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A stability observer for human-robot and environment-robot interaction with variable admittance control

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Abstract

When the robot interacts with the environment or people, if the stiffness of the environment or people suddenly increases, the robot is prone to instability. Traditional solutions, such as the stability observer based on frequency, are easily affected by high-frequency signal noise or filter phase error, resulting in misdiagnosis. In addition, the adaptive algorithm keeps the contact force stable in the environment-robot interaction by identifying the environmental stiffness. Still, the change in the environmental stiffness is too significant, which may lead to the failure of the adaptive algorithm. Therefore, this paper proposes an improved observer stabilization method, using the ratio of the standard deviation of force to the maximum allowable force to eliminate the influence of high-frequency noise and reduce misdiagnosis. In addition, the designed stability observer can monitor the interaction between the robot and the environment in real-time and ensure the stable operation of the adaptive algorithm by updating the initial environment stiffness. Finally, some comparative experiments are carried out. The results show that the proposed method has good accuracy and robustness in human-robot and environment-robot interaction.

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References

  1. Sirintuna D, Ozdamar I, Aydin Y, Basdogan C (2020) Detecting human motion intention during PHRI using artificial neural networks trained by EMG signals. In: 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). https://doi.org/10.1109/RO-MAN47096.2020.9223438

  2. Zahedi F, Arnold J, Phillips C, Lee H (2021) Variable damping control for PHRI: considering stability, agility, and human effort in controlling human interactive robots. IEEE Trans Hum Mach Syst 51(5):504–513. https://doi.org/10.1109/THMS.2021.3090064

    Article  Google Scholar 

  3. Labrecque PD, Gosselin C (2018) Variable admittance for PHRI: from intuitive unilateral interaction to optimal bilateral force amplification. Robot Comput Integr Manuf 52:1–8. https://doi.org/10.1016/j.rcim.2018.01.005

    Article  Google Scholar 

  4. Campeau-Lecours A, Otis M, Belzile P-L, Gosselin C (2016) A time-domain vibration observer and controller for physical human-robot interaction. Mechatronics 36:45–53. https://doi.org/10.1016/j.mechatronics.2016.04.006

  5. Sassi MA, Otis MJ, Campeau-Lecours A (2017) Active stability observer using artificial neural network for intuitive physical human-robot interaction. Int J Adv Rob Syst 14(4):1729881417727326. https://doi.org/10.1177/1729881417727326

  6. Landi CT, Ferraguti F, Sabattini L, Secchi C, Bonfè M, Fantuzzi C (2017) Variable admittance control preventing undesired oscillating behaviors in physical human-robot interaction. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/IROS.2017.8206207. IEEE, pp 3611–3616

  7. Landi CT, Ferraguti F, Sabattini L, Secchi C, Fantuzzi C (2017) Admittance control parameter adaptation for physical human-robot interaction. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp 2911–2916. https://doi.org/10.1109/ICRA.2017.7989338

  8. Ferraguti F, Talignani Landi C, Sabattini L, Bonfè M, Fantuzzi C, Secchi C (2019) A variable admittance control strategy for stable physical human-robot interaction. Int J Robot Res 38(6):747–765. https://doi.org/10.1177/0278364919840415

    Article  Google Scholar 

  9. Ryu D, Song J-B, Kang S, Kim M (2008) Frequency domain stability observer and active damping control for stable haptic interaction. IET Control Theory Appl 2(4):261–268. https://doi.org/10.1049/iet-cta:20070069

    Article  Google Scholar 

  10. Dimeas F, Aspragathos N (2016) Online stability in human-robot cooperation with admittance control. IEEE Trans Haptics 9(2):267–278. https://doi.org/10.1109/TOH.2016.2518670

    Article  Google Scholar 

  11. Kim H, Yang W (2021) Variable admittance control based on human-robot collaboration observer using frequency analysis for sensitive and safe interaction. Sensors 21(5):1899. https://doi.org/10.3390/s21051899

    Article  Google Scholar 

  12. Duan J, Gan Y, Chen M, Dai X (2018) Adaptive variable impedance control for dynamic contact force tracking in uncertain environment. Robot Auton Syst 102:54–65. https://doi.org/10.1016/j.robot.2018.01.009

    Article  Google Scholar 

  13. Buerger SP, Hogan N (2007) Complementary stability and loop shaping for improved human-robot interaction. IEEE Trans Rob 23(2):232–244. https://doi.org/10.1109/TRO.2007.892229

    Article  Google Scholar 

  14. Mallapragada V, Erol D, Sarkar N (2007) A new method of force control for unknown environments. Int J Adv Rob Syst 4(3):34. https://doi.org/10.5772/5684

