A Review on Human–Robot Interaction Control Systems
DOI:
https://doi.org/10.64321/jcr.v2i5.06Keywords:
Human, Robot, Interaction, Control SystemAbstract
Human-Robot Interaction (HRI) is an evolving field where robots are increasingly integrated into environments requiring close cooperation with humans, such as manufacturing, healthcare, and service industries. The efficiency and safety of these systems are critical, and this paper explores two essential factors: control and safety. Effective control in HRI ensures that robots adapt to human actions and environmental changes, while safety mechanisms prevent harm to human operators. The Virtual Admittance (VA) control system is discussed in the context of enhancing robot responsiveness, where both virtual damping and inertia are adjusted to achieve smoother, more efficient collaboration between humans and robots. The paper also examines collision detection methods, emphasizing model-based and data-driven approaches, to ensure safe interaction between robots and humans. Performance metrics such as task completion time, human effort, accuracy, and system stability are compared across various VA control systems used in co-manipulation tasks. Additionally, the paper reviews safety measures through collision detection, highlighting methods that integrate sensors and neural networks to detect and prevent harmful interactions. By providing a comprehensive analysis of VA control systems and safety mechanisms, this paper offers insights into the advancements and challenges of human-robot collaboration. The goal is to guide future developments in HRI, emphasizing the importance of robust control systems and safety protocols for improving the performance, efficiency, and reliability of these interactions in diverse applications.
References
Sharkawy, A.-N. Human-Robot Interaction: Applications. In Proceedings of the 1st IFSA Winter Conference on Automation, Robotics & Communications for Industry 4.0 (ARCI’ 2021); Yurish, S.Y., Ed.; International Frequency Sensor Association (IFSA) Publishing, S. L.: Chamonix-Mont-Blanc, France, 2021; pp. 98–103.
Sharkawy, A.-N. A Survey on Applications of Human-Robot Interaction. Sens. Transducers 2021, 251, 19–27.
Kruger, J.; Lien, T.K.; Verl, A. Cooperation of human and machines in assembly lines. CIRP Ann.-Manuf. Technol. 2009, 58, 628–646. [CrossRef]
Liu, C.; Tomizuka, M. Algorithmic Safety Measures for Intelligent Industrial Co-Robots. In Proceedings of the IEEE International Conference on Robotics and Automation 2016, Stockholm, Sweden, 16–21 May 2016; pp. 3095–3102.
Sharkawy, A.N.; Papakonstantinou, C.; Papakostopoulos, V.; Moulianitis, V.C.; Aspragathos, N. Task Location for High Performance Human-Robot Collaboration. J. Intell. Robot. Syst. Theory Appl. 2020, 100, 183–202. [CrossRef]
Sharkawy, A.-N. Intelligent Control and Impedance Adjustment for Efficient Human-Robot Cooperation. Ph.D. Thesis, University of Patras, Patras, Greece, 2020. [CrossRef]
Thomas, C.; Matthias, B.; Kuhlenkötter, B. Human—Robot Collaboration—New Applications in Industrial Robotics. In Proceedings of the International Conference in Competitive Manufacturing 2016 (COMA’16), Stellenbosch University, Stellenbosch, South Africa, 27–29 January 2016; pp. 1–7.
Billard, A.; Robins, B.; Nadel, J.; Dautenhahn, K. Building Robota, a Mini-Humanoid Robot for the Rehabilitation of Children With Autism. Assist. Technol. 2007, 19, 37–49. [CrossRef]
Robins, B.; Dickerson, P.; Stribling, P.; Dautenhahn, K. Robot-mediated joint attention in children with autism: A case study in robot-human interaction. Interact. Stud. 2004, 5, 161–198. [CrossRef]
Werry, I.; Dautenhahn, K.; Ogden, B.; Harwin, W. Can Social Interaction Skills Be Taught by a Social Agent? The Role of a Robotic Mediator in Autism Therapy. In Cognitive Technology: Instruments of Mind. CT 2001. Lecture Notes in Computer Science; Beynon, M., Nehaniv, C.L., Dautenhahn, K., Eds.; Springer: Berlin/Heidelberg, Germany, 2001; ISBN 978-3-540-44617-0.
Lum, P.S.; Burgar, C.G.; Shor, P.C.; Majmundar, M.; Loos, M. Van der Robot-Assisted Movement Training Compared With Conventional Therapy Techniques for the Rehabilitation of Upper-Limb Motor Function After Stroke. Arch. Phys. Med. Rehabil. Vol. 2002, 83, 952–959. [CrossRef]
COVID-19 Test Robot as a Tireless Colleague in the Fight against the Virus. Available online: https://www.kuka.com/en-de/ press/news/2020/06/robot-helps-with-coronavirus-tests (accessed on 24 June 2020).
