Journal of Robotics Spectrum

Enhancing Safety and Collaboration in Human Robot Interaction for Industrial Robotics

Journal of Robotics Spectrum

Received On : 15 May 2023

Revised On : 02 October 2023

Accepted On : 10 November 2023

Published On : 22 November 2023

Volume 01, 2023

Pages : 134-143


This research evaluates the aspect of collaboration between humans and robots in industrial robotics. It highlights the advantages of using robots in non-ergonomic tasks while at the same time recognizing that there are challenges preventing them from achieving manipulation accuracy with precision. Collaborative robots also known as robots, have been proposed to address these limitations. Safety has been identified as one of the most critical issues in collaborative environments, which calls for a discussion on various strategies and practices to ensure the safety of operators. The study explores many facets of human-robot interaction and collaboration such as physicality and proximity, house sharing, and collaboration. Furthermore, this article argues that it is vital to consider human aspects of human-robot interaction such as trustworthiness, mental effort, and fear. The final part presents a case study on incorporation of humans and robots in assembly and sealing process of refrigerator. Finally, this case underlines safety measures that need to be included during robot type selection and assembly process equipment used should match robot’s characteristics like size etc. This study suggests possible avenues for future inquiry including augmented reality methods and integrating safety constraints into design software and planning software.


Collaborative Robots, Industrial Robots, Human-Robot Collaboration, Augmented Reality Techniques, Human-Robot Interaction.

