012. p. 288–295.
   Google Scholar
• Wang N, Zeng Y, Geng J. A brief review on safety strategies of physical human-robot interaction. ITM Web of Conferences. Vol. 25. EDP Sciences; 2019. p. 01015.
   Google Scholar
• Flacco F, Kroeger T, De Luca A, et al. A depth space approach for evaluating distance to objects. J Intell Robot Syst. 2015;80(1):7–22.
   Web of Science ®Google Scholar
• Cherubini A, Passama R, Meline A, et al. Multimodal control for human-robot cooperation. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE; 2013. p. 2202–2207.
   Google Scholar
• Mansard N, Khatib O, Kheddar A. A unified approach to integrate unilateral constraints in the stack of tasks. IEEE Trans Robot. 2009;25(3):670–685.
   Web of Science ®Google Scholar
• Mörtl A, Lawitzky M, Kucukyilmaz A, et al. The role of roles: physical cooperation between humans and robots. Int J Rob Res. 2012;31(13):1656–1674.
   Web of Science ®Google Scholar
• Mainprice J, Sisbot EA, Siméon T, et al. Planning safe and legible hand-over motions for human-robot interaction. IARP/IEEE-RAS/EURON Workshop on Technical Challenges for Dependable Robots in Human Environments. 2010.
   Google Scholar
• Fujii M, Murakami H, Sonehara M. Study on application of a human-robot collaborative system using hand-guiding in a production line. IHI Eng Rev. 2016;49(1):24–29.
   Google Scholar
• Jarrassé N, Charalambous T, Burdet E. A framework to describe, analyze and generate interactive motor behaviors. PLoS ONE. 2012;7(11):e49945.
   PubMed Web of Science ®Google Scholar
• Akella P, Peshkin M, Colgate E, et al. Cobots for the automobile assembly line. 1999 IEEE International Conference on Robotics and Automation; 1999. Proceedings. Vol. 1. IEEE; 1999. p. 728–733.
   Google Scholar
• Peshkin M, Colgate JE. Cobots. Ind Robot Int J. 1999;26(5):335–341.
   Web of Science ®Google Scholar
• Brending S, Lawo M, Pannek J, et al. Certifiable software architecture for human robot collaboration in industrial production environments. IFAC-PapersOnLine. 2017;50(1):1983–1990.
   Google Scholar
• Universal robots. 2017. Available from: https://www.universal-robots.com/products/ur5-robot/
   Google Scholar
• Bluethmann W, Ambrose R, Diftler M, et al. Robonaut: a robot designed to work with humans in space. Auton Robots. 2003;14(2–3):179–197.
   PubMed Web of Science ®Google Scholar
• Soratana T, Balakuntala MV, Abbaraju P, et al. Glovebox handling of high-consequence materials with super baxter and gesture-based programming – 18598. Waste Management (WM 2018), 44th International Symposium on; Phoenix, AZ, USA; 2018. p. 1–14.
   Google Scholar
• Kuka robotics. 2017. Available from: http://www.kuka-robotics.com/
   Google Scholar
• Bitonneau D, Moulières-Seban T, Dumora J, et al. Human-centered design of an interactive industrial robot system through participative simulations: application to a pyrotechnic tank cleaning workstation. 26th IEEE International Symposium on Robot and Human Interactive Communication; Lisbon, Portugal; 2017.
   Google Scholar
• Ge JG. Programming by demonstration by optical tracking system for dual arm robot. IEEE ISR 2013. IEEE; 2013. p. 1–7.
   Google Scholar
• Stenmark M, Haage M, Topp EA, et al. Supporting semantic capture during kinesthetic teaching of collaborative industrial robots. Int J Semant Comput. 2018;12(1):167–186.
   Web of Science ®Google Scholar
• Stenmark M, Malec J. Knowledge-based instruction of manipulation tasks for industrial robotics. Robot Comput Integr Manuf. 2015;33:56–67.
   Web of Science ®Google Scholar
• Klotzbücher M, Bruyninckx H. Coordinating robotic tasks and systems with rfsm statecharts. J Softw Eng Robot. 2012;3(1):28–56.
   Google Scholar
• Gammieri L, Schumann M, Pelliccia L, et al. Coupling of a redundant manipulator with a virtual reality environment to enhance human-robot cooperation. Procedia CIRP. 2017;62:618–623.
   Google Scholar
• Apas: humans and machines hand in hand. 2018. Available from: https://www.bosch-apas.com/en/home/
   Google Scholar
• Tsarouchi P, Makris S, Michalos G, et al. Ros based coordination of human robot cooperative assembly tasks-an industrial case study. Procedia CIRP. 2015;37:254–259.
   Google Scholar
• Krüger J, Wang L, Verl A, et al. Innovative control of assembly systems and lines. CIRP Ann. 2017;66(2):707–730.
   Web of Science ®Google Scholar
• Unhelkar VV, Siu HC, Shah JA. Comparative performance of human and mobile robotic assistants in collaborative fetch-and-deliver tasks. 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE; 2014. p. 82–89.
   Google Scholar
• De Luca A, Albu-Schaffer A, Haddadin S, et al. Collision detection and safe reaction with the dlr-iii lightweight manipulator arm. 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE; 2006. p. 1623–1630.
   Google Scholar
• Haddadin S, Albu-Schäffer A, Hirzinger G. Requirements for safe robots: measurements, analysis and new insights. Int J Rob Res. 2009;28(11–12):1507–1527.
   Web of Science ®Google Scholar
• Geravand M, Flacco F, De Luca A. Human-robot physical interaction and collaboration using an industrial robot with a closed control architecture. 2013 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2013. p. 4000–4007.
   Google Scholar
• Haddadin S, Albu-Schaffer A, Frommberger M, et al. The ‘DLR crash report’: towards a standard crash-testing protocol for robot safety – part II: discussions. 2009 IEEE International Conference on Robotics and Automation. ICRA'09. IEEE; 2009. p. 280–287.
   Google Scholar
• Albu-Schäffer A, Haddadin S, Ott C, et al. The DLR lightweight robot: design and control concepts for robots in human environments. Ind Robot Int J. 2007;34(5):376–385.
   Web of Science ®Google Scholar
• Haddadin S, Albu-Schaffer A, De Luca A, et al. Collision detection and reaction: a contribution to safe physical human-robot interaction. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS 2008. IEEE; 2008. p. 3356–3363.
   Google Scholar
• Bischoff R, Kurth J, Schreiber G, et al. The kuka-dlr lightweight robot arm-a new reference platform for robotics research and manufacturing. ISR 2010 (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics). VDE; 2010. p. 1–8.
   Google Scholar
• Scholtz J. Theory and evaluation of human robot interactions. 2003 Proceedings of the 36th Annual Hawaii International Conference on System Sciences. IEEE; 2003. p. 10–pp.
   Google Scholar
• Modares H, Ranatunga I, Lewis FL, et al. Optimized assistive human–robot interaction using reinforcement learning. IEEE Trans Cybern. 2016;46(3):655–667.
   PubMed Web of Science ®Google Scholar
• Müller R, Vette M, Mailahn O. Process-oriented task assignment for assembly processes with human-robot interaction. Procedia CIRP. 2016;44:210–215.
   Google Scholar
• Dimoulas KN. Human robot collaboration (HRC) for assembly operations using industrial robots [PhD thesis]. University of Patras, Department of Mechanical Engineering & Aeronautics, Laboratory for Manufacturing Systems and Automation (LMS); 2017.
   Google Scholar
• Djuric AM, Urbanic R, Rickli J. A framework for collaborative robot (cobot) integration in advanced manufacturing systems. SAE Int J Mater Manuf. 2016;9(2):457–464.