Vasconez, J.P.; Kantor, G.A.; Auat Cheein, F.A. Human—Robot interaction in agriculture: A survey and current challenges. Biosyst. Eng. 2019, 179, 35–48. [CrossRef]
Baxter, P.; Cielniak, G.; Hanheide, M.; From, P. Safe Human-Robot Interaction in Agriculture. In Proceedings of the HRI’18 Companion, Session; Late-Breaking Reports, Chicago, IL, USA, 5–8 March 2018.
Smart Robot Installed Inside Greenhouse Care. Available online: https://www.shutterstock.com/image-photo/smart-robotinstalled-inside-greenhouse-care-765510412 (accessed on 17 June 2022).
Lyon, N. Robot Turns Its Eye to Weed Recognition at Narrabri. Available online: https://www.graincentral.com/ag-tech/dronesand-automated-vehicles/robot-turns-its-eye-to-weed-recognition-at-narrabri/ (accessed on 19 October 2018).
Bergerman, M.; Maeta, S.M.; Zhang, J.; Freitas, G.M.; Hamner, B.; Singh, S.; Kantor, G. Robot farmers: Autonomous orchard vehicles help tree fruit production. IEEE Robot. Autom. Mag. 2015, 22, 54–63. [CrossRef]
Freitas, G.; Zhang, J.; Hamner, B.; Bergerman, M.; Kantor, G. A low-cost, practical localization system for agricultural vehicles. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Springer: Berlin/Heidelberg, Germany, 2012; Volume 7508, pp. 365–375; ISBN 9783642335020.
Cooper, M.; Keating, D.; Harwin, W.; Dautenhahn, K. Robots in the classroom—Tools for accessible education. In Assistive Technology on the Threshold of the New Millennium, Assistive Technology Research Series; Buhler, C., Knops, H., Eds.; ISO Press: Düsseldorf, Germany, 1999; pp. 448–452.
Han, J.; Jo, M.; Park, S.; Kim, S. The Educational Use of Home Robots for Children. In Proceedings of the ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, Nashville, TN, USA, 13–15 August 2005; pp. 378–383.
How Robotics Is Changing the Mining Industry. Available online: https://eos.org/features/underground-robots-how-roboticsis-changing-the-mining-industry (accessed on 13 May 2019).
Bandoim, L. Grocery Retail Lessons from the Coronavirus Outbreak for the Robotic Future. Available online: https://www.forbes.com/sites/lanabandoim/2020/04/14/grocery-retail-lessons-from-the-coronavirus-outbreak-for-the-robotic-future/?sh=5c0 dfe1b15d1 (accessed on 14 April 2020).
Dautenhahn, K. Methodology & Themes of Human-Robot Interaction: A Growing Research Field. Int. J. Adv. Robot. Syst. 2007, 4, 103–108.
Moniz, A.B.; Krings, B. Robots Working with Humans or Humans Working with Robots ? Searching for Social Dimensions in New Human-Robot Interaction in Industry. Societies 2016, 6, 23. [CrossRef]
De Santis, A.; Siciliano, B.; De Luca, A.; Bicchi, A. An atlas of physical human—Robot interaction. Mech. Mach. Theory 2008, 43, 253–270. [CrossRef]
Khatib, O.; Yokoi, K.; Brock, O.; Chang, K.; Casal, A. Robots in Human Environments: Basic Autonomous Capabilities. Int. J. Rob. Res. 1999, 18, 684–696. [CrossRef]
Song, P.; Yu, Y.; Zhang, X. A Tutorial Survey and Comparison of Impedance Control on Robotic Manipulation. Robotica 2019, 37, 801–836. [CrossRef]
Hogan, N. Impedance control: An approach to manipulation: Part I theory; Part II implementation; Part III applications. J. Dynamlc Syst. Meas. Contral 1985, 107, 1–24. [CrossRef]
Sam Ge, S.; Li, Y.; Wang, C. Impedance adaptation for optimal robot—Environment interaction. Int. J. Control 2014, 87, 249–263.
Ott, C.; Mukherjee, R.; Nakamura, Y. Unified impedance and admittance control. In Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA, 3–7 May 2010; pp. 554–561.
Song, P.; Yu, Y.; Zhang, X. Impedance control of robots: An overview. In Proceedings of the 2017 2nd International Conference on Cybernetics, Robotics and Control (CRC), Chengdu, China, 21–23 July 2017; pp. 51–55.