  1. M. J. Rosenstrauch and J. Krüger, “Safe human-robot-collaboration-introduction and experiment using ISO/TS 15066,” 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), Apr. 2017, doi: 10.1109/iccar.2017.7942795.
  2. D. Comanicìu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603–619, May 2002, doi: 10.1109/34.1000236.
  3. S. M. Dominguez, T. Keaton, and A. H. Sayed, “A robust finger tracking method for multimodal wearable computer interfacing,” IEEE Transactions on Multimedia, vol. 8, no. 5, pp. 956–972, Oct. 2006, doi: 10.1109/tmm.2006.879872.
  4. I. 200 Beijing, Advances in Multimodal Interfaces - ICMI 2000: Third International Conference Beijing, China, October 14-16, 2000 Proceedings. Springer Science & Business Media, 2000.
  5. M. Shahpari, F. M. Saradj, M. S. Pishvaee, and S. Piri, “Assessing the productivity of prefabricated and in-situ construction systems using hybrid multi-criteria decision making method,” Journal of Building Engineering, vol. 27, p. 100979, Jan. 2020, doi: 10.1016/j.jobe.2019.100979.
  6. O. A. Hamed, “Overview of hybrid desalination systems — current status and future prospects,” Desalination, vol. 186, no. 1–3, pp. 207–214, Dec. 2005, doi: 10.1016/j.desal.2005.03.095.
  7. A. D. Friend, A. Stevens, R. G. Knox, and M. G. R. Cannell, “A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0),” Ecological Modelling, vol. 95, no. 2–3, pp. 249–287, Feb. 1997, doi: 10.1016/s0304-3800(96)00034-8.
  8. S. A. Kolyubin, L. Paramonov, and A. S. Shiriaev, “Robot Kinematics Identification: KUKA LWR4+ Redundant Manipulator Example,” Journal of Physics, vol. 659, p. 012011, Nov. 2015, doi: 10.1088/1742-6596/659/1/012011.
  9. D. Mukherjee, K. C. Gupta, L. H. Chang, and H. Najjaran, “A survey of Robot Learning Strategies for Human-Robot Collaboration in Industrial Settings,” Robotics and Computer-Integrated Manufacturing, vol. 73, p. 102231, Feb. 2022, doi: 10.1016/j.rcim.2021.102231.
  10. M. J. Rosenstrauch and J. Krüger, “Safe human robot collaboration — Operation area segmentation for dynamic adjustable distance monitoring,” 2018 4th International Conference on Control, Automation and Robotics (ICCAR), Apr. 2018, doi: 10.1109/iccar.2018.8384637.
  11. T. Salter, F. Michaud, D. Létourneau, D. C. Lee, and I. Werry, “Using proprioceptive sensors for categorizing human-robot interactions,” 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI), Mar. 2007, doi: 10.1145/1228716.1228731.
  12. N. C. Krämer, A. M. Von Der Pütten, and S. C. Eimler, “Human-Agent and Human-Robot Interaction Theory: Similarities to and Differences from Human-Human Interaction,” in Studies in computational intelligence, 2012, pp. 215–240. doi: 10.1007/978-3-642-25691-2_9.
  13. M. I. A. Ferreira and S. R. Fletcher, The 21st century Industrial Robot: When tools become collaborators. Springer Nature, 2021.
  14. L. Wang, X. V. Wang, J. Váncza, and Z. Kemény, Advanced Human-Robot collaboration in manufacturing. Springer Nature, 2021.
  15. L. Bascetta 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, Sep. 2011, doi: 10.1109/iros.2011.6048287.
  16. F. Ferraguti, M. Bertuletti, C. T. Landi, M. Bonfè, C. Fantuzzi and C. Secchi, "A Control Barrier Function Approach for Maximizing Performance While Fulfilling to ISO/TS 15066 Regulations," in IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 5921-5928, Oct. 2020, doi: 10.1109/LRA.2020.3010494.
  17. G. Michalos, S. Makris, P. Tsarouchi, T. Guasch, D. Kontovrakis, and G. Chryssolouris, “Design considerations for safe human-robot collaborative workplaces,” Procedia CIRP, vol. 37, pp. 248–253, Jan. 2015, doi: 10.1016/j.procir.2015.08.014.
  18. G. Vivo, A. Zanella, Ö. Tokçalar, and G. Michalos, “The ROBO-PARTNER EC Project: CRF activities and Automotive Scenarios,” Procedia Manufacturing, vol. 11, pp. 364–371, Jan. 2017, doi: 10.1016/j.promfg.2017.07.119.