   Web of Science ®Google Scholar
• Fogarty J, Hudson SE, Atkeson CG, et al. Predicting human interruptibility with sensors. ACM Trans Comput Hum Int (TOCHI). 2005;12(1):119–146.
   Google Scholar
• Shravani NK, Rao SB. Introducing robots without creating fear of unemployment and high cost in industries. Int J Eng Technol Sci Res. 2018;5(1):1128–1138.
   Google Scholar
• De Santis A. Modelling and control for human-robot interaction: physical and cognitive aspects. 2008 IEEE International Conference on Robotics and Automation. IEEE; 2008.
   Google Scholar
• Moulières-Seban T, Bitonneau D, Salotti JM, et al. Human factors issues for the design of a cobotic system. In: Savage-Knepshield P, Chen J, editor. Advances in human factors in robots and unmanned systems. Cham: Springer International Publishing; 2017. p. 375–385.
   Google Scholar
• Moulières-Seban T, Salotti JM, Claverie B, et al. Classification of cobotic systems for industrial applications. Ingénierie Cognit. 2018;1(1):1–8.
   Google Scholar
• Moulières-Seban T, Salotti JM, Claverie B, et al. Classification of cobotic systems for industrial applications. The 6th Workshop Towards a Framework for Joint Action; 2015 Oct. p. 1–6.
   Google Scholar
• Tolós Pons N. Standarization in human robot interaction [PhD thesis]. Universitat Politècnica de Catalunya; 2013.
   Google Scholar
• Restrepo SS, Raiola G, Chevalier P, et al. Iterative virtual guides programming for human-robot comanipulation. 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). IEEE; 2017. p. 9p.
   Google Scholar
• Koch PJ, Van Amstel MK, Debska P, et al. A skill-based robot co-worker for industrial maintenance tasks. Procedia Manuf. 2017;11:83–90.
   Google Scholar
• Meziane R, Li P, Otis MJD, et al. Safer hybrid workspace using human-robot interaction while sharing production activities. 2014 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Proceedings. IEEE; 2014.
   Google Scholar
• Liang YS, Pellier D, Fiorino H, et al. A framework for robot programming in cobotic environments: first user experiments. Proceedings of the 3rd International Conference on Mechatronics and Robotics Engineering. ACM; 2017. p. 30–35.
   Google Scholar
• Bo H, Mohan DM, Azhar M, et al. Human-robot collaboration for tooling path guidance. 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob). IEEE; 2016. p. 1340–1345.
   Google Scholar
• Peshkin MA, Colgate JE, Wannasuphoprasit W, et al. Cobot architecture. IEEE Trans Rob Autom. 2001;17(4):377–390.
   Google Scholar
• Gambao E, Hernando M, Surdilovic D. A new generation of collaborative robots for material handling. ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction. Vol. 29. Vilnius Gediminas Technical University, Department of Construction Economics & Property; 2012. p. 1.
   Google Scholar
• Bernhardt R, Surdilovic D, Katschinski V, et al. Next generation of flexible assembly systems. In: Azevedo A, editor. Innovation in manufacturing networks. Boston, MA: Springer US; 2008. p. 279–288.
   Google Scholar
• Bortolini M, Ferrari E, Gamberi M, et al. Assembly system design in the industry 4.0 era: a general framework. IFAC-PapersOnLine. 2017;50(1):5700–5705.
   Google Scholar
• She Y, Su HJ, Lai C, et al. Design and prototype of a tunable stiffness arm for safe human-robot interaction. ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers; 2016. p. V05BT07A063–V05BT07A063.
   Google Scholar
• Ferland F, Reveleau A, Leconte F, et al. Coordination mechanism for integrated design of human-robot interaction scenarios. Paladyn J Behav Robot. 2017;8(1):100–111.
   Google Scholar
• Karolak F, Le Scouarnec N, Campana M, et al. Le développement industriel futur de la robotique personnelle et de service en france. 2012. (Tech Rep).
   Google Scholar
• Alvarez-de-los Mozos E, Renteria A. Collaborative robots in e-waste management. Procedia Manuf. 2017;11:55–62.
   Google Scholar
• Maurice P, Schlehuber P, Padois V, et al. Automatic selection of ergonomie indicators for the design of collaborative robots: a virtual-human in the loop approach. 2014 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids). IEEE; 2014. p. 801–808.
   Google Scholar
• Maurice P, Padois V, Measson Y, et al. Human-oriented design of collaborative robots. Int J Ind Ergon. 2017;57:88–102.
   Web of Science ®Google Scholar
• Amoretti M, Reggiani M. Architectural paradigms for robotics applications. Adv Eng Inform. 2010;24(1):4–13.
   Web of Science ®Google Scholar
• Unland R. Chapter 2 – Industrial agents. In: Leitão P, Karnouskos S, editor. Industrial agents. Boston: Morgan Kaufmann; 2015. p. 23–44.
   Google Scholar
• Leitão P, Vrba P. Recent developments and future trends of industrial agents. International Conference on Industrial Applications of Holonic and Multi-Agent Systems. Springer; 2011. p. 15–28.
   Google Scholar
• Leitão P, Inden U, Rückemann CP. Parallelising multi-agent systems for high performance computing. Third International Conference on Advanced Communications and Computation (INFOCOMP13). Vol. 6. IARIA; 2013. p. 1.
   Google Scholar
• Leitão P, Mařík V, Vrba P. Past, present, and future of industrial agent applications. IEEE Trans Ind Inform. 2013;9(4):2360–2372.
   Web of Science ®Google Scholar
• Leitão P. Agent-based distributed manufacturing control: a state-of-the-art survey. Eng Appl Artif Intell. 2009;22(7):979–991.
   Web of Science ®Google Scholar
• Feng C, Xiao Y, Willette A, et al. Vision guided autonomous robotic assembly and as-built scanning on unstructured construction sites. Autom Constr. 2015;59:128–138.
   Web of Science ®Google Scholar
• Fabrizio F, De Luca A. Real-time computation of distance to dynamic obstacles with multiple depth sensors. IEEE Robot Autom Lett. 2016;2(1):56–63.
   Web of Science ®Google Scholar
• Magyar G, Cadrik T, Vircikova M, et al. Towards adaptive cloud-based platform for robotic assistants in education. 2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE; 2014. p. 285–289.
   Google Scholar
• Magyar G, Vircikova M. Proposal of a cloud-based agent for social human-robot interaction that learns from the human experimenters. 2015 IEEE 19th International Conference on Intelligent Engineering Systems (INES). IEEE; 2015. p. 107–111.
   Google Scholar
• Vick A, Surdilovic D, Dräger AK, et al. The industrial robot as intelligent tool carrier for human-robot interactive artwork. 2014 Ro-Man: The 23rd IEEE International Symposium on Robot and Human Interactive Communication. IEEE; 2014. p. 880–885.
   Google Scholar
• Vick A, Vonásek V, Penicka R, et al. Robot control as a service-towards cloud-based motion planning and control for industrial robots. RoMoCo. 2015;15:33–39.
   Google Scholar
• Stenmark M, Malec J, Nilsson K, et al. On distributed knowledge bases for robotized small-batch assembly. IEEE Trans Autom Sci Eng. 2015;12(2):519–528.
   Web of Science ®Google Scholar
• De Benedictis C, Franco W, Maffiodo D, et al. Control of force impulse in human-machine impact. International Conference on Robotics in Alpe-Adria Danube Region. Springer; 2017. p. 956–964.
   Google Scholar
• Gaz C, Flacco F, De Luca A. Identifying the dynamic model used by the KUKA LWR: a reverse engineering approach. 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2014. p. 1386–1392.
   Google Scholar
• Rozo L, Bruno D, Calinon S, et al. Learning optimal controllers in human-robot cooperative transportation tasks with position and force constraints. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2015. p. 1024–1030.