    Article  Google Scholar 

  15. Melo J, Sánchez E, Díaz I (2012) Adaptive admittance control to generate real-time assistive fixtures for a cobot in transpedicular fixation surgery. In: 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob). IEEE, pp 1170–1175. https://doi.org/10.1109/BioRob.2012.6290806

  16. Woo H, Kong K (2014) Controller design for mechanical impedance reduction. IEEE/ASME Trans Mechatron 20(2):845–854. https://doi.org/10.1109/TMECH.2014.2312795

  17. Ott C, Mukherjee R, Nakamura Y (2015) A hybrid system framework for unified impedance and admittance control. J Intell Robot Syst 78(3):359–375. https://doi.org/10.1007/s10846-014-0082-1

  18. Lamy X, Colledani F, Geffard F, Measson Y, Morel G (2009) Achieving efficient and stable comanipulation through adaptation to changes in human arm impedance. In: 2009 IEEE International Conference on Robotics and Automation. IEEE, pp 265–271. https://doi.org/10.1109/ROBOT.2009.5152294

  19. Singh SK, Popa DO (1995) An analysis of some fundamental problems in adaptive control of force and impedance behavior: theory and experiments. IEEE Trans Robot Autom 11(6):912–921. https://doi.org/10.1109/70.478439

    Article  Google Scholar 

  20. Erickson D, Weber M, Sharf I (2003) Contact stiffness and damping estimation for robotic systems. Int J Robot Res 22(1):41–57. https://doi.org/10.1177/0278364903022001004

    Article  Google Scholar 

  21. Haddadi A, Hashtrudi-Zaad K (2008) Online contact impedance identification for robotic systems. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp 974–980. https://doi.org/10.1109/IROS.2008.4650572

  22. Kim S, Ryu J (2022) Robust interaction control for environments having uncertainties. Robot Auton Syst 151:104023. https://doi.org/10.1016/j.robot.2022.104023

    Article  Google Scholar 

  23. Jung S, Hsia TC, Bonitz RG (2001) Force tracking impedance control for robot manipulators with an unknown environment: theory, simulation, and experiment. Int J Robot Res 20(9):765–774. https://doi.org/10.1177/02783640122067651

    Article  Google Scholar 

  24. Love LJ, Book WJ (2004) Force reflecting teleoperation with adaptive impedance control. IEEE Trans Syst Man Cybern B Cybern 34(1):159–165. https://doi.org/10.1109/TSMCB.2003.811756

  25. de Vlugt E, Schouten AC, van der Helm FC, Teerhuis PC, Brouwn GG (2003) A force-controlled planar haptic device for movement control analysis of the human arm. J Neurosci Methods 129(2):151–168. https://doi.org/10.1016/S0165-0270(03)00203-6

    Article  Google Scholar 

  26. Moon H, Kim C, Kwon M, Chen YT, Onushko T, Lodha N, Christou EA (2014) Force control is related to low-frequency oscillations in force and surface EMG. PLoS ONE 9(11):109202. https://doi.org/10.1371/journal.pone.0109202

    Article  Google Scholar 

  27. Seraji H, Colbaugh R (1997) Force tracking in impedance control. Int J Robot Res 16(1):97–117. https://doi.org/10.1177/027836499701600107

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Key R &D Program of China(2019YFB1309900), the National Natural Science Foundation of China (NSFC) Program-Co-Focus(U21A20122, 92048201), International Partnership Program, Bureau of International Cooperation, Chinese Academy of Sciences(174433KYSB20190036), Zhejiang Province "leading geese" attack plan project(2022C01101).

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Authors and Affiliations

  1. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, China

    Longxiang Wang & Yanbiao Li

  2. Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, Zhejiang, China

    Longxiang Wang, Chin-Yin Chen, Kaichen Ying & Guilin Yang

  3. AuBo(Beijing) Intelligent Technology Co, Beijing, 102308, Beijing, China

    Chongchong Wang

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  1. Longxiang Wang
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  2. Chin-Yin Chen
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  4. Kaichen Ying
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  5. Yanbiao Li
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Contributions

L.Wang contributed to theoretical development simulation, experiment validation and writing the original draft; C.-Y.Chen contributed to research idea, theoretical development, funding acquisition, revision of the manuscript and student supervision; C. Wang performed revision of the manuscript; K.Ying contributed to formal analysis; Y.Li and G.Yang performed funding acquisition, revision of the manuscript and student supervision.

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Correspondence to Chin-Yin Chen.

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Wang, L., Chen, CY., Wang, C. et al. A stability observer for human-robot and environment-robot interaction with variable admittance control. Int J Adv Manuf Technol 128, 437–450 (2023). https://doi.org/10.1007/s00170-023-11626-4

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  • DOI: https://doi.org/10.1007/s00170-023-11626-4

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