Dimeas, F. Development of Control Systems for Human-Robot Collaboration in Object Co-Manipulation. Ph.D. Thesis, University of Patras, Patras, Greece, 2017.
Adams, R.J.; Hannaford, B. Stable Haptic Interaction with Virtual Environments. IEEE Trans. Robot. Autom. 1999, 15, 465–474. [CrossRef]
Ott, C. Cartesian Impedance Control of Redundant and Flexible-Joint Robots; Siciliano, B., Khatib, O., Groen, F., Eds.; Springer Tracts in Advanced Robotics; Springer: Berlin/Heidelberg, Germany, 2008; Volume 49, pp. 1–192. ISBN 9783540692539.
Duchaine, V.; Gosselin, M. General Model of Human-Robot Cooperation Using a Novel Velocity Based Variable Impedance Control. In Proceedings of the Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (WHC’07), Tsukuba, Japan, 22–24 March 2007; pp. 446–451.
Sharkawy, A.-N.; Koustoumpardis, P.N.; Aspragathos, N. A recurrent neural network for variable admittance control in human— Robot cooperation: Simultaneously and online adjustment of the virtual damping and Inertia parameters. Int. J. Intell. Robot. Appl. 2020, 4, 441–464. [CrossRef]
Yang, C.; Peng, G.; Li, Y.; Cui, R.; Cheng, L.; Li, Z. Neural networks enhanced adaptive admittance control of optimized robot-environment interaction. IEEE Trans. Cybern. 2019, 49, 2568–2579. [CrossRef] [PubMed]
Sidiropoulos, A.; Kastritsi, T.; Papageorgiou, D.; Doulgeri, Z. A variable admittance controller for human-robot manipulation of large inertia objects. In Proceedings of the 2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021, Vancouver, BC, Canada, 8–12 August 2021; pp. 509–514.
Topini, A.; Sansom, W.; Secciani, N.; Bartalucci, L.; Ridolfi, A.; Allotta, B. Variable Admittance Control of a Hand Exoskeleton for Virtual Reality-Based Rehabilitation Tasks. Front. Neurorobot. 2022, 15, 1–18. [CrossRef] [PubMed]
Wang, Y.; Yang, Y.; Zhao, B.; Qi, X.; Hu, Y.; Li, B.; Sun, L.; Zhang, L.; Meng, M.Q.H. Variable admittance control based on trajectory prediction of human hand motion for physical human-robot interaction. Appl. Sci. 2021, 11, 5651. [CrossRef]
Du, Z.; Wang, W.; Yan, Z.; Dong, W.; Wang, W. Variable Admittance Control Based on Fuzzy Reinforcement Learning for Minimally Invasive Surgery Manipulator. Sensors 2017, 17, 844. [CrossRef] [PubMed]
Dimeas, F.; Aspragathos, N. Fuzzy Learning Variable Admittance Control for Human-Robot Cooperation. In Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), Chicago, IL, USA, 14–18 September 2014; pp. 4770–4775.
Tsumugiwa, T.; Yokogawa, R.; Hara, K. Variable Impedance Control with Regard to Working Process for Man-Machine Cooperation-Work System. In Proceedings of the 2001 IEEE/RsI International Conference on Intelligent Robots and Systems, Maui, HI, USA, 29 October–3 November 2001; pp. 1564–1569.
Lecours, A.; Mayer-st-onge, B.; Gosselin, C. Variable admittance control of a four-degree-of-freedom intelligent assist device. In Proceedings of the 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA, 14–18 May 2012; pp. 3903–3908.
Okunev, V.; Nierhoff, T.; Hirche, S. Human-preference-based Control Design: Adaptive Robot Admittance Control for Physical Human-Robot Interaction. In Proceedings of the 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, Paris, France, 9–13 September 2012; pp. 443–448.
Landi, C.T.; Ferraguti, F.; Sabattini, L.; Secchi, C.; Bonf, M.; Fantuzzi, C. Variable Admittance Control Preventing Undesired Oscillating Behaviors in Physical Human-Robot Interaction. In Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada, 24–28 September 2017; pp. 3611–3616.
Sharkawy, A.-N.; Koustoumpardis, P.N.; Aspragathos, N. Variable Admittance Control for Human—Robot Collaboration based on Online Neural Network Training. In Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), Madrid, Spain, 1–5 October 2018.