   Google Scholar
• Navarro B, Cherubini A, Fonte A, et al. An iso10218-compliant adaptive damping controller for safe physical human-robot interaction. 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2016. p. 3043–3048.
   Google Scholar
• International Organization for Standardization S Geneva. ISO 10218-2:2011 robots and robotic devices – safety requirements for industrial robots – part 2: robot systems and integration. 2016. (Tech Rep).
   Google Scholar
• Waurzyniak P. Fast, lightweight robots help factories go faster. Manuf Eng. 2015;154(3):55–64.
   Web of Science ®Google Scholar
• Robla-Gómez S, Becerra VM, Llata JR, et al. Working together: a review on safe human-robot collaboration in industrial environments. IEEE Access. 2017;5:26754–26773.
   Web of Science ®Google Scholar
• Vasic M, Billard A. Safety issues in human-robot interactions. 2013 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2013. p. 197–204.
   Google Scholar
• Guiochet J. Hazard analysis of human–robot interactions with hazop–uml. Saf Sci. 2016;84:225–237.
   Web of Science ®Google Scholar
• De Santis A, Siciliano B. Safety issues for human-robot cooperation in manufacturing systems. Tools and Perspectives in Virtual Manufacturing. VRTest 2008; 2008.
   Google Scholar
• Morato C, Kaipa KN, Zhao B, et al. Toward safe human robot collaboration by using multiple kinects based real-time human tracking. J Comput Inf Sci Eng. 2014;14(1):011006.
   Web of Science ®Google Scholar
• Tan JTC, Duan F, Zhang Y, et al. Safety design and development of human-robot collaboration in cellular manufacturing. 2009 IEEE International Conference on Automation Science and Engineering. CASE 2009. IEEE; 2009. p. 537–542.
   Google Scholar
• Bobka P, Germann T, Heyn JK, et al. Simulation platform to investigate safe operation of human-robot collaboration systems. Procedia CIRP. 2016;44:187–192.
   Google Scholar
• Courreges F, Laribi MA, Arsicault M, et al. Designing a biomimetic model of non-linear elastic safety mechanism for collaborative robots. 2016 IEEE 14th International Conference on Industrial Informatics (INDIN). IEEE; 2016. p. 231–236.
   Google Scholar
• She Y, Su HJ, Hurd CJ. Shape optimization of 2D compliant links for design of inherently safe robots. ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers; 2015. p. V05BT08A004–V05BT08A004.
   Google Scholar
• Meng D, Song S, Wang J. Design and modeling of a compliant link for inherently safe corobots. J Mech Robot. 2017;2017:1–14.
   Google Scholar
• Weitschat R, Vogel J, Lantermann S, et al. End-effector airbags to accelerate human-robot collaboration. 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2017. p. 2279–2284.
   Google Scholar
• Vanderborght B, Albu-Schäffer A, Bicchi A, et al. Variable impedance actuators: a review. Rob Auton Syst. 2013;61(12):1601–1614.
   Web of Science ®Google Scholar
• Tagliamonte NL, Sergi F, Accoto D, et al. Double actuation architectures for rendering variable impedance in compliant robots: a review. Mechatronics. 2012;22(8):1187–1203.
   Web of Science ®Google Scholar
• Walker DS, Thoma DJ, Niemeyer G. Variable impedance magnetorheological clutch actuator and telerobotic implementation. 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS 2009. IEEE; 2009. p. 2885–2891.
   Google Scholar
• Wolf S, Hirzinger G. A new variable stiffness design: matching requirements of the next robot generation. 2008 IEEE International Conference on Robotics and Automation. ICRA 2008. IEEE; 2008. p. 1741–1746.
   Google Scholar
• Groothuis S, Carloni R, Stramigioli S. A novel variable stiffness mechanism capable of an infinite stiffness range and unlimited decoupled output motion. Actuators. Vol. 3. Multidisciplinary Digital Publishing Institute; 2014. p. 107–123.
   Google Scholar
• Ayoubi Y, Laribi MA, Courrèges F, et al. A complete methodology to design a safety mechanism for prismatic joint implementation. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2016. p. 304–309.
   Google Scholar
• Fenucci A, Indri M, Romanelli F. A real time distributed approach to collision avoidance for industrial manipulators. 2014 IEEE Emerging Technology and Factory Automation (ETFA). IEEE; 2014. p. 1–8.
   Google Scholar
• Flacco F, De Luca A. Multiple depth/presence sensors: integration and optimal placement for human/robot coexistence. 2010 IEEE International Conference on Robotics and Automation. IEEE; 2010. p. 3916–3923.
   Google Scholar
• Flacco F, Kröger T, De Luca A, et al. A depth space approach to human-robot collision avoidance. 2012 IEEE International Conference on Robotics and Automation. IEEE; 2012. p. 338–345.
   Google Scholar
• Najmaei N, Kermani MR. Prediction-based reactive control strategy for human-robot interactions. 2010 IEEE International Conference on Robotics and Automation. IEEE; 2010. p. 3434–3439.
   Google Scholar
• Antonelli G, Chiaverini S, Perna V, et al. A modular and task-oriented architecture for open control system: the evolution of c5g open towards high level programming. IEEE 2010 International Conference on Robotics and Automation, Workshop on Innovative Robot Control Architectures for Demanding (Research) Applications, Anchorage, Alaska. 2010.
   Google Scholar
• Quigley M, Conley K, Gerkey B, et al. Ros: an open-source robot operating system. ICRA Workshop on Open Source Software. Vol. 3; Kobe, Japan; 2009. p. 5.
   Google Scholar
• Rhoden B, Klues K, Waterman A, et al. ROS: a scalable operating system for parallel applications on many-core architectures. Research Supported by Microsoft Award# 024263 and Intel Award# 024894. 2013.
   Google Scholar
• Indri M, Trapani S, Lazzero I. A general procedure for collision detection between an industrial robot and the environment. 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA). IEEE; 2015. p. 1–8.
   Google Scholar
• Bøgh S, Hvilshøj M, Kristiansen M, et al. Identifying and evaluating suitable tasks for autonomous industrial mobile manipulators (AIMM). Int J Adv Manuf Technol. 2012;61(5–8):713–726.
   Web of Science ®Google Scholar
• Mantegazza P. Diapm RTAI for linux: whys, whats and hows. Real-Time Linux Workshop, Conference; 1999 Dec.
   Google Scholar
• Balkan T, Ozgoren MK, Arikan MS. Structure based classification and kinematic analysis of six-joint industrial robotic manipulators. In: Cubero S, editor. Industrial robotics: theory, modelling and control, InTech Europe, Croatia. Mammendorf, Germany: Pro Literatur Verlag/Linz, Austria: ARS; 2006.
   Google Scholar
• Ibari B, Bouzgou K, Ahmed-Foitih Z, et al. An application of augmented reality (AR) in the manipulation of fanuc 200ic robot. 2015 Fifth International Conference on Innovative Computing Technology (INTECH). IEEE; 2015. p. 56–60.
   Google Scholar
• Suárez-Ruiz F, Pham QC. A framework for fine robotic assembly. Preprint 2015. arXiv:150904806.
   Google Scholar
• Avanzini GB, Ceriani NM, Zanchettin AM, et al. Safety control of industrial robots based on a distributed distance sensor. IEEE Trans Control Syst Technol. 2014;22(6):2127–2140.
   Web of Science ®Google Scholar
• Ragaglia M, Bascetta L, Rocco P, et al. Integration of perception, control and injury knowledge for safe human-robot interaction. 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2014. p. 1196–1202.
   Google Scholar
• Kevin BK. White paper safe collaboration with abb robots, electronic position switch and safemove. ABB, 2008. 2008.