  19. J. M. D. Delgado, A. O. Ajayi, L. Akanbi, O. O. Akinadé, and M. Bilal, “Robotics and automated systems in construction: Understanding industry-specific challenges for adoption,” Journal of Building Engineering, vol. 26, p. 100868, Nov. 2019, doi: 10.1016/j.jobe.2019.100868.
  20. T. Tashtoush et al., “Human-Robot Interaction and Collaboration (HRI-C) utilizing Top-View RGB-D camera system,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 1, Jan. 2021, doi: 10.14569/ijacsa.2021.0120102.
  21. L. R. Long, “Contacts and collisions,” in Apress eBooks, 2014, pp. 141–182. doi: 10.1007/978-1-4302-6440-8_8.
  22. T. Lew et al., “Contact Inertial Odometry: Collisions are your Friends,” in Springer proceedings in advanced robotics, 2022, pp. 938–958. doi: 10.1007/978-3-030-95459-8_58.
  23. C. W. Therrien, T. F. Quatieri, and D. E. Dudgeon, “Statistical model-based algorithms for image analysis,” Proceedings of the IEEE, vol. 74, no. 4, pp. 532–551, Jan. 1986, doi: 10.1109/proc.1986.13504.
  24. J. L. M. C, T. Kluß, and C. Zetzsche, “Categorization of Contact Events as Intended or Unintended using Pre-Contact Kinematic Features,” 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Mar. 2020, doi: 10.1109/vrw50115.2020.00016.
  25. W. Burgard, O. Brock, and C. Stachniss, “Safety Evaluation of physical Human-Robot interaction via Crash-Testing,” in The MIT Press eBooks, 2008, pp. 217–224. doi: 10.7551/mitpress/7830.003.0029.
  26. R.-J. Halme, M. Lanz, J. Kämäräinen, R. Pieters, J. Latokartano, and A. Hietanen, “Review of vision-based safety systems for human-robot collaboration,” Procedia CIRP, vol. 72, pp. 111–116, Jan. 2018, doi: 10.1016/j.procir.2018.03.043.
  27. X. Li, Y. Pan, C. Gong, and Y. Huang, “Adaptive Human–Robot Interaction control for robots driven by series elastic actuators,” IEEE Transactions on Robotics, vol. 33, no. 1, pp. 169–182, Feb. 2017, doi: 10.1109/tro.2016.2626479.
  28. H. Su, C. Yang, G. Ferrigno, and E. De Momi, “Improved Human–Robot collaborative control of redundant robot for teleoperated minimally invasive surgery,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 1447–1453, Apr. 2019, doi: 10.1109/lra.2019.2897145.
  29. A. Ajoudani, A. M. Zanchettin, S. Ivaldi, A. Albu‐Schäffer, K. Kosuge, and O. Khatib, “Progress and prospects of the human–robot collaboration,” Autonomous Robots, vol. 42, no. 5, pp. 957–975, Oct. 2017, doi: 10.1007/s10514-017-9677-2.
  30. T. Lesort, N. Díaz-Rodríguez, J.-F. Goudou, and D. Filliat, “State representation learning for control: An overview,” Neural Networks, vol. 108, pp. 379–392, Dec. 2018, doi: 10.1016/j.neunet.2018.07.006.
  31. E. Prati, M. Peruzzini, M. Pellicciari, and R. Raffaeli, “How to include User eXperience in the design of Human-Robot Interaction,” Robotics and Computer-Integrated Manufacturing, vol. 68, p. 102072, Apr. 2021, doi: 10.1016/j.rcim.2020.102072.
  32. W. He, C. Xue, X. Yu, Z. Li, and C. Yang, “Admittance-Based controller design for physical Human–Robot interaction in the constrained task space,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 4, pp. 1937–1949, Oct. 2020, doi: 10.1109/tase.2020.2983225.
  33. M. Safeea, N. Mendes, and P. Neto, “Minimum distance calculation for safe human robot interaction,” Procedia Manufacturing, vol. 11, pp. 99–106, Jan. 2017, doi: 10.1016/j.promfg.2017.07.157.
  34. A.-N. Sharkawy, P. Ν. Koustoumpardis, and N. Α. Aspragathos, “Human–robot collisions detection for safe human–robot interaction using one multi-input–output neural network,” Soft Computing, vol. 24, no. 9, pp. 6687–6719, Aug. 2019, doi: 10.1007/s00500-019-04306-7.
  35. V. Villani, F. Pini, F. Leali, and C. Secchi, “Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications,” Mechatronics, vol. 55, pp. 248–266, Nov. 2018, doi: 10.1016/j.mechatronics.2018.02.009.
  36. M. Lorenzini, M. Lagomarsino, L. Fortini, S. Gholami, and A. Ajoudani, “Ergonomic human-robot collaboration in industry: A review,” Frontiers in Robotics and AI, vol. 9, Jan. 2023, doi: 10.3389/frobt.2022.813907.
