Skip to main content
SpringerLink
Log in

Decentralized variable impedance control of modular robot manipulators with physical human–robot interaction using Gaussian process-based motion intention estimation

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper proposes a decentralized variable impedance control method of modular robot manipulators (MRM) with physical human-robot interaction (pHRI) using Gaussian process-based motion intention estimation. The dynamic model of MRM subsystem is established by using joint torque feedback (JTF) technique. Human limb dynamic model is regarded as mechanical impedance model, and human motion intention is estimated online based on Gaussian process. A variable impedance control method is proposed to make the MRM comply with human motion intention in the process of pHRI. A decentralized sliding mode control strategy is designed to achieve high performance position tracking and compensate the uncertainty of the controller. Based on Lyapunov theory, the uniform ultimately bounded of tracking error of each joint is proved. Finally, the effectiveness of the proposed control method under pHRI is verified by experiments. The experimental results show that the proposed method reduces the position tracking error by \( \sim \)10% and the interaction force by \(\sim \)20% compared with the existing control methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. An T, Wang Y, Liu G (2023) Cooperative game-based approximate optimal control of modular robot manipulators for human-robot collaboration. IEEE Trans Cybern 53(7):4691–4703

    Article  PubMed  Google Scholar 

  2. Ma B, Dong B, Zhou F, Li Y (2020) Adaptive dynamic programming-based fault-tolerant position-force control of constrained reconfigurable manipulators. IEEE Access 8:183286–183299

    Article  Google Scholar 

  3. Tremblay T, Padir T (2013) Modular robot arm design for physical human-robot interaction. IEEE International Conference on Systems, Man, and Cybernetics, pp 4482-4487

  4. Feng C, Xiao Y, Willette A, McGee W (2015) Vision guided autonomous robotic assembly and as-built scanning on unstructured construction sites. Autom Constr Technol 59:128–138

    Article  Google Scholar 

  5. Li X, Pan Y, Chen G, Yu H (2016) Adaptive human-robot interaction control for robots driven by series elastic actuators. IEEE Trans Rob 33(1):169–182

    Article  Google Scholar 

  6. Fleischer C, Hommel G (2008) A human-exoskeleton interface utilizing electromyography. Int J IEEE Trans Robot 24(4):872–882

    Article  Google Scholar 

  7. Deng X, Yu Z, Lin C (2019) A Bayesian shared control approach for wheelchair robot with brain machine interface. Int J IEEE Trans Neural Syst Rehab Eng. 28(1):328–338

    Article  Google Scholar 

  8. Kale SN, Dudul SV (2009) Intelligent noise removal from EMG signal using focused time-lagged recurrent neural network. Appl Comput Intell Soft Comput 1-9

  9. Ge S, Li Y, He H (2011) Neural-network-based human intention estimation for physical human-robot interaction. International Conference on Ubiquitous Robots and Ambient Intelligence, pp 390-395

  10. Li Y, Ge S (2013) Human-robot collaboration based on motion intention estimation. IEEE/ASME Trans Mechatron 19(3):1007–1014

    Article  Google Scholar 

  11. Yu X, He W, Li Y (2019) Bayesian estimation of human impedance and motion intention for human-robot collaboration. IEEE Trans Cybern 51(4):1822–1834

    Article  Google Scholar 

  12. Ali G, Al-Obeidat F, Tubaishat A (2021) Counterfactual explanation of Bayesian model uncertainty. Neural Comput Appl, pp 1-8

  13. Seeger M (2004) Gaussian processes for machine learning. Int J Neural Syst 14(02):69–106

    Article  PubMed  Google Scholar 

  14. Chen Z, Wang B, Gorban AN (2020) Multivariate Gaussian and Student-t process regression for multi-output prediction. Neural Comput Appl 32:3005–3028

    Article  Google Scholar 

  15. Wu M, Taetz B, Saraiva E (2019) On-line motion prediction and adaptive control in human-robot handover tasks. IEEE International Conference on Advanced Robotics and its Social Impacts, pp 1-6

  16. Medina J, Börner H, Endo S (2019) Impedance-based Gaussian processes for modeling human motor behavior in physical and non-physical interaction. IEEE Trans Biomed Eng 66(9):2499–2511

    Article  PubMed  Google Scholar 

  17. Hentout A, Aouache M, Maoudj A (2019) Human-robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017. Adv Robot 33(15–16):764–799

    Article  Google Scholar 

  18. Sharifi M, Zakerimanesh A, Mehr JK (2021) Impedance variation and learning strategies in human-robot interaction. IEEE Trans Cybern 52(7):6462–6475

    Article  Google Scholar 

  19. Millán-Arias C, Fernandes B, Cruz F (2022) Proxemic behavior in navigation tasks using reinforcement learning. Neural Comput Appl, pp 1-16