   Google Scholar
• Shin D, Sardellitti I, Park YL, et al. Design and control of a bio-inspired human-friendly robot. Int J Rob Res. 2010;29(5):571–584.
   Web of Science ®Google Scholar
• Mumm J, Mutlu B. Human-robot proxemics: physical and psychological distancing in human-robot interaction. Proceedings of the 6th International Conference on Human-Robot Interaction. ACM; 2011. p. 331–338.
   Google Scholar
• Ogorodnikova O. How safe the human-robot coexistence is? Theoretical presentation. Acta Polytech Hung. 2009;6(4):51–74.
   Web of Science ®Google Scholar
• Lasota PA, Fong T, Shah JA, et al. A survey of methods for safe human-robot interaction. Found Trends${}^{\text{®}}$ Robot. 2017;5(4):261–349.
   Google Scholar
• Quarta D, Pogliani M, Polino M, et al. An experimental security analysis of an industrial robot controller. 2017 38th IEEE Symposium on Security and Privacy (SP). IEEE; 2017. p. 268–286.
   Google Scholar
• Haddadin S, Albu-Schaffer A, Hirzinger G. The role of the robot mass and velocity in physical human-robot interaction-part I: non-constrained blunt impacts. 2008 IEEE International Conference on Robotics and Automation. ICRA 2008. IEEE; 2008. p. 1331–1338.
   Google Scholar
• Haddadin S, Haddadin S, Khoury A, et al. A truly safely moving robot has to know what injury it may cause. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2012. p. 5406–5413.
   Google Scholar
• Matthias B, Kock S, Jerregard H, et al. Safety of collaborative industrial robots: certification possibilities for a collaborative assembly robot concept. 2011 IEEE International Symposium on Assembly and Manufacturing (ISAM). IEEE; 2011. p. 1–6.
   Google Scholar
• Tan Y, Vuran MC, Goddard S. Spatio-temporal event model for cyber-physical systems. 2009 29th IEEE International Conference on Distributed Computing Systems Workshops. ICDCS Workshops' 09. IEEE; 2009. p. 44–50.
   Google Scholar
• Vladareanu V, Dumitrache I, Vladareanu L, et al. Versatile intelligent portable robot control platform based on cyber physical systems principles. Stud Inf Control. 2015;24(4):409–418.
   Web of Science ®Google Scholar
• Lee EA, Seshia SA. Introduction to embedded systems: a cyber-physical systems approach. Cambridge, MA: Mit Press; 2016.
   Google Scholar
• Hentout A, Hamdania A, Kachouane H, et al. Multi-agent control architecture for RFID cyber-physical robotic systems: initial validation of tagged objects detection and identification using player/stage. 2016 Global Information Infrastructure and Networking Symposium (GIIS). IEEE; 2016. p. 1–6.
   Google Scholar
• Korthals T, Krause T, Rückert U. Evidence grid based information fusion for semantic classifiers in dynamic sensor networks. In: Niggemann O, Beyerer J, Machine learning for cyber physical systems. Berlin: Springer; 2016. p. 9–14.
   Google Scholar
• Perez JB, Arrieta AG, Hernández Encinas A, et al. Industrial cyber-physical systems in textile engineering. International Joint Conference SOCO'16-CISIS'16-ICEUTE'16. Springer; 2016. p. 126–135.
   Google Scholar
• Khalid A, Kirisci P, Ghrairi Z, et al. Safety requirements in collaborative human–robot cyber-physical system. In: Freitag M, Kotzab H, Pannek, J, editor. Dynamics in logistics. Cham: Springer International Publishing; 2017. p. 41–51.
   Google Scholar
• Vogel C, Fritzsche M, Elkmann N. Safe human-robot cooperation with high-payload robots in industrial applications. 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE; 2016. p. 529–530.
   Google Scholar
• Bogue R. Europe continues to lead the way in the collaborative robot business. Ind Robot Int J. 2016;43(1):6–11.
   Web of Science ®Google Scholar
• Schmidt B, Wang L. Contact-less and programming-less human-robot collaboration. Procedia CIRP. 2013;7:545–550.
   Google Scholar
• Backman B, et al. Simulating human-robot collaboration: -an example from cab assembly. 2015.
   Google Scholar
• Charoenseang S, Tonggoed T. Human–robot collaboration with augmented reality. International Conference on Human-Computer Interaction. Springer; 2011. p. 93–97.
   Google Scholar
• Adamovich SV, Fluet GG, Tunik E, et al. Sensorimotor training in virtual reality: a review. NeuroRehabilitation. 2009;25(1):29–44.
   PubMed Web of Science ®Google Scholar
• Hernoux F, Nyiri E, Gibaru O. Virtual reality for improving safety and collaborative control of industrial robots. Proceedings of the 2015 Virtual Reality International Conference. ACM; 2015. p. 26.
   Google Scholar
• Makris S, Karagiannis P, Koukas S, et al. Augmented reality system for operator support in human–robot collaborative assembly. CIRP Ann Manuf Technol. 2016;65(1):61–64.
   Web of Science ®Google Scholar
• Michalos G, Makris S, Tsarouchi P, et al. Design considerations for safe human-robot collaborative workplaces. Procedia CIRP. 2015;37:248–253.
   Google Scholar
• Zhou F, Duh HBL, Billinghurst M. Trends in augmented reality tracking, interaction and display: a review of ten years of ismar. Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality. IEEE Computer Society; 2008. p. 193–202.
   Google Scholar
• Paelke V. Augmented reality in the smart factory: supporting workers in an industry 4.0. environment. Proceedings of the 2014 IEEE emerging technology and factory automation (ETFA). IEEE; 2014. p. 1–4.
   Google Scholar
• Ding H, Reißig G, Wijaya K, et al. Human arm motion modeling and long-term prediction for safe and efficient human-robot-interaction. 2011 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2011. p. 5875–5880.
   Google Scholar
• Vasquez D, Fraichard T, Laugier C. Growing hidden Markov models: an incremental tool for learning and predicting human and vehicle motion. Int J Rob Res. 2009;28(11–12):1486–1506.
   Web of Science ®Google Scholar
• Cherubini A. Multimodal sensor-based control for human-robot interaction [PhD thesis]. Université de Montpellier; 2016.
   Google Scholar
• Hiatt LM, Harrison AM, Trafton JG. Accommodating human variability in human-robot teams through theory of mind. IJCAI Proceedings-International Joint Conference on Artificial Intelligence. Vol. 22. 2011. p. 2066.
   Google Scholar
• Nikolaidis S, Ramakrishnan R, Gu K, et al. Efficient model learning from joint-action demonstrations for human-robot collaborative tasks. Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction. ACM; 2015. p. 189–196.
   Google Scholar
• Huang CM, Mutlu B. Anticipatory robot control for efficient human-robot collaboration. The Eleventh ACM/IEEE International Conference on Human Robot Interaction. IEEE Press; 2016. p. 83–90.
   Google Scholar
• Milliez G, Lallement R, Fiore M, et al. Using human knowledge awareness to adapt collaborative plan generation, explanation and monitoring. The Eleventh ACM/IEEE International Conference on Human Robot Interaction. IEEE Press; 2016. p. 43–50.
   Google Scholar
• Görür OC, Rosman BS, Hoffman G, et al. Toward integrating theory of mind into adaptive decision-making of social robots to understand human intention. Workshop on Intentions in HRI at ACM/IEEE International Conference on Human-Robot Interaction (HRI'17). IEEE; 2017.
   Google Scholar
• Corrales J, Gomez GG, Torres F, et al. Cooperative tasks between humans and robots in industrial environments. Int J Adv Robot Syst. 2012;9(3):94.
   Web of Science ®Google Scholar
• Bascetta L, Ferretti G, Rocco P, et al. Towards safe human-robot interaction in robotic cells: an approach based on visual tracking and intention estimation. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE; 2011. p. 2971–2978.
