Hostname: page-component-8448b6f56d-xtgtn Total loading time: 0 Render date: 2024-04-16T13:41:12.247Z Has data issue: false hasContentIssue false
  • English
  • Français

Importing the Human Factor into Safe Human–Robot Interaction Function Using the Bond Graph Method

Published online by Cambridge University Press:  12 August 2020

Po-Jen Cheng Open the ORCID record for Po-Jen Cheng [Opens in a new window] ,
Hsiang-Yuan Ting Open the ORCID record for Hsiang-Yuan Ting [Opens in a new window]  and
Han-Pang Huang Open the ORCID record for Han-Pang Huang [Opens in a new window]
Po-Jen Cheng
Affiliation:
Department of Product Development, TECHMAN ROBOT Inc., Taoyuan, 33383, Taiwan, R.O.C.
Hsiang-Yuan Ting
Affiliation:
Department of Mechanical Engineering, National Taiwan University, Taipei, 10617, Taiwan, R.O.C.
Han-Pang Huang*
Affiliation:
Department of Mechanical Engineering, National Taiwan University, Taipei, 10617, Taiwan, R.O.C.
*
*Corresponding author. E-mail: hanpang@ntu.edu.tw
Get access Rights & Permissions [Opens in a new window]

Summary

The variable stiffness actuator (VSA) is helpful to realize the post-collision safety strategies for safe human–robot interaction.1 The stiffness of the robot will be reduced to protect the user from injury when the collision between the robot and human occurs. However, The VSA has a mechanism limit constraint that can cause harm to users even if the stiffness is minimized. Accordingly, in this article, a concept combining danger index and robust fault detection and isolation is presented and applied to active–passive variable stiffness elastic actuator (APVSEA). APVSEA can actively change joint stiffness with the change of danger index. Experimental results show that this concept can effectively confirm the fault mode and provide additional protection measures to ensure the safety of users when the joint stiffness has been adjusted to the minimum.

Keywords

SafeHuman–robot interactionBond graph methodFault detectionVariable stiffness actuator
Type
Articles
Information
Robotica , Volume 39 , Issue 5 , May 2021 , pp. 772 - 786
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Tadele, T. S., de Vries, T. and Stramigioli, S., “The safety of domestic robotics: A survey of various safety-related publications,” IEEE Robot. Autom. Mag. 21(3), 134–142 (2014).CrossRefGoogle Scholar
Bicchi, A., Peshkin, M. A. and Colgate, J. E., “Safety for Physical Human–Robot Interaction,” In: Springer Handbook of Robotics (Siciliano, B. and Khatib, O., eds.), Ch. 58 (Springer, Berlin, 2008) pp. 1335–1348.CrossRefGoogle Scholar
Bicchi, A., Bavaro, M., Boccadamo, G., Carli, D. D., Filippini, R., Grioli, G., Piccigallo, M., Rosi, A., Schiavi, R., Sen, S. and Tonietti, G., “Physical human-robot interaction: Dependability, safety, and performance,” Proceedings of the IEEE International Workshop on Advanced Motion Control (AMC), Trento, Italy (March 2008), pp. 9–14.CrossRefGoogle Scholar
Huang, M. B. and Huang, H. P., “Innovative human-like dual robotic hand mechatronic design and its chess-playing experiment,” IEEE Access. 7(1), 7872–7888 (2019).CrossRefGoogle Scholar
Lasota, P. A., Fong, T. and Shah, J. A., “A survey of methods for safe human–robot interaction,” Found. Trends Robot. 5(4), 261–349 (2017).CrossRefGoogle Scholar
Villani, V., Pini, F., Leali, F. and Secchi, C., “Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications,” Mechatronics 55, 248–266 (2018).CrossRefGoogle Scholar
Ting, H. Y., Hsu, H. K., Huang, M. B. and Huang, H. P., “Safety of human-robot interaction: Concepts and implementation based on robot-related standards,” J. Chinese Soc. Mech. Engin. (JCSME) 41(2), 199–209 (2020).Google Scholar
Yamada, Y., Morizono, T., Umetani, Y. and Takahashi, H., “Highly soft viscoelastic robot skin with a contact object-location-sensing capability,” IEEE Tran. Ind. Elec. 52(4), 960–968 (2005).CrossRefGoogle Scholar
Ham, R. V., Sugar, T. G., Vanderborght, B., Hollander, K. W. and Lefeber, D., “Compliant actuator designs,” IEEE Robot. Autom. Mag. 16(3), 81–94 (2009).CrossRefGoogle Scholar
Park, J. J., Lee, Y. J., Song, J. B. and Kim, H. S., “Safe Joint Mechanism Based on Nonlinear Stiffness for Safe Human-Robot Collision,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Pasadena, California (2008), pp. 2177–2182.Google Scholar
Schiavi, R., Grioli, G., Sen, S. and Bicchi, A., “VSA-II: A Novel Prototype of Variable Stiffness Actuator for Safe and Performing Robots Interacting with Humans,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Pasadena, California (May 2008), pp. 2171–2176.CrossRefGoogle Scholar
Tonietti, G., Schiavi, R. and Bicchi, A., “Design and Control of a Variable Stiffness Actuator for Safe and Fast Physical Human/Robot Interaction,” Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain (April 2005), pp. 526–531.Google Scholar
Vuong, N. D., Li, R., Chew, C. M., Jafari, A. and Polden, J., “A novel variable stiffness mechanism with linear spring characteristic for machining operations,” Robotica 35(7), 1627–1637 (2017).CrossRefGoogle Scholar
Wolf, S., Bahls, T., Chalon, M., Friedl, W., Grebenstein, M., Höppner, H., Kühne, M., Lakatos, D., Mansfeld, N., Özparpucu, M. C., Petit, F., Reinecke, J., Weitschat, R. and Albu-Schäffer, A., “Soft Robotics with Variable Stiffness Actuators: Tough Robots for Soft Human Robot Interaction,” In: Soft Robotics (Verl, A., Albu-Schäffer, A., Brock, O. and Raatz, A., eds.), Ch. 20 (Springer, Berlin, 2015) pp. 231–254.CrossRefGoogle Scholar
Li, X., Xiang, Y. H. Liu and H. Yu, “Adaptive Impedance Control for Compliantly Actuated Robots with a Unified Safety Measure.” Proceedings of the World Congress on Intelligent Control and Automation (WCICA), Changsha, China (July 2018), pp. 444–449.CrossRefGoogle Scholar
Hwang, I., Kim, S., Kim, Y. and Seah, C. E., “A survey of fault detection, isolation, and reconfiguration methods,” IEEE Tran. Control Sys. Tech. 18(3), 636–653 (2010).CrossRefGoogle Scholar
Venkatasubramanian, V., Rengaswamy, R., Yin, K. and Kavuri, S. N., “A review of process fault detection and diagnosis part I: Quantitative model-based methods,” Comp. Chem. Eng. 27(3), 293–311 (2003).CrossRefGoogle Scholar
Venkatasubramanian, V., Rengaswamy, R., Yin, K. and Kavuri, S. N., “A review of process fault detection and diagnosis part II: Qualitative models and search strategies,” Comp. Chem. Eng. 27(3), 313–326 (2003).CrossRefGoogle Scholar
Venkatasubramanian, V., Rengaswamy, R., Yin, K. and Kavuri, S. N., “A review of process fault detection and diagnosis part III: Process history based methods,” Comp. Chem. Eng. 27(3), 327–346 (2003).CrossRefGoogle Scholar
Medjaher, K., “A Bond Graph Model-based Fault Detection and Isolation,” In: Maintenance Modelling and Applications (Andrews, J., Bérenguer, CH. and Jackson, L., eds.), Ch. 6 (Det Norske Veritas, Hovik, 2011) pp. 497–506.Google Scholar