  37. G. Charalambous, S. Fletcher, and P. Webb, “Identifying the key organisational human factors for introducing human-robot collaboration in industry: an exploratory study,” The International Journal of Advanced Manufacturing Technology, vol. 81, no. 9–12, pp. 2143–2155, Jun. 2015, doi: 10.1007/s00170-015-7335-4.
  38. K. Naveen Durai, R. Subha, and A. Haldorai, “Hybrid Invasive Weed Improved Grasshopper Optimization Algorithm for Cloud Load Balancing,” Intelligent Automation & Soft Computing, vol. 34, no. 1, pp. 467–483, 2022, doi: 10.32604/iasc.2022.026020.
  39. A. Jevtić, A. Colomé, G. Alenyà, and C. Torras, “Robot motion adaptation through user intervention and reinforcement learning,” Pattern Recognition Letters, vol. 105, pp. 67–75, Apr. 2018, doi: 10.1016/j.patrec.2017.06.017.
  40. M. T. J. Spaan, “Partially observable Markov decision processes,” in Adaptation, learning, and optimization, 2012, pp. 387–414. doi: 10.1007/978-3-642-27645-3_12.
  41. A. P. Dani, I. Salehi, G. Rotithor, D. Trombetta, and H. Ravichandar, “Human-in-the-Loop robot control for Human-Robot collaboration: human intention estimation and safe trajectory tracking control for collaborative tasks,” IEEE Control Systems Magazine, vol. 40, no. 6, pp. 29–56, Dec. 2020, doi: 10.1109/mcs.2020.3019725.
  42. K. Kasmarik and M. L. Maher, “Motivated reinforcement learning for non-player characters in persistent computer game worlds,” Proceedings of the 2006 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, Jun. 2006, doi: 10.1145/1178823.1178828.
  43. A. Doucet, N. De Freitas, N. Gordon, and A. F. M. Smith, Sequential Monte Carlo methods in practice. 2001. doi: 10.1007/978-1-4757-3437-9.
  44. B. Stroud, “Knowledge from a Human Point of View,” in Synthese Library, 2019, pp. 141–148. doi: 10.1007/978-3-030-27041-4_9.
  45. C. Pezzato, R. Ferrari, and C. H. Corbato, “A novel adaptive controller for robot manipulators based on active inference,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2973–2980, Apr. 2020, doi: 10.1109/lra.2020.2974451.
  46. N. Mitsunaga, C. Smith, T. Kanda, H. Ishiguro, and N. Hagita, “Adapting robot behavior for Human--Robot Interaction,” IEEE Transactions on Robotics, vol. 24, no. 4, pp. 911–916, Aug. 2008, doi: 10.1109/tro.2008.926867.
  47. P. Sterzer and A. Kleinschmidt, “A neural basis for inference in perceptual ambiguity,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 1, pp. 323–328, Jan. 2007, doi: 10.1073/pnas.0609006104.
  48. M. Nourian and P. E. Caines, “$\epsilon$-Nash Mean Field Game Theory for Nonlinear Stochastic Dynamical Systems with Major and Minor Agents,” Siam Journal on Control and Optimization, vol. 51, no. 4, pp. 3302–3331, Jan. 2013, doi: 10.1137/120889496.
  49. E. P. Fenichel et al., “Adaptive human behavior in epidemiological models,” Proceedings of the National Academy of Sciences of the United States of America, vol. 108, no. 15, pp. 6306–6311, Mar. 2011, doi: 10.1073/pnas.1011250108.
  50. C. Eppner, R. Deimel, Jos, Álvarez-Ruiz, M. Maertens, and O. Brock, “Exploitation of environmental constraints in human and robotic grasping,” The International Journal of Robotics Research, vol. 34, no. 7, pp. 1021–1038, Apr. 2015, doi: 10.1177/0278364914559753.
  51. G. E. Navas-Reascos, D. Romero, J. Stahre, and A. Caballero-Ruiz, “Wire Harness Assembly Process Supported by Collaborative Robots: Literature Review and Call for R&D,” Robotics, vol. 11, no. 3, p. 65, Jun. 2022, doi: 10.3390/robotics11030065.
  52. S. Ayub, N. Singh, Md. Z. Hussain, M. Ashraf, D. K. Singh, and A. Haldorai, “Hybrid Approach to Implement Multi-robotic Navigation System Using Neural Network, Fuzzy Logic and Bio-inspired Optimization Methodologies,” Oct. 2021, doi: 10.21203/


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Cite this article

Marie T Greally, “Enhancing Safety and Collaboration in Human Robot Interaction for Industrial Robotics”, Journal of Robotics Spectrum, vol.1, pp. 134-143, 2023. doi: 10.53759/9852/JRS202301013.


© 2023 Marie T Greally. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.