   Google Scholar
• Li X, Pan Y, Chen G, et al. Adaptive human–robot interaction control for robots driven by series elastic actuators. IEEE Trans Robot. 2017;33(1):169–182.
   Web of Science ®Google Scholar
• Ragaglia M, Zanchettin AM, Rocco P. Safety-aware trajectory scaling for human-robot collaboration with prediction of human occupancy. 2015 International Conference on Advanced Robotics (ICAR). IEEE; 2015. p. 85–90.
   Google Scholar
• Zanchettin AM, Rocco P. Path-consistent safety in mixed human-robot collaborative manufacturing environments. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE; 2013. p. 1131–1136.
   Google Scholar
• Dunjó J, Fthenakis V, Vílchez JA, et al. Hazard and operability (hazop) analysis: a literature review. J Hazard Mater. 2010;173(1–3):19–32.
   PubMed Web of Science ®Google Scholar
• Mohan RE, Wijesoma WS, Acosta Calderon CA, et al. False alarm metrics for human–robot interactions in service robots. Adv Robot. 2010;24(13):1841–1859.
   Web of Science ®Google Scholar
• National institute of standards and technology. 2017. Available from: https://www.nist.gov/
   Google Scholar
• Marvel JA. Sensors for safe, collaborative robots in smart manufacturing. 2017 IEEE Sensors. IEEE; 2017. p. 1–3.
   Google Scholar
• Bdiwi M, Pfeifer M, Sterzing A. A new strategy for ensuring human safety during various levels of interaction with industrial robots. CIRP Ann. 2017;66(1):453–456.
   Web of Science ®Google Scholar
• Oren Y, Bechar A, Edan Y. Performance analysis of a human–robot collaborative target recognition system. Robotica. 2012;30(5):813–826.
   Web of Science ®Google Scholar
• Tsai CS, Hu JS, Tomizuka M. Ensuring safety in human-robot coexistence environment. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE; 2014. p. 4191–4196.
   Google Scholar
• Lacevic B, Rocco P. Kinetostatic danger field-a novel safety assessment for human-robot interaction. 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE; 2010. p. 2169–2174.
   Google Scholar
• Polverini MP, Zanchettin AM, Rocco P. Real-time collision avoidance in human-robot interaction based on kinetostatic safety field. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE; 2014. p. 4136–4141.
   Google Scholar
• Lacevic B. Safe motion planning and control for robotic manipulators-a kinematic approach [PhD thesis, PhD dissertation]. Dipto. Elettron. Inform. e Bioing., Politecnico di; 2011.
   Google Scholar
• ISO10218:1. 2017. Available from: https://www.iso.org/standard/51330.html
   Google Scholar
• ISO10218:2. 2017. Available from: https://www.iso.org/standard/41571.html
   Google Scholar
• Fast-Berglund Å, Palmkvist F, Nyqvist P, et al. Evaluating cobots for final assembly. Procedia CIRP. 2016;44:175–180.
   Google Scholar
• Association RI, et al. ANSI/RIA r15. 06: 2012 safety requirements for industrial robots and robot systems. Ann Arbor Robot Ind Assoc. 2012.
   Google Scholar
• Harper C, Virk G. Towards the development of international safety standards for human robot interaction. Int J Soc Robot. 2010;2(3):229–234.
   Web of Science ®Google Scholar
• Fryman J, Matthias B. Safety of industrial robots: from conventional to collaborative applications. 7th German Conference on Robotics. Proceedings of ROBOTIK 2012. VDE; 2012. p. 1–5.
   Google Scholar
• Maurtua I, Ibarguren A, Kildal J, et al. Human–robot collaboration in industrial applications: safety, interaction and trust. Int J Adv Robot Syst. 2017;14(4):1729881417716010.
   Web of Science ®Google Scholar
• Bragagnolo H. Industry 4.0: from theory to practice. An empirical analysis of the factory of the future. 2017.
   Google Scholar
• El Makrini I, Merckaert K, Lefeber D, et al. Design of a collaborative architecture for human-robot assembly tasks. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2017. p. 1624–1629.
   Google Scholar
• Coupeté E, Moutarde F, Manitsaris S. A user-adaptive gesture recognition system applied to human-robot collaboration in factories. Proceedings of the 3rd International Symposium on Movement and Computing. ACM; 2016. p. 12.
   Google Scholar
• Akkaladevi SC, Heindl C. Action recognition for human robot interaction in industrial applications. 2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS). IEEE; 2015. p. 94–99.
   Google Scholar
• Ramirez-Amaro K, Beetz M, Cheng G. Understanding the intention of human activities through semantic perception: observation, understanding and execution on a humanoid robot. Adv Robot. 2015;29(5):345–362.
   Web of Science ®Google Scholar
• Ramirez-Amaro K, Dean-Leon E, Cheng G. Robust semantic representations for inferring human co-manipulation activities even with different demonstration styles. 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids). IEEE; 2015. p. 1141–1146.
   Google Scholar
• Barattini P, Morand C, Robertson NM. A proposed gesture set for the control of industrial collaborative robots. 2012 IEEE Ro-Man. IEEE; 2012. p. 132–137.
   Google Scholar
• Peppoloni L, Brizzi F, Avizzano CA, et al. Immersive ros-integrated framework for robot teleoperation. 2015 IEEE Symposium on 3D User Interfaces (3DUI). IEEE; 2015. p. 177–178.
   Google Scholar
• Tsarouchi P, Matthaiakis AS, Makris S, et al. On a human-robot collaboration in an assembly cell. Int J Comput Integr Manuf. 2017;30(6):580–589.
   Web of Science ®Google Scholar
• Potter LE, Araullo J, Carter L. The leap motion controller: a view on sign language. Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration. ACM; 2013. p. 175–178.
   Google Scholar
• Hernoux F, Béarée R, Gibaru O. Investigation of dynamic 3D hand motion reproduction by a robot using a leap motion. Proceedings of the 2015 Virtual Reality International Conference. ACM; 2015. p. 24.
   Google Scholar
• Wu J, Konrad J, Ishwar P. Dynamic time warping for gesture-based user identification and authentication with kinect. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE; 2013. p. 2371–2375.
   Google Scholar
• Loper MM, Koenig NP, Chernova SH, et al. Mobile human-robot teaming with environmental tolerance. 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE; 2009. p. 157–163.
   Google Scholar
• Correa M, Ruiz-Del-Solar J, Bernuy F. Face recognition for human-robot interaction applications: a comparative study. In: Iocchi L, Matsubara H, Weitzenfeld A, Zhou C, editor. RoboCup 2008: Robot Soccer World Cup XII. Berlin: Springer; 2008. p. 473–484.
   Google Scholar
• Bdiwi M. Integrated sensors system for human safety during cooperating with industrial robots for handing-over and assembling tasks. Procedia CIRP. 2014;23:65–70.
   Google Scholar
• Wiedemeyer T. Iai kinect2. Institute for artificial intelligence, University Bremen; 2014–2015. Available from: https://githubcom/code-iai/iai_kinect2.
   Google Scholar
• Ziafati P, Lera F, Costa A, et al. Procrob architecture for personalized social robotics. R4L 2017. HRI'17; 2017.
   Google Scholar
• Niculescu AI, Banchs RE, Li H. Why industrial robots should become more social. International Conference on Social Robotics. Springer; 2014. p. 276–278.
   Google Scholar
• Stenmark M, Nugues P. Natural language programming of industrial robots. 2013 44th International Symposium on Robotics (ISR). IEEE; 2013. p. 1–5.
   Google Scholar
• Bauzano E, Estebanez B, Garcia-Morales I, et al. Collaborative human–robot system for hals suture procedures. IEEE Syst J. 2016;10(3):957–966.
   Web of Science ®Google Scholar
• Morvan J. Understanding and communicating intentions in human-robot interaction. 2015.
   Google Scholar
• Kshirsagar A, Chen J. Toward bridging the gap between social robots and industrial robots. 2016.
   Google Scholar
• Fischer K, Jensen LC, Kirstein F, et al. The effects of social gaze in human-robot collaborative assembly. International Conference on Social Robotics. Springer; 2015. p. 204–213.
   Google Scholar
• Bee N, André E, Tober S. Breaking the ice in human-agent communication: eye-gaze based initiation of contact with an embodied conversational agent. International Workshop on Intelligent Virtual Agents. Springer; 2009. p. 229–242.
   Google Scholar
• Elprama B, El Makrini I, Jacobs A. Acceptance of collaborative robots by factory workers: a pilot study on the importance of social cues of anthropomorphic robots. International Symposium on Robot and Human Interactive Communication; 2016.
   Google Scholar
• Elprama SA, Jewell CI, Jacobs A, et al. Attitudes of factory workers towards industrial and collaborative robots. Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. ACM; 2017. p. 113–114.
   Google Scholar
• Fang H, Ong SK, Nee AYC. Robot programming using augmented reality. 2009 International Conference on CyberWorlds. CW'09. IEEE; 2009. p. 13–20.
   Google Scholar
• Steinmetz F, Weitschat R. Skill parametrization approaches and skill architecture for human-robot interaction. 2016 IEEE International Conference on Automation Science and Engineering (CASE). IEEE; 2016. p. 280–285.
   Google Scholar
• Hein B, Hensel M, Worn H. Intuitive and model-based on-line programming of industrial robots: a modular on-line programming environment. 2008 IEEE International Conference on Robotics and Automation. ICRA 2008. IEEE; 2008. p. 3952–3957.
   Google Scholar
• Lambrecht J, Kleinsorge M, Rosenstrauch M, et al. Spatial programming for industrial robots through task demonstration. Int J Adv Robot Syst. 2013;10(5):254.
   Web of Science ®Google Scholar
• Tykal M, Montebelli A, Kyrki V. Incrementally assisted kinesthetic teaching for programming by demonstration. The Eleventh ACM/IEEE International Conference on Human Robot Interaction. IEEE Press; 2016. p. 205–212.
   Google Scholar
• Zhang J, Wang Y, Xiong R. Industrial robot programming by demonstration. 2016 International Conference on Advanced Robotics and Mechatronics (ICARM). IEEE; 2016. p. 300–305.
   Google Scholar
• Tsarouchi P, Makris S, Chryssolouris G. Human–robot interaction review and challenges on task planning and programming. Int J Comput Integr Manuf. 2016;29(8):916–931.
   Web of Science ®Google Scholar
• Musat FC, Mihu FC. Collaborative robots and knowledge management – a short review. ACTA Univ Cibiniensis. 2017;69(1):143–147.
   Google Scholar
• Balakirsky S, Kootbally Z, Schlenoff C, et al. An industrial robotic knowledge representation for kit building applications. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2012. p. 1365–1370.
   Google Scholar
• Roveda L. Model based compliance shaping control of light-weight manipulator in hard-contact industrial applications [PhD thesis]. Italy: 2015.
   Google Scholar
• Ramos L. Semantic web for manufacturing, trends and open issues: toward a state of the art. Comput Ind Eng. 2015;90:444–460.
   Web of Science ®Google Scholar
• Schou C, Hansson M, Madsen O. Assisted hardware selection for industrial collaborative robots. Procedia Manuf. 2017;11:174–184.
   Google Scholar
• Guerin KR, Lea C, Paxton C, et al. A framework for end-user instruction of a robot assistant for manufacturing. 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2015. p. 6167–6174.
   Google Scholar
• Colledanchise M, Ögren P. How behavior trees modularize robustness and safety in hybrid systems. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). IEEE; 2014. p. 1482–1488.
   Google Scholar
• Paxton C, Hundt A, Jonathan F, et al. Costar: instructing collaborative robots with behavior trees and vision. 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2017. p. 564–571.
   Google Scholar
• Pichler A, Akkaladevi SC, Ikeda M, et al. Towards shared autonomy for robotic tasks in manufacturing. Procedia Manuf. 2017;11:72–82.
   Google Scholar
• Mocan B, Fulea M, Brad S. Designing a multimodal human-robot interaction interface for an industrial robot. In: Borangiu T, editor. Advances in robot design and intelligent control. Cham: Springer International Publishing; 2016. p. 255–263.
   Google Scholar
• Maoudj A, Bouzouia B, Hentout A, et al. Distributed multi-agent scheduling and control system for robotic flexible assembly cells. J Intell Manuf. 2019;30(4):1629–1644.
   Web of Science ®Google Scholar
• Gombolay MC, Shah JA. Challenges in collaborative scheduling of human-robot teams. Artificial Intelligence for Human-Robot Interaction: Papers From the 2014 AAAI Fall Symposium. Google Scholar; 2014.
   Google Scholar
• Gombolay MC, Huang C, Shah JA. Coordination of human-robot teaming with human task preferences. AAAI Fall Symposium Series on AI-HRI. Vol. 11. 2015.
   Google Scholar
• Chen F, Sekiyama K, Sasaki H, et al. Assembly strategy modeling and selection for human and robot coordinated cell assembly. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2011. p. 4670–4675.
   Google Scholar
• Tan JTC, Duan F, Kato R, et al. Collaboration planning by task analysis in human-robot collaborative manufacturing system. In: Ernest H, editor. Advances in robot manipulators. Rijeka, Croatia: IntechOpen; 2010. p. 113–132.
   Google Scholar
• Tan JTC, Duan F, Zhang Y, et al. Task decomposition of cell production assembly operation for man-machine collaboration by HTA. 2008 IEEE International Conference on Automation and Logistics. IEEE; 2008. p. 1066–1071.
   Google Scholar
• Takata S, Hirano T. Human and robot allocation method for hybrid assembly systems. CIRP Ann. 2011;60(1):9–12.
   Web of Science ®Google Scholar
• Akan B, AAmeri E, Çürüklü B. Augmented reality-based industrial robot control. Proceedings of SIGRAD 2011. Evaluations of Graphics and Visualization Efficiency; Usefulness; Accessibility; Usability; 2011 Nov 17–18; KTH; Stockholm; Sweden. Linköping University Electronic Press; 2011. p. 113–114; 065.
   Google Scholar
• Nee AY, Ong S, Chryssolouris G, et al. Augmented reality applications in design and manufacturing. CIRP Ann Manuf Technol. 2012;61(2):657–679.
   Web of Science ®Google Scholar
• Moniz AB, Krings BJ. Robots working with humans or humans working with robots? Searching for social dimensions in new human-robot interaction in industry. Societies. 2016;6(3):23.
   Web of Science ®Google Scholar
• Makris S, Pintzos G, Rentzos L, et al. Assembly support using ar technology based on automatic sequence generation. CIRP Ann. 2013;62(1):9–12.
   Web of Science ®Google Scholar
• Michalos G, Karagiannis P, Makris S, et al. Augmented reality (ar) applications for supporting human-robot interactive cooperation. Procedia CIRP. 2016;41:370–375.
   Google Scholar
• Sääski J, Salonen T, Hakkarainen M, et al. Integration of design and assembly using augmented reality. International Precision Assembly Seminar. Springer; 2008. p. 395–404.
   Google Scholar
• Mourtzis D, Doukas M. A web-based virtual and augmented reality platform for supporting the design of personalised products. CMS (2012), 45th CIRP Conference on Manufacturing Systems, Athens, Greece; 2012. p. 234–241.
   Google Scholar
• Matsas E, Vosniakos GC. Design of a virtual reality training system for human–robot collaboration in manufacturing tasks. Int J Interact Des Manuf (IJIDeM). 2017;11(2):139–153.
   Google Scholar
• Di Gironimo G, Patalano S, Tarallo A. Innovative assembly process for modular train and feasibility analysis in virtual environment. Int J Interact Des Manuf (IJIDeM). 2009;3(2):93–101.
   Google Scholar
• Sauppé A, Mutlu B. The social impact of a robot co-worker in industrial settings. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM; 2015. p. 3613–3622.
   Google Scholar
• Glasauer S, Huber M, Basili P, et al. Interacting in time and space: investigating human-human and human-robot joint action. 2010 IEEE Ro-Man. IEEE; 2010. p. 252–257.
   Google Scholar
• Huber M, Knoll A, Brandt T, et al. When to assist? Modelling human behaviour for hybrid assembly systems. 2010 41st International Symposium on Robotics (ISR) and 2010 6th German Conference on Robotics (Robotik). VDE; 2010. p. 1–6.
   Google Scholar
• Yagoda RE, Gillan DJ. You want me to trust a robot? The development of a human–robot interaction trust scale. Int J Soc Robot. 2012;4(3):235–248.
   Web of Science ®Google Scholar
• Charalambous G, Fletcher S, Webb P. Human-automation collaboration in manufacturing. Contemporary ergonomics and human factors 2013. Vol. 59. Routledge in association with GSE Research; 2013. p. 59–66.
   Google Scholar
• Yagoda R. You want me to use that robot? Identifying underlying factors affecting robot use [PhD thesis]. Raleigh, North Carolina, USA; 2013.
   Google Scholar
• Ogorodnikova O. Human weaknesses and strengths in collaboration with robots. Period Polytech Mech Eng. 2008;52(1):25–33.
   Google Scholar
• Dragan AD, Lee KC, Srinivasa SS. Legibility and predictability of robot motion. Proceedings of the 8th ACM/IEEE International Conference on Human-robot Interaction. IEEE Press; 2013. p. 301–308.
   Google Scholar
• Hentout A, Messous MA, Bouzouia B. Fault-tolerant multi-agent control architecture for autonomous mobile manipulators: simulation results. Comput Elect Eng. 2015;43:238–256.
   Web of Science ®Google Scholar
• Caccavale F, Villani L. Fault diagnosis and fault tolerance for mechatronic systems: recent advances. In: Caccavale F, Villani L, editor. Springer Tracts in Advanced Robotics, Proceedings of a Workshop at the 2002 IEEE International Symposium on Intelligent Control, Vol. 1. Berlin: Springer-Verlag; 2002.
   Google Scholar
• Kalisch M. Supervised context classification methods for an industrial machinery. 2015 Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE; 2015. p. 1667–1672.
   Google Scholar
• De Santis A, Siciliano B, De Luca A, et al. An atlas of physical human–robot interaction. Mech Mach Theory. 2008;43(3):253–270.
   Web of Science ®Google Scholar
• Crestani D, Godary-Dejean K, Lapierre L. Enhancing fault tolerance of autonomous mobile robots. Rob Auton Syst. 2015;68:140–155.
   Web of Science ®Google Scholar
• Dhillon B, Fashandi A, Liu K. Robot systems reliability and safety: a review. J Qual Maint Eng. 2002;8(3):170–212.
   Google Scholar
• Bicchi A, Bavaro M, Boccadamo G, et al. Physical human-robot interaction: dependability, safety, and performance. 2008 10th IEEE International Workshop on Advanced Motion Control. AMC'08. IEEE; 2008. p. 9–14.
   Google Scholar
• Pervez A, Ryu J. Safe physical human robot interaction-past, present and future. J Mech Sci Technol. 2008;22(3):469.
   Web of Science ®Google Scholar
• Krüger J, Surdilovic D. Robust control of force-coupled human–robot-interaction in assembly processes. CIRP Ann Manuf Technol. 2008;57(1):41–44.
   Web of Science ®Google Scholar
• Jacob RJ, Girouard A, Hirshfield LM, et al. Reality-based interaction: a framework for post-wimp interfaces. Proceedings of the SIGCHI conference on Human factors in computing systems. ACM; 2008. p. 201–210.
   Google Scholar
• Gao D, Wampler CW. Head injury criterion. IEEE Robot Autom Mag. 2009;16(4):71–74.
   Web of Science ®Google Scholar
• Zoghbi S, Croft E, Kulić D, et al. Evaluation of affective state estimations using an on-line reporting device during human-robot interactions. 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS 2009. IEEE; 2009. p. 3742–3749.
   Google Scholar
• Ding H, Kain S, Schiller F, et al. Increasing reliability of intelligent manufacturing systems by adaptive optimization and safety supervision. IFAC Proc Vol. 2009;42(8):1533–1538.
   Google Scholar
• Tan JTC, Duan F, Zhang Y, et al. Human-robot collaboration in cellular manufacturing: design and development. 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS 2009. IEEE; 2009. p. 29–34.
   Google Scholar
• Hoffmann A, Angerer A, Ortmeier F, et al. Hiding real-time: a new approach for the software development of industrial robots. IROS; 2009. p. 2108–2113.
   Google Scholar
• Zhang B, Wang J, Fuhlbrigge T. A review of the commercial brain-computer interface technology from perspective of industrial robotics. 2010 IEEE International Conference on Automation and logistics (ICAL). IEEE; 2010. p. 379–384.
   Google Scholar
• International Organization for Standardization I. Safety of machinery – general principles for design – risk assessment and risk reduction. 2010.
   Google Scholar
• Surdilovic D, Yakut Y, Nguyen TM, et al. Compliance control with dual-arm humanoid robots: design, planning and programming. 2010 10th IEEE-RAS International Conference on Humanoid Robots. IEEE; 2010. p. 275–281.
   Google Scholar
• Esfahani ET, Sundararajan V. Using brain–computer interfaces to detect human satisfaction in human–robot interaction. Int J Human Robot. 2011;8(01):87–101.
   Web of Science ®Google Scholar
• Lim S, Chung L, Han O, et al. An interactive cyber-physical system (CPS) for people with disability and frail elderly people. Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication. ACM; 2011. p. 113.
   Google Scholar
• Lamy X. Conception d'une interface de pilotage d'un cobot [PhD thesis]. France: 2011.
   Google Scholar
• Papakostas N, Michalos G, Makris S, et al. Industrial applications with cooperating robots for the flexible assembly. Int J Comput Integr Manuf. 2011;24(7):650–660.
   Web of Science ®Google Scholar
• Sghaier A, Charpentier P. La problématique de l'utilisation des robots industriels en matière de sécurité. Annales des Mines-Réalités industrielles. 1. Eska; 2012. p. 24–31.
   Google Scholar
• Haddadin S, Haddadin S, Khoury A, et al. On making robots understand safety: embedding injury knowledge into control. Int J Rob Res. 2012;31(13):1578–1602.
   Web of Science ®Google Scholar
• Stenmark M, Malec J. A helping hand: industrial robotics, knowledge and user-oriented services. Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems; Tokyo, Japan. 2013. p. 3–7.
   Google Scholar
• Daniel B, Thomessen T, Korondi P. Simplified human-robot interaction: modeling and evaluation. Model Identif Control. 2013;34(4):199–211.
   Web of Science ®Google Scholar
• Hentout A, Benbouali M, Akli I, et al. A telerobotic human/robot interface for mobile manipulators: a study of human operator performance. 2013 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE; 2013. p. 641–646.
   Google Scholar
• Garber L. Robot OS: a new day for robot design. Computer. 2013;12:16–20.
   Google Scholar
• Dobra A. General classification of robots. size criteria. 2014 23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD). IEEE; 2014. p. 1–6.