  20. Raibert MH, Craig JJ (1981) Hybrid position/force control of manipulators

  21. Hogan N (1985) Impedance control: an approach to manipulation: Part II-Implementation

  22. Jung S, Hsia TC, Bonitz RG (2004) Force tracking impedance control of robot manipulators under unknown environment. IEEE Trans Control Syst Technol 12(3):474–483

    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. J Int J Robot Res 20(9):765–774

    Article  Google Scholar 

  24. Li J, Liu L, Wang Y (2015) Adaptive hybrid impedance control of robot manipulators with robustness against environment’s uncertainties. IEEE International Conference on Mechatronics and Automation, pp 1846-1851

  25. Duan J, Gan Y, Chen M (2018) Adaptive variable impedance control for dynamic contact force tracking in uncertain environment. Robot Auton Syst 102:54–65

    Article  Google Scholar 

  26. Liu G (2002) Decomposition-based friction compensation of mechanical systems. Mechatronics 12(5):755–769

    Article  Google Scholar 

  27. Perić ZH, Dinčić MR (2014) Improved linearization of the optimal compression function for Laplacian source. J Electr Eng 65(3):179–183

    Google Scholar 

  28. Tsuji T, Takeda Y, Tanaka Y (2004) Analysis of mechanical impedance in human arm movements using a virtual tennis system. Biol Cybern 91:295–305

    Article  PubMed  Google Scholar 

  29. Snelson E, Ghahramani Z, Rasmussen C (2003) Warped gaussian processes. Adv Neural Inform Process Syst 16

  30. Bohrnstedt GW, Goldberger AS (1969) On the exact covariance of products of random variables. J Am Stat Assoc 64(328):1439–1442

    Article  Google Scholar 

  31. Duan J, Gan Y, Chen M et al (2018) Adaptive variable impedance control for dynamic contact force tracking in uncertain environment. Robot Auton Syst 102:54–65

    Article  Google Scholar 

  32. Li HY, Paranawithana I, Yang L (2018) Stable and compliant motion of physical human-robot interaction coupled with a moving environment using variable admittance and adaptive control. IEEE Robot Autom Lett 3(3):2493–2500

    Article  Google Scholar 

  33. Ficuciello F, Villani L, Siciliano B (2015) Variable impedance control of redundant manipulators for intuitive human-robot physical interaction. IEEE Trans Rob 31(4):850–863

    Article  Google Scholar 

  34. Campeau-Lecours A, Otis MJD, Gosselin C (2016) Modeling of physical human-robot interaction: admittance controllers applied to intelligent assist devices with large payload. Int J Adv Rob Syst 13(5):1729881416658167

    Google Scholar 

  35. Sun K, Li Y, Tong S (2016) Fuzzy adaptive output feedback optimal control design for strict-feedback nonlinear systems. IEEE Trans Syst Man Cybern: Syst 47(1):33–44

    Article  Google Scholar 

  36. Zhai J, Xu G (2020) A novel non-singular terminal sliding mode trajectory tracking control for robotic manipulators. IEEE Trans Circ Syst II Express Briefs 68(1):391–395

    Google Scholar 

  37. Lin G, Yu J, Liu J (2021) Adaptive fuzzy finite-time command filtered impedance control for robotic manipulators. IEEE Access 9:50917–50925

    Article  Google Scholar 

Download references

Acknowledgements

The work is supported by the National Natural Science Foundation of China (62173047), the Scientific Technological Development Plan Project in Jilin Province of China (20220201038GX), Key Laboratory of Advanced Structural Materials (Changchun University of Technology), Ministry of Education, China (ASM-202202).

Author information

Authors and Affiliations

  1. Key Laboratory of Advanced Structural Materials, Ministry of Education, Changchun University of Technology, Changchun, 130012, China

    Bo Dong, Shijie Li, Tianjiao An, Yiming Cui & Xinye Zhu

  2. Department of Control Science and Engineering, Changchun University of Technology, Changchun, 130012, China

    Bo Dong, Shijie Li, Tianjiao An, Yiming Cui & Xinye Zhu

Authors
  1. Bo Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shijie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tianjiao An
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yiming Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xinye Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tianjiao An.

Ethics declarations

Conflict of interest

The authors declared no potential conflicts of interest with respect to the research, authorship and publication of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, B., Li, S., An, T. et al. Decentralized variable impedance control of modular robot manipulators with physical human–robot interaction using Gaussian process-based motion intention estimation. Neural Comput & Applic 36, 6757–6769 (2024). https://doi.org/10.1007/s00521-024-09428-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-024-09428-0

Keywords

Search

Navigation