Sharkawy, A.-N.; Koustoumpardis, P.N.; Aspragathos, N. A Neural Network based Approach for Variable Admittance Control in Human- Robot Cooperation: Online Adjustment of the Virtual Inertia. Intell. Serv. Robot. 2020, 13, 495–519. [CrossRef]
Sauro, J. A Practical Guide to the System Usability Scale: Background, Benchmarks and Best Practices; CreateSpace Independent Publishing Platform: Scotts Valley, CA, USA, 2011; pp. 1–162.
Ficuciello, F.; Villani, L.; Siciliano, B. Variable Impedance Control of Redundant Manipulators for Intuitive Human-Robot Physical Interaction. IEEE Trans. Robot. 2015, 31, 850–863. [CrossRef]
Gualtieri, L.; Rauch, E.; Vidoni, R. Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review. Robot. Comput. Integr. Manuf. 2021, 67, 101998. [CrossRef]
ISO 10218-1; Robots and Robotic Devices—Safety Requirements for Industrial Robots—Part 1: Robots. ISO Copyright Office: Zurich, Switzerland, 2011.
ISO 10218-2; Robots and robotic devices—Safety Requirements for Industrial Robots—Part 2: Robot Systems and Integration. ISO Copyright Office: Zurich, Switzerland, 2011.
ISO/TS 15066; Robots and Robotic Devices—Collaborative Robots. ISO Copyright Office: Geneva, Switzerland, 2016.
Yamada, Y.; Hirasawa, Y.; Huang, S.; Umetani, Y.; Suita, K. Human—Robot Contact in the Safeguarding Space. IEEE/ASME Trans. Mechatron. 1997, 2, 230–236. [CrossRef]
Flacco, F.; Kroger, T.; De Luca, A.; Khatib, O. A Depth Space Approach to Human-Robot Collision Avoidance. In Proceedings of the 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA, 14–18 May 2012; pp. 338–345.
Schmidt, B.; Wang, L. Contact-less and Programming-less Human-Robot Collaboration. In Proceedings of the Forty Sixth CIRP Conference on Manufacturing Systems 2013; Elsevier: Amsterdam, The Netherlands, 2013; Volume 7, pp. 545–550.
Anton, F.D.; Anton, S.; Borangiu, T. Human-Robot Natural Interaction with Collision Avoidance in Manufacturing Operations. In Service Orientation in Holonic and Multi Agent Manufacturing and Robotics; Springer: Berlin/Heidelberg, Germany, 2013; pp. 375–388. ISBN 9783642358524.
Kitaoka, M.; Yamashita, A.; Kaneko, T. Obstacle Avoidance and Path Planning Using Color Information for a Biped Robot Equipped with a Stereo Camera System. In Proceedings of the 4th Asia International Symposium on Mechatronics, Singapore, 15–18 December 2010; pp. 38–43.
Lenser, S.; Veloso, M. Visual Sonar: Fast Obstacle Avoidance Using Monocular Vision. In Proceedings of the Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), Las Vegas, NV, USA, 27–31 October 2003.
Peasley, B.; Birchfield, S. Real-Time Obstacle Detection and Avoidance in the Presence of Specular Surfaces Using an Active 3D Sensor. In Proceedings of the 2013 IEEE Workshop on Robot Vision (WORV), Clearwater Beach, FL, USA, 15–17 January 2013; pp. 197–202.
Flacco, F.; Kroeger, T.; De Luca, A.; Khatib, O. A Depth Space Approach for Evaluating Distance to Objects. J. Intell. Robot. Syst. 2014, 80, 7–22. [CrossRef]
Gandhi, D.; Cervera, E. Sensor Covering of a Robot Arm for Collision Avoidance. In Proceedings of the SMC’03 Conference Proceedings 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme—System Security and Assurance (Cat. No.03CH37483), Washington, DC, USA, 8 October 2003; pp. 4951–4955.
Lam, T.L.; Yip, H.W.; Qian, H.; Xu, Y. Collision Avoidance of Industrial Robot Arms using an Invisible Sensitive Skin. In Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura-Algarve, Portugal, 7–12 October 2012; pp. 4542–4543.
Shi, L.; Copot, C.; Vanlanduit, S. A Bayesian Deep Neural Network for Safe Visual Servoing in Human–Robot Interaction. Front. Robot. AI 2021, 8, 1–13. [CrossRef]
Haddadin, S.; Albu-sch, A.; De Luca, A.; Hirzinger, G. Collision Detection and Reaction: A Contribution to Safe Physical Human-Robot Interaction. In Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, 22–26 September 2008; pp. 3356–3363.
Cho, C.; Kim, J.; Lee, S.; Song, J. Collision detection and reaction on 7 DOF service robot arm using residual observer. J. Mech. Sci. Technol. 2012, 26, 1197–1203. [CrossRef]
Jung, B.; Choi, H.R.; Koo, J.C.; Moon, H. Collision Detection Using Band Designed Disturbance Observer. In Proceedings of the 8th IEEE International Conference on Automation Science and Engineering, Seoul, Korea, 20–24 August 2012; pp. 1080–1085.
Cao, P.; Gan, Y.; Dai, X. Model-based sensorless robot collision detection under model uncertainties with a fast dynamics identification. Int. J. Adv. Robot. Syst. 2019, 16, 1729881419853713. [CrossRef]
Morinaga, S.; Kosuge, K. Collision Detection System for Manipulator Based on Adaptive Impedance Control Law. In Proceedings of the 2003 IEEE International Conference on Robotics &Automation, Taipei, Taiwan, 14–19 September 2003; pp. 1080–1085.
Kim, J. Collision detection and reaction for a collaborative robot with sensorless admittance control. Mechatronics 2022, 84, 102811. [CrossRef]
Lu, S.; Chung, J.H.; Velinsky, S.A. Human-Robot Collision Detection and Identification Based on Wrist and Base Force/Torque Sensors. In Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain, 18–22 April 2005; pp. 796–801.
Dimeas, F.; Avenda, L.D.; Nasiopoulou, E.; Aspragathos, N. Robot Collision Detection based on Fuzzy Identification and Time Series Modelling. In Proceedings of the RAAD 2013, 22nd InternationalWorkshop on Robotics in Alpe-Adria-Danube Region, Portoroz, Slovenia, 11–13 September 2013.
Dimeas, F.; Avendano-valencia, L.D.; Aspragathos, N. Human—Robot collision detection and identification based on fuzzy and time series modelling. Robotica 2014, 33, 1886–1898. [CrossRef]
Franzel, F.; Eiband, T.; Lee, D. Detection of Collaboration and Collision Events during Contact Task Execution. In Proceedings of the IEEE-RAS International Conference on Humanoid Robots, Munich, Germany, 19–21 July 2021; pp. 376–383.
Cioffi, G.; Klose, S.; Wahrburg, A. Data-Efficient Online Classification of Human-Robot Contact Situations. In Proceedings of the 2020 European Control Conference (ECC), St. Petersburg, Russia, 12–15 May 2020; pp. 608–614.
Briquet-Kerestedjian, N.; Wahrburg, A.; Grossard, M.; Makarov, M.; Rodriguez-Ayerbe, P. Using neural networks for classifying human-robot contact situations. In Proceedings of the 2019 18th European Control Conference, ECC 2019, Naples, Italy, 25–28 June 2019; pp. 3279–3285.
Sharkawy, A.-N.; Aspragathos, N. Human-Robot Collision Detection Based on Neural Networks. Int. J. Mech. Eng. Robot. Res. 2018, 7, 150–157. [CrossRef]
Sharkawy, A.-N.; Koustoumpardis, P.N.; Aspragathos, N. Manipulator Collision Detection and Collided Link Identification based on Neural Networks. In Advances in Service and Industrial Robotics. RAAD 2018. Mechanisms and Machine Science; Nikos, A., Panagiotis, K., Vassilis, M., Eds.; Springer: Cham, Switzerland, 2018; pp. 3–12.
Sharkawy, A.N.; Koustoumpardis, P.N.; Aspragathos, N. Neural Network Design for Manipulator Collision Detection Based only on the Joint Position Sensors. Robotica 2020, 38, 1737–1755. [CrossRef]
Sharkawy, A.N.; Koustoumpardis, P.N.; Aspragathos, N. Human–robot collisions detection for safe human–robot interaction using one multi-input–output neural network. Soft Comput. 2020, 24, 6687–6719. [CrossRef]
Sharkawy, A.-N.; Mostfa, A.A. Neural Networks’ Design and Training for Safe Human-Robot Cooperation. J. King Saud Univ. Eng. Sci. 2021, 1–15. [CrossRef]
Sotoudehnejad, V.; Takhmar, A.; Kermani, M.R.; Polushin, I.G. Counteracting modeling errors for sensitive observer-based manipulator collision detection. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Vilamoura-Algarve, Portugal, 7–12 October 2012; pp. 4315–4320.
De Luca, A.; Albu-Schäffer, A.; Haddadin, S.; Hirzinger, G. Collision detection and safe reaction with the DLR-III lightweight manipulator arm. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Beijing, China, 9–15 October 2006; pp. 1623–1630.
Sharkawy, A.-N. Principle of Neural Network and Its Main Types: Review. J. Adv. Appl. Comput. Math. 2020, 7, 8–19. [CrossRef]
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