   Google Scholar
• Thompson JL. Redesigning the human-robot interface: intuitive teleoperation of anthropomorphic robots [PhD thesis]. 2014.
   Google Scholar
• Augustsson S, Olsson J, Christiernin LG, et al. How to transfer information between collaborating human operators and industrial robots in an assembly. Proceedings of the 8th Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational. ACM; 2014. p. 286–294.
   Google Scholar
• Lasota PA, Rossano GF, Shah JA. Toward safe close-proximity human-robot interaction with standard industrial robots. 2014 IEEE International Conference on Automation Science and Engineering (CASE). IEEE; 2014. p. 339–344.
   Google Scholar
• Knight W. How human-robot teamwork will upend manufacturing. 2014.
   Google Scholar
• Michalos G, Makris S, Spiliotopoulos J, et al. Robo-partner: seamless human-robot cooperation for intelligent, flexible and safe operations in the assembly factories of the future. Procedia CIRP. 2014;23:71–76.
   Google Scholar
• Masinga P, Campbell H, Trimble J. A framework for human collaborative robots, operations in South African automotive industry. 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE; 2015. p. 1494–1497.
   Google Scholar
• Maurice P. Virtual ergonomics for the design of collaborative robots [PhD thesis]. Université Pierre et Marie Curie-Paris VI; 2015.
   Google Scholar
• Brossog M, Bornschlegl M, Franke J. Reducing the energy consumption of industrial robots in manufacturing systems. Int J Adv Manuf Technol. 2015;78(5–8):1315–1328.
   Web of Science ®Google Scholar
• Massa D, Callegari M, Cristalli C. Manual guidance for industrial robot programming. Ind Robot Int J. 2015;42(5):457–465.
   Web of Science ®Google Scholar
• Sbanca MP, Mogan GL. Cooperative assembly using two industrial robots. In: Borangiu T, editor. Advances in robot design and intelligent control. Cham: Springer International Publishing; 2016. p. 47–57.
   Google Scholar
• Bloss R. Collaborative robots are rapidly providing major improvements in productivity, safety, programing ease, portability and cost while addressing many new applications. Ind Robot Int J. 2016;43(5):463–468.
   Web of Science ®Google Scholar
• Tang G. The development of a human-robot interface for industrial collaborative system [PhD thesis]. School of Engineering, Cranfield University; 2016.
   Google Scholar
• Bauer W, Bender M, Braun M, et al. Lightweight robots in manual assembly-best to start simply. Stuttgart: Frauenhofer-Institut für Arbeitswirtschaft und Organisation IAO; 2016.
   Google Scholar
• Shneiderman B, Plaisant C, Cohen M, et al. Designing the user interface: strategies for effective human-computer interaction. 6th ed. Pearson; 2016.
   Google Scholar
• Hentout A, Maoudj A, Bouzouia B. A survey of development frameworks for robotics. 2016 8th International Conference on Modelling, Identification and Control (ICMIC). IEEE; 2016. p. 67–72.
   Google Scholar
• Khalid A, Kirisci P, Ghrairi Z, et al. A methodology to develop collaborative robotic cyber-physical systems for production environments. Logist Res. 2016;9(1):23.
   Google Scholar
• Correai Marques JM. A point-and-command interface for grasping unknown objects with robotic manipulators [PhD thesis]. Italy: 2017.
   Google Scholar
• Hussein A, Gaber MM, Elyan E, et al. Imitation learning: a survey of learning methods. ACM Comput Surv (CSUR). 2017;50(2):21.
   Web of Science ®Google Scholar
• Martinez C, Barrero N, Hernandez W, et al. Setup of the yaskawa sda10f robot for industrial applications, using ros-industrial. Advances in automation and robotics research in Latin America. Springer; 2017. p. 186–203.
   Google Scholar
• Robot operating system. 2017. Available from: http://www.ros.org
   Google Scholar
• Unified modeling language. 2017. Available from: http://www.uml.org/
   Google Scholar
• Artoolkit. 2017. Available from: https://github.com/artoolkit
   Google Scholar
• Open graphics library. 2017. Available from: https://www.opengl.org/
   Google Scholar
• ISO/TS 15066:2016. Robots and robotic devices – collaborative robots. 2016. Available from: https://www.iso.org/standard/62996.html
   Google Scholar
• Long P, Chevallereau C, Chablat D, et al. An industrial security system for human-robot coexistence. Ind Robot Int J. 2018;45(2):220–226.
   Web of Science ®Google Scholar
• Hirzinger G, Albu-Schaffer A, Hahnle M, et al. On a new generation of torque controlled light-weight robots. Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164). Vol. 4. IEEE; 2001. p. 3356–3363.
   Google Scholar
• Bicchi A, Tonietti G. Fast and ‘soft-arm’ tactics [robot arm design]. IEEE Robot Autom Mag. 2004;11(2):22–33.
   Web of Science ®Google Scholar
• Yamada Y, Yamamoto T, Morizono T, et al. Fta-based issues on securing human safety in a human/robot coexistence system. IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 99CH37028). Vol. 2. IEEE; 1999. p. 1058–1063.
   Google Scholar
• Salisbury JK. Active stiffness control of a manipulator in cartesian coordinates. 1980 19th IEEE Conference on Decision and Control Including the Symposium on Adaptive Processes. IEEE; 1980. p. 95–100.
   Google Scholar
• Hogan N. Impedance control: an approach to manipulation: part II–implementation. J Dyn Syst Meas Control. 1985;107(1):8–16.
   Web of Science ®Google Scholar
• Butler JT, Agah A. Psychological effects of behavior patterns of a mobile personal robot. Auton Robots. 2001;10(2):185–202.
   Web of Science ®Google Scholar
• Yamada Y, Hirasawa Y, Huang S, et al. Human-robot contact in the safeguarding space. IEEE ASME Trans Mechatron. 1997;2(4):230–236.
   Web of Science ®Google Scholar
• Kulić D, Croft EA. Real-time safety for human–robot interaction. Rob Auton Syst. 2006;54(1):1–12.
   Web of Science ®Google Scholar
• Gopinath V, Johansen K, Ölvander J. Risk assessment for collaborative operation: a case study on hand-guided industrial robots. In: Valentina S, editor. Risk assessment. Rijeka, Croatia: InTech; 2018. p. 167–187.
   Google Scholar
• Chen JH, Song KT. Collision-free motion planning for human-robot collaborative safety under cartesian constraint. 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2018. p. 1–7.
   Google Scholar
• Grahn S, Gopinath V, Wang XV, et al. Exploring a model for production system design to utilize large robots in human-robot collaborative assembly cells. Procedia Manuf. 2018;25:612–619.
   Google Scholar
• Villani V, Pini F, Leali F, et al. Survey on human–robot collaboration in industrial settings: safety, intuitive interfaces and applications. Mechatronics. 2018;55:248–266.
   Web of Science ®Google Scholar
• Akima H. A new method of interpolation and smooth curve fitting based on local procedures. J ACM (JACM). 1970;17(4):589–602.
   Web of Science ®Google Scholar
• Djezairi S, Akli I, Zamoum-Boushaki R, et al. Mission allocation and execution for human and robot agents in industrial environment. 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man). IEEE; 2018. p. 796–801.
   Google Scholar
• Heydaryan S, Suaza Bedolla J, Belingardi G. Safety design and development of a human-robot collaboration assembly process in the automotive industry. Appl Sci. 2018;8(3):344.
   Google Scholar
• Hentout A, Aouache M, Maoudj A, et al. Key challenges and open issues of industrial collaborative robotics. 2018 The 27th IEEE International Symposium on Workshop on Human-Robot Interaction: from Service to Industry (HRI-SI2018) at Robot and Human Interactive Communication. Proceedings. IEEE; 2018.
   Google Scholar