Skip to main content

Advertisement

Log in

Stable haptic rendering in interactive virtual control laboratory

  • Original Research Paper
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

Abstract

Stable control of haptic interfaces is one of the most important challenges in haptic simulations, because any instability of a haptic interface can cause it to get far from the realistic sense. In this paper, the control strategies employed for a stable haptic rendering in an interactive virtual control laboratory are presented. In this interactive virtual laboratory, there are different scenarios to teach the control concepts, in which a haptic interface is used in the two cases of force control and position control. In this regard, two control strategies are employed to avoid instability. An energy-compensating controller is utilized to remove energy leakage. Besides, a fuzzy impedance control is used along with the energy-compensating controller for the position control scenarios. The results obtained indicate the proposed approaches practically guarantee the stability of the haptic interface for an educational application in practice.

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

Access this article

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

Similar content being viewed by others

References

  1. Salisbury K, Conti F, Barbagli F (2004) Haptic rendering: introductory concepts. IEEE Comput Graph Appl 24(2):24–32

    Article  Google Scholar 

  2. Zhou M, Tse S, Derevianko A, Jones D, Schwaitzberg S, Cao C (2012) Effect of haptic feedback in laparoscopic surgery skill acquisition. Surg Endosc 26(4):1128–1134

    Article  Google Scholar 

  3. Shen X, Hamam A, Malric F, Nourian S, Naim R, Georganas ND (2007) Immersive haptic eye tele-surgery training simulation. In: 3DTV conference, 2007, IEEE, pp 1–4

  4. Wang D, Zhang Y, Wang Y, Lee Y-S, Lu P, Wang Y (2005) Cutting on triangle mesh: local model-based haptic display for dental preparation surgery simulation. IEEE Trans Vis Comput Graph 11(6):671–683

    Article  Google Scholar 

  5. Seidi E, Amirkhani S, Nahvi A (2015). A neuro-fuzzy model of soft tissue in haptