Hostname: page-component-669899f699-8p65j Total loading time: 0 Render date: 2025-05-01T11:53:44.517Z Has data issue: false hasContentIssue false
  • English
  • Français

Toward safe and high-performance human–robot collaboration via implementation of redundancy and understanding the effects of admittance term parameters

Published online by Cambridge University Press:  15 November 2021

Mert Kanık ,
Orhan Ayit Open the ORCID record for Orhan Ayit [Opens in a new window] ,
Mehmet Ismet Can Dede Open the ORCID record for Mehmet Ismet Can Dede [Opens in a new window]  and
Enver Tatlicioglu Open the ORCID record for Enver Tatlicioglu [Opens in a new window]
Mert Kanık
Affiliation:
Biomedical Engineering Department, New Jersey Institute of Technology, Newark, NJ, USA
Orhan Ayit
Affiliation:
Department of Mechanical Engineering, Izmir Institute of Technology, Izmir, Turkey
Mehmet Ismet Can Dede*
Affiliation:
Department of Mechanical Engineering, Izmir Institute of Technology, Izmir, Turkey
Enver Tatlicioglu
Affiliation:
Department of Electrical and Electronics Engineering, Ege University, Izmir, Turkey
*
*Corresponding author. E-mail: candede@iyte.edu.tr
Get access Rights & Permissions [Opens in a new window]

Summary

Today, demandsin industrial manufacturing mandate humans to work with large-scale industrial robots, and this collaboration may result in dangerous conditions for humans. To deal with this situation, this work proposes a novel approach for redundant large-scale industrial robots. In the proposed approach, an admittance controller is designed to regulate the interaction between the end effector of the robot and the human. Additionally, an obstacle avoidance algorithm is implemented in the null space of the robot to prevent any possible unexpected collision between the human and the links of the robot. After safety performance of this approach is verified via simulations and experimental studies, the effect of the parameters of the admittance controller on the performance of collaboration in terms of both accuracy and total human effort is investigated. This investigation is carried out via 8 experiments by the participation of 10 test subjects in which the effect of different admittance controller parameters such as mass and damper are compared. As a result of this investigation, tuning insights for such parameters are revealed.

Keywords

collaborative robothuman–robot interactionadmittance controlobstacle avoidanceredundancy resolution
Type
Research Article
Information
Robotica , Volume 40 , Issue 7 , July 2022 , pp. 2112 - 2127
Copyright
© The Author(s), 2021. 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.)

Article purchase

Temporarily unavailable

References

Sherwani, F., Asad, M. M. and Ibrahim, B. S. K. K., “Collaborative Robots and Industrial Revolution 4.0 (IR 4.0),” 2020 International Conference on Emerging Trends in Smart Technologies (ICETST), IEEE, Karachi, Pakistan, 1–5 (2020).CrossRefGoogle Scholar
Gihleb, R., O. Giuntella, L. Stella and T. Wang, et al. Industrial Robots, Workers’ Safety, and Health (IZA - Institute of Labor Economics, Bonn, Germany, 2020).CrossRefGoogle Scholar
Perez, L., Rodríguez-Jiménez, S., Rodríguez, N., R. Usamentiaga, D. F. García and L. Wang, et al. “Symbiotic human–robot collaborative approach for increased productivity and enhanced safety in the aerospace manufacturing industry,” Int. J. Adv. Manuf. Technol. 5, 1–5 (2020).Google Scholar
iso.com, ISO 10218–1:2011. https://www.iso.org/standard/51330.html.Google Scholar
iso.com, ISO 10218–2:2011. https://www.iso.org/standard/41571.html.Google Scholar
Lešo, M., Žilková, J. and Vacek, M., “Robotic manipulator with optical safety system,” 2015 International Conference on Electrical Drives and Power Electronics (EDPE), 2015, pp. 389–393.CrossRefGoogle Scholar
Vogel, C., Walter, C. and Elkmann, N., “A projection-based sensor system for safe physical human-robot collaboration,” 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 5359–5364 (2013).CrossRefGoogle Scholar
Vogel, C., Walter, C. and Elkmann, N., “Safeguarding and supporting future human-robot cooperative manufacturing processes by a projection- and camera-based technology,” 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems 11, 39–46 (2017).Google Scholar
Marvel, J. A., Norcross, R., “Implementing speed and separation monitoring in collaborative robot workcells,” Rob. Comput. Integr. Manuf. 44, 144–155 (2017).CrossRefGoogle ScholarPubMed
Shi, D., Collins, E. G. Jr, Goldiez, B., Donate, A., Liu, X. and Dunlap, D., “Human-aware robot motion planning with velocity constraints,” 2008 International Symposium on Collaborative Technologies and Systems, IEEE, Irvine, CA, USA. 490–497 (2008).Google Scholar
Kanazawa, A., Kinugawa, J. and Kosuge, K., “Adaptive motion planning for a collaborative robot based on prediction uncertainty to enhance human safety and work efficiency,” IEEE Trans. Rob. 35, 817–832 (2019).10.1109/TRO.2019.2911800CrossRefGoogle Scholar
Himmelsbach, U. B., Wendt, T. M. and Lai, M., “Towards safe speed and separation monitoring in human-robot collaboration with 3D-time-of-flight cameras,” 2018 Second IEEE International Conference on Robotic Computing (IRC), 197–200 (2018).CrossRefGoogle Scholar
Andres, C. P. C., Hernandes, J. P. L., Baldelomar, L. T., et al., “Tri-Modal speed and separation monitoring technique using static-dynamic danger field implementation,” 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Springer, Cham, 1–6 (2018).CrossRefGoogle Scholar
iso.com, ISO 15066:2016. International Organization for Standardization, Vernier, Geneva, Switzerland. URL https://www.iso.org/standard/62996.html.Google Scholar
Lämmle, A., “Development of a new mechanic safety coupling for human robot collaboration using Magnetorheological fluids,” Procedia CIRP 81, 908–913 (2019).CrossRefGoogle Scholar
Kokkalis, K., Michalos, G., Aivaliotis, P., Makris, S., “An approach for implementing power and force limiting in sensorless industrial robots,” Procedia CIRP 76, 138–143 (2018).10.1016/j.procir.2018.01.028CrossRefGoogle Scholar
Maaroof, O. W., and Dede, M. İ. C., “Physical human-robot interaction: Increasing safety by robot arm’s posture optimization,” In: ROMANSY 21 – Robot Design, Dynamics and Control vol. 569, (2016).Google Scholar
Fanuc America, “Manufacturing with robots and automation,” Automation and Robotic Articles, URL: https://www.fanucamerica.com/news-resources/articles/the-facts-about-hand-guiding-robots, Accessed Date: 08.07.2021, (2020).Google Scholar
Hogan, N., “Impedance control – an approach to manipulation. I – Theory. II – Implementation. III – Applications,” ASME Trans. J. Dyn. Syst. Meas. Control 107, 1–24 (1985).CrossRefGoogle Scholar
Bascetta, L., Ferretti, G., Magnani, G., and Rocco, P., “Walk-through programming for robotic manipulators based on admittance control,” Robotica 31(7), 1143–1153 (2013).CrossRefGoogle Scholar
Ikeura, R., Monden, H. and Inooka, H., “Cooperative motion control of a robot and a human,” Proc. 1994 3rd IEEE Int. Workshop Robot Human Commun. 112–117 (1994).Google Scholar
Ikeura, R. and Inooka, H., “Variable impedance control of a robot for cooperation with a human,” Proc. 1995 IEEE Int. Conf. Rob. Autom. 3, 3097–3102 (1995).Google Scholar
Tee, K. P., Yan, R. and Li, H., “Adaptive admittance control of a robot manipulator under task space constraint,” 2010 IEEE Int. Conf. Rob. Autom. 5181–5186 (2010).Google Scholar
Hughes, D., Lammie, J. and Correll, N., “A Robotic skin for collision avoidance and affective touch recognition,” IEEE Rob. Autom. Lett. 3, 1386–1393 (2018).CrossRefGoogle Scholar
Matsuno, T., Wang, Z., Althoefer, K. and Hirai, S., “Adaptive update of reference capacitances in conductive fabric based robotic skin,” IEEE Rob. Autom. Lett. 4, 2212–2219 (2019).CrossRefGoogle Scholar
Ohta, P., Valle, L., King, J., Low, K., Yi, J., Atkeson, C. G. and Park, Y., “Design of a lightweight soft robotic arm using pneumatic artificial muscles and inflatable sleeves,” Soft Rob. 5, 204–215 (2018).CrossRefGoogle ScholarPubMed
Tsuji, S. and Kohama, T., “Proximity skin sensor using time-of-flight sensor for human collaborative robot,” IEEE Sens. J. 19 (14), 5859–5864 (2019).CrossRefGoogle Scholar
Kanik, M., “Compliance control of SHAD redundant robot”, Master thesis, Izmir Institute of Technology, Izmir, Turkey, 39–45 (2018).Google Scholar
Tatlicioglu, E., McIntyre, M., Dawson, D. and Walker, I., “Adaptive nonlinear tracking control of kinematically redundant robot manipulators with sub-task extensions,” Proc.44th IEEE Conf. Decis. Control, 4373–4378 (2005).CrossRefGoogle Scholar