Samantaray, A. K. and Belkacem, B. O., “Bond Graph Model-based Quantitative FDI,” In: Model-based Process Supervision: A Bond Graph Approach, Ch. 5 (Springer, London, 2008), pp. 177–228.Google Scholar
Bouallegue, W., Bouabdallah, S. B. and Tagina, M., “A New Adaptive Fuzzy FDI Method for Bond Graph Uncertain Parameters Systems,” Proceedings of the IEEE International Conference on Electronics, Circuits and Systems (ICECS), Beirut, Lebanon (December, 2011), pp. 643–648.CrossRefGoogle Scholar
Bouallegue, W., Bouabdallah, S. B. and Tagina, M., “Causal Approaches and Fuzzy Logic in FDI of Bond Graph Uncertain Parameters Systems,” Proceedings of the International Conference on Communications, Computing and Control Applications (CCCA), Hammamet, Tunisia (March 2011), pp. 1–6.CrossRefGoogle Scholar
Emami, K., Nener, B., Sreeram, V., Trinh, H. and Fernando, T., “A Fault Detection Technique for Dynamical Systems,” Proceedings of the IEEE Conference on Industrial and Information Systems (ICIIS), Sri Lanka (August 2013), pp. 201–206.CrossRefGoogle Scholar
Pourbabaee, B., Meskin, N. and Khorasani, K., “Sensor Fault Detection and Isolation using Multiple Robust Filters for Linear Systems with Time-Varying Parameter Uncertainty and Error Variance Constraints,” Proceedings of the IEEE Conference on Control Applications (CCA), Juan Les Antibes, France (October 2014), pp. 382–389.CrossRefGoogle Scholar
Djeziri, M. A., Merzouki, R., Bunamama, B. O. and Dauphin-Tanguy, G., “Robust Fault Diagnosis by Using Bond Graph Approach,” IEEE Trans. Mechatron. 12(6), 599–611 (2007).CrossRefGoogle Scholar
Cheng, P. J. and Huang, H. P., “Robust Fault Detection and Isolation using Bond Graph for an Active-Passive Variable Serial Elastic Actuator,” Int. J. Robot. Auto. 6(2), 29–47 (2015).Google Scholar
Liu, H. and Yu, L., “Analytical Method of Fault Detection and Isolation Based on Bond Graph for Electromechanical Actuator,” Proceedings of the 2017 IEEE International Conference on Mechatronics and Automation (ICMA), Takamatsu, Japan (August 2017), pp. 393–397.CrossRefGoogle Scholar
Mojallal, A. and Lotfifard, S., “Multi-physics graphical model based fault detection and isolation in wind turbines,” IEEE Trans. Smart Grid 9(6), 5599–5612 (2018).CrossRefGoogle Scholar
Sidobre, D., Broquère, X., Mainprice, J., Burattini, E., Finzi, A., Rossi, S. and Staffa, M., “Human–Robot interaction,” In: Advanced Bimanual Manipulation (Siciliano, B., ed.), Ch. 3 (Springer, Berlin, 2012) pp. 123–172.CrossRefGoogle Scholar
Beyl, T., Nicolai, P., Raczkowsky, J., Worn, H., Comparetti, M. D. and Momi, E. D., “Multi Kinect People Detection for Intuitive and Safe Human Robot Cooperation in The Operating Room,” Proceedings of the International Conference on Advanced Robotics (ICAR), Montevideo, Uruguay (November 2013), pp. 1–6.CrossRefGoogle Scholar
Luca, A. D. and Flacco, F., “Integrated Control for pHRI: Collision Avoidance, Detection, Reaction and Collaboration,” Proceedings of the IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), Rome, Italy (June 2012), pp. 288–295.CrossRefGoogle Scholar
Morato, C., Kaipa, K. N., Zhao, B. and Gupta, S. K., “Toward safe human robot collaboration by using multiple kinects based real-time human tracking,” J. Com. Info. Sci. Engin. 14(1), 0110061–0110069 (2014).Google Scholar
Randelli, G., Bonanni, T., Iocchi, L. and Nardi, D., “Knowledge acquisition through human–robot multimodal interaction,” Intel. Serv. Robot. 6(1), 19–31 (2013).CrossRefGoogle Scholar
Yun, S. S., “A gaze control of socially interactive robots in multiple-person interaction,” Robotica 35(11), 2122–2138 (2017).CrossRefGoogle Scholar
Flacco, F., Kroger, T., Luca, A. D. and Khatib, O., “A Depth Space Approach to Human-Robot Collision Avoidance,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Saint Paul, Minnesota (May 2012), pp. 338–345.CrossRefGoogle Scholar
Rybski, P., Anderson-Sprecher, P., Huber, D., Niessl, C. and Simmons, R., “Sensor Fusion for Human Safety in Industrial Workcells,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Portugal (October 2012), pp. 3612–3619.CrossRefGoogle Scholar
Cheng, P. J. and Huang, H. P., “Model matching contorl for an active-passive variable stiffness actuator,” Int. J. Robot. Auto. 6(3), 48–64 (2015).Google Scholar
Cheng, P. J., Ting, H. Y. and Huang, H. P., “Safe human robot inter action using model matching control,” J. Chinese Soc. Mech. Engin. (JCSME) 37(6), 587–596 (2016).Google Scholar
Wang, R. J. and Huang, H. P., “Mechanically stiffness-adjustable actuator using a leaf spring for safe physical human-robot interaction,” Mechanika 18(1), 77–83 (2012).CrossRefGoogle Scholar
IFA, BG/BGIA risk assessment recommendations according to machinery directive: Design of workplaces with collaborative robots (2013).Google Scholar
Supplementary material: File

Cheng et al. supplementary material

Cheng et al. supplementary material

Download Cheng et al. supplementary material(File)
File 3.2 MB