Control System Design and Methods for Collaborative Robots: Review
Source abstract
Abstract
Collaborative robots cooperate with humans to assist them in undertaking simple-to-complex tasks in several fields, including industry, education, agriculture, healthcare services, security, and space exploration. These robots play a vital role in the revolution of Industry 4.0, which defines new standards of manufacturing and the organization of products in the industry. Incorporating collaborative robots in the workspace improves efficiency, but it also introduces several safety risks. Effective safety measures then become indispensable to ensure safe and robust interaction. This paper presents the review of low-level control methodologies of a collaborative robot to assess the current status of human–robot collaboration over the last decade. First, we discuss the classification of human–robot collaboration, architectures of systems and the complex requirements on control strategies. The most commonly used control approaches were presented and discussed. Several methods of control, reported in industrial applications, are elaborated upon with a prime focus on HR-collaborative assembly operations. Since the physical HRC is a critical control problem for the co-manipulation task, this article identifies key control challenges such as the prediction of human intentions, safety, and human-caused disturbances in motion synchronization; the proposed solutions were analyzed afterwards. The discussion at the end of the paper summarizes the features of the control systems that should be incorporated within the systematic framework to allow the execution of a robotic task from global task planning to low-level control implementation for safe and robust interactions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Teleoperation and Haptic Systems · Robot Manipulation and Learning · Human-Automation Interaction and Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Automatic Control and Robotics, Warsaw University of Technology, 00-661 Warsaw, Poland
Institute of Micromechanics and Photonics, Warsaw University of Technology, 00-661 Warsaw, Poland
References (100+)
Show source-provided references
1. Cobot architecture10.1109/70.954751 ↗
2. KUKA (2022, December 31). KUKA Robotics, 2021. Available online: https://www.kuka.com/.
3. Simulation in the design and operation of manufacturing systems: State of the art and new trends10.1080/00207543.2019.1636321 ↗
4. MarketsMarkets (2022, December 31). Collaborative Robot Market by Payload Capacity (up to 5 Kg, between 5 and 10 K, above 10 K), Industry, Application, and G. Forecast to 2025. Technical Report. Available online: https://www.marketsandmarkets.com/Market-Reports/collaborative-robot-market-194541294.html.
5. Human–robot collaborative assembly in cyber-physical production: Classification framework and implementation10.1016/j.cirp.2017.04.101 ↗
6. Human–robot collaboration disassembly planning for end-of-life product disassembly process10.1016/j.rcim.2021.102170 ↗
7. Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review10.1016/j.rcim.2020.101998 ↗
8. Technology jump in the industry: Human–robot cooperation in production10.1108/IR-02-2020-0039 ↗
9. Human–robot interaction in industrial collaborative robotics: A literature review of the decade 2008–201710.1080/01691864.2019.1636714 ↗
10. An Ontology for Human-Robot Collaboration10.1016/j.procir.2020.04.045 ↗
11. Collaborative manufacturing with physical human–robot interaction10.1016/j.rcim.2015.12.007 ↗
12. Safety assurance mechanisms of collaborative robotic systems in manufacturing10.1016/j.rcim.2020.102022 ↗
13. Human Centered Assistance Applications for the working environment of the future10.3233/OER-150226 ↗
14. Matheson, E., Minto, R., Zampieri, E.G.G., Faccio, M., and Rosati, G. (2019). Human–Robot Collaboration in Manufacturing Applications: A Review. Robotics, 8.10.3390/robotics8040100 ↗
15. Symbiotic human–robot collaborative assembly10.1016/j.cirp.2019.05.002 ↗
16. Improving human robot collaboration through Force/Torque based learning for object manipulation10.1016/j.rcim.2020.102111 ↗
17. Mutual trust-based subtask allocation for human–robot collaboration in flexible lightweight assembly in manufacturing10.1016/j.mechatronics.2018.07.007 ↗
18. State-of-the-Art control strategies for robotic PiH assembly10.1016/j.rcim.2019.101894 ↗
19. Neural network–based direct adaptive robust control of unknown MIMO nonlinear systems using state observer10.1002/acs.3064 ↗
20. Human-Robot Collaborative Manipulation with the Suppression of Human-caused Disturbance10.1007/s10846-021-01429-8 ↗
21. RBFNN-Based Adaptive Sliding Mode Control Design for Delayed Nonlinear Multilateral Telerobotic System With Cooperative Manipulation10.1109/TII.2019.2927806 ↗
22. Fuzzy adaptive fixed-time sliding mode control with state observer for a class of high-order mismatched uncertain systems10.1007/s12555-019-0650-z ↗
23. Scholtz, J. (2003, January 6–9). Theory and evaluation of human robot interactions. Proceedings of the 36th Annual Hawaii International Conference on System Sciences, Big Island, HI, USA.10.1109/HICSS.2003.1174284 ↗
24. Cherubini, A., Passama, R., Meline, A., Crosnier, A., and Fraisse, P. (2013, January 3–7). Multimodal control for human–robot cooperation. Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan.10.1109/IROS.2013.6696664 ↗
25. Output-feedback adaptive control of networked teleoperation system with time-varying delay and bounded inputs10.1109/TMECH.2014.2359969 ↗
26. Adaptive fuzzy control of multilateral asymmetric teleoperation for coordinated multiple mobile manipulators10.1109/TFUZZ.2015.2426215 ↗
27. RBF-neural-network-based adaptive robust control for nonlinear bilateral teleoperation manipulators with uncertainty and time delay10.1109/TMECH.2019.2962081 ↗
28. Rosenstrauch, M.J., and Krüger, J. (2017, January 24–26). Safe human–robot-collaboration-introduction and experiment using ISO/TS 15066. Proceedings of the 3rd International Conference on Control, Automation and Robotics (ICCAR), Nagoya, Japan.10.1109/ICCAR.2017.7942795 ↗
29. Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications10.1016/j.mechatronics.2018.02.009 ↗
30. Asfour, T., Kaul, L., Wächter, M., Ottenhaus, S., Weiner, P., Rader, S., Grimm, R., Zhou, Y., Grotz, M., and Paus, F. (2018, January 6–9). ARMAR-6: A Collaborative Humanoid Robot for Industrial Environments. Proceedings of the 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), Beijing, China.10.1109/HUMANOIDS.2018.8624966 ↗
31. Reliability architecture for collaborative robot control systems in complex environments10.5772/62201 ↗
32. Liu, X.J., Nie, Z., Yu, J., Xie, F., and Song, R. Real-Time Design Based on PREEMPT_RT and Timing Analysis of Collaborative Robot Control System. Proceedings of the Intelligent Robotics and Applications.
33. Dumonteil, G., Manfredi, G., Devy, M., Confetti, A., and Sidobre, D. (2015, January 21–23). Reactive planning on a collaborative robot for industrial applications. Proceedings of the 2015 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Colmar, France.10.5220/0005575804500457 ↗
34. Parusel, S., Haddadin, S., and Albu-Schäffer, A. (2011, January 9–13). Modular state-based behavior control for safe human-robot interaction: A lightweight control architecture for a lightweight robot. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.10.1109/ICRA.2011.5980549 ↗
35. Xi, Q., Zheng, C.W., Yao, M.Y., Kou, W., and Kuang, S.L. (2021, January 14–16). Design of a Real-time Robot Control System oriented for Human-Robot Cooperation. Proceedings of the 2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), Guangzhou, China.10.1109/AIEA53260.2021.00013 ↗
36. A new generation of collaborative robots for material handling
37. Advanced Interfaces for Vehicle Teleoperation: Collaborative Control, Sensor Fusion Displays, and Remote Driving Tools10.1023/A:1011212313630 ↗
38. Haddadin, S., and Croft, E. (2016). Physical human-obot interaction. Springer Handbook of Robotics, Springer.10.1007/978-3-319-32552-1_69 ↗
39. A review of continuous contact-force models in multibody dynamics10.1016/j.ijmecsci.2018.07.010 ↗
40. Dynamic analysis of multiple inclined and frictional impact-contacts in multi-branch robotic systems10.1016/j.apm.2020.09.017 ↗
41. Symbolic derivation of governing equations for dual-arm mobile manipulators used in fruit-picking and the pruning of tall trees10.1016/j.compag.2014.04.013 ↗
42. Planar multibranch open-loop robotic manipulators subjected to ground collision
43. Ding, H., Reißig, G., Wijaya, K., Bortot, D., Bengler, K., and Stursberg, O. (2011, January 9–13). Human arm motion modeling and long-term prediction for safe and efficient Human-Robot-Interaction. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.10.1109/ICRA.2011.5980248 ↗
44. Computational Human-Robot Interaction
45. Lasota, P.A., Song, T., and Shah, J.A. (2017). A Survey of Methods for Safe Human-Robot Interaction, Now.10.1561/9781680832792 ↗
46. Facilitating Human–Robot Collaborative Tasks by Teaching-Learning-Collaboration From Human Demonstrations10.1109/TASE.2018.2840345 ↗
47. Ragaglia, M., Zanchettin, A.M., and Rocco, P. (2015, January 27–31). Safety-aware trajectory scaling for Human-Robot Collaboration with prediction of human occupancy. Proceedings of the 2015 International Conference on Advanced Robotics (ICAR), Istanbul, Turkey.10.1109/ICAR.2015.7251438 ↗
48. Model predictive control of a collaborative manipulator considering dynamic obstacles10.1002/oca.2599 ↗
49. Impedance Control: An Approach to Manipulation: Part I—Theory10.1115/1.3140702 ↗
50. Ott, C., Mukherjee, R., and Nakamura, Y. (2010, January 3–7). Unified Impedance and Admittance Control. Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA.10.1109/ROBOT.2010.5509861 ↗
51. Impedance Control: An Approach to Manipulation: Part II—Implementation10.1115/1.3140713 ↗
52. Benedictis, C.D., Franco, W., Maffiodo, D., and Ferraresi, C. (2018). Control of Force Impulse in Human–Machine Impact, Springer International Publishing.10.1007/978-3-319-61276-8_102 ↗
53. Adaptive variable impedance control for dynamic contact force tracking in uncertain environment10.1016/j.robot.2018.01.009 ↗
54. Double actuation architectures for rendering variable impedance in compliant robots: A review10.1016/j.mechatronics.2012.09.011 ↗
55. Adaptive Human–Robot Interaction Control for Robots Driven by Series Elastic Actuators10.1109/TRO.2016.2626479 ↗
56. Albu-Schaffer, A., and Hirzinger, G. (2002, January 11–15). Cartesian impedance control techniques for torque controlled light-weight robots. Proceedings of the 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), Washington, DC, USA.
57. Kosuge, K., Sato, M., and Kazamura, N. (2000, January 24–28). Mobile robot helper. Proceedings of the IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), San Francisco, CA, USA.
58. Geravand, M., Flacco, F., and Luca, A.D. (2013, January 6–10). Human–robot physical interaction and collaboration using an industrial robot with a closed control architecture. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.10.1109/ICRA.2013.6631141 ↗
59. Salter, T., Michaud, F., Létourneau, D., Lee, D.C., and Werry, I.P. (2007, January 10–12). Using proprioceptive sensors for categorizing human–robot interactions. Proceedings of the HRI ’07: Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, Arlington, VA, USA.10.1145/1228716.1228731 ↗
60. Ćehajić, D., Erhart, S., and Hirche, S. (October, January 28). Grasp pose estimation in human–robot manipulation tasks using wearable motion sensors. Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.10.1109/IROS.2015.7353497 ↗
61. Adaptive EKF Based on HMM Recognizer for Attitude Estimation Using MEMS MARG Sensors10.1109/JSEN.2017.2787578 ↗
62. Invariance Control for Safe Human–Robot Interaction in Dynamic Environments10.1109/TRO.2017.2750697 ↗
63. A Unified Passivity-based Control Framework for Position, Torque and Impedance Control of Flexible Joint Robots10.1177/0278364907073776 ↗
64. Gribovskaya, E., Kheddar, A., and Billard, A. (2011, January 9–13). Motion learning and adaptive impedance for robot control during physical interaction with humans. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.10.1109/ICRA.2011.5980070 ↗
65. Impedance control with adaptation for robotic manipulations10.1109/70.88152 ↗
66. Safety in human–robot collaborative manufacturing environments: Metrics and control10.1109/TASE.2015.2412256 ↗
67. Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities10.3389/fnbot.2020.576846 ↗
68. Hand Impedance Measurements During Interactive Manual Welding With a Robot10.1109/TRO.2014.2385212 ↗
69. Lippiello, V., Siciliano, B., and Villani, L. (2006, January 9–15). Robot Interaction Control Using Force and Vision. Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China.10.1109/IROS.2006.281974 ↗
70. Santis, A.D., Lippiello, V., Siciliano, B., and Luigi, V. (2007). Human-Robot Interaction Control Using Force and Vision. Advances in Control Theory and Applications, Springer.10.1007/978-3-540-70701-1_3 ↗
71. Safety Control of Industrial Robots Based on a Distributed Distance Sensor10.1109/TCST.2014.2300696 ↗
72. A Frequency Domain Approach for Contact Type Distinction in Human–Robot Collaboration10.1109/LRA.2017.2789249 ↗
73. An Extended Kalman Filter as an Observer in a Control Structure for Health Monitoring of a Metal–Polymer Hybrid Soft Actuator10.1109/TMECH.2018.2792321 ↗
74. Lacevic, B., and Rocco, P. (2010, January 18–22). Kinetostatic danger field—A novel safety assessment for human–robot interaction. Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan.10.1109/IROS.2010.5649124 ↗
75. Parker, C.A.C., and Croft, E.A. (2012, January 14–18). Design & Personalization of a Cooperative Carrying Robot Controller. Proceedings of the 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA.10.1109/ICRA.2012.6225120 ↗
76. Meziane, R., Li, P., Otis, M.J., Ezzaidi, H., and Cardou, P. (2014, January 16–18). Safer hybrid workspace using human–robot interaction while sharing production activities. Proceedings of the 2014 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Proceedings, Timisoara, Romania.10.1109/ROSE.2014.6952980 ↗
77. Michieletto, S., Ghidoni, S., Pagello, E., and Moro, M. (2014). Why teach robotics using ROS. J. Autom. Mob. Robot. Intell. Syst., 60–68.10.14313/JAMRIS_1-2014/8 ↗
78. Fong, T., Thorpe, C., and Baur, C. (2003). Collaboration, Dialogue, Human-Robot Interaction, Springer.
79. Kragic, D., Gustafson, J., Karaoguz, H., Jensfelt, P., and Krug, R. (2018). Interactive, Collaborative Robots: Challenges and Opportunities, AAAI Press.10.24963/ijcai.2018/3 ↗
80. An Integrated Framework for Human–Robot Collaborative Manipulation10.1109/TCYB.2014.2363664 ↗
81. Evaluating Fluency in Human–Robot Collaboration10.1109/THMS.2019.2904558 ↗
82. Bascetta, L., Ferretti, G., Rocco, P., Ardö, H., Bruyninckx, H., Demeester, E., and Lello, E.D. (2011, January 25–30). Towards safe human–robot interaction in robotic cells: An approach based on visual tracking and intention estimation. Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA.10.1109/IROS.2011.6094642 ↗
83. Berger, E., Vogt, D., Haji-Ghassemi, N., Jung, B., and Amor, H.B. (2013, January 15–17). Inferring guidance information in cooperative human-robot tasks. Proceedings of the 2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Atlanta, GA, USA.10.1109/HUMANOIDS.2013.7029966 ↗
84. Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances10.1007/s11071-019-05170-8 ↗
85. Schaal, S. (2006). Dynamic movement primitives-a framework for motor control in humans and humanoid robotics. Adaptive Motion of Animals and Machines, Springer.10.1007/4-431-31381-8_23 ↗
86. Shyam, R.A., Lightbody, P., Das, G., Liu, P., Gomez-Gonzalez, S., and Neumann, G. (2019, January 3–8). Improving local trajectory optimisation using probabilistic movement primitives. Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.10.1109/IROS40897.2019.8967980 ↗
87. Ding, H., Heyn, J., Matthias, B., and Staab, H. (2013, January 17–20). Structured collaborative behavior of industrial robots in mixed human–robot environments. Proceedings of the 2013 IEEE International Conference on Automation Science and Engineering (CASE), Madison, WI, USA.10.1109/CoASE.2013.6653962 ↗
88. Ding, H., Schipper, M., and Matthias, B. (2013, January 24–26). Collaborative behavior design of industrial robots for multiple human–robot collaboration. Proceedings of the IEEE ISR 2013, Seoul, Republic of Korea.10.1109/ISR.2013.6695707 ↗
89. Hawkins, K.P., Bansal, S., Vo, N.N., and Bobick, A.F. (June, January 31). Anticipating human actions for collaboration in the presence of task and sensor uncertainty. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.10.1109/ICRA.2014.6907165 ↗
90. Mariotti, E., Magrini, E., and Luca, A.D. (2019, January 20–24). Admittance Control for Human-Robot Interaction Using an Industrial Robot Equipped with a F/T Sensor. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.10.1109/ICRA.2019.8793657 ↗
91. Discrete-Time Formulation for Optimal Impact Control in Interaction Tasks10.1007/s10846-017-0683-6 ↗
92. Rahman, S.M., Wang, Y., Walker, I.D., Mears, L., Pak, R., and Remy, S. (2016, January 21–25). Trust-based compliant robot-human handovers of payloads in collaborative assembly in flexible manufacturing. Proceedings of the 2016 IEEE International Conference on Automation Science and Engineering (CASE), Fort Worth, TX, USA.
93. Rahman, S.M., Liao, Z., Jiang, L., and Wang, Y. (2016, January 21–25). A regret-based autonomy allocation scheme for human–robot shared vision systems in collaborative assembly in manufacturing. Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), Fort Worth, TX, USA.
94. Physical human–robot interaction (pHRI) in 6 DOF with asymmetric cooperation10.1109/ACCESS.2017.2708658 ↗
95. Bös, J., Wahrburg, A., and Listmann, K.D. (June, January 29). Iteratively learned and temporally scaled force control with application to robotic assembly in unstructured environments. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.10.1109/ICRA.2017.7989344 ↗
96. A design metric for safety assessment of industrial robot design suitable for power- and force-limited collaborative operation10.1007/s41315-018-0055-9 ↗
97. Darvish, K., Bruno, B., Simetti, E., Mastrogiovanni, F., and Casalino, G. (2018, January 27–31). Interleaved online task planning, simulation, task allocation and motion control for flexible human–robot cooperation. Proceedings of the 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Nanjing, China.10.1109/ROMAN.2018.8525644 ↗
98. Wojtynek, M., Oestreich, H., Beyer, O., and Wrede, S. (2017, January 11–14). Collaborative and robot-based plug & produce for rapid reconfiguration of modular production systems. Proceedings of the 2017 IEEE/SICE International Symposium on System Integration (SII), Taipei, Taiwan.10.1109/SII.2017.8279364 ↗
99. Seamless human robot collaborative assembly–An automotive case study10.1016/j.mechatronics.2018.08.006 ↗
100. ROS Based Coordination of Human Robot Cooperative Assembly Tasks-An Industrial Case Study10.1016/j.procir.2015.08.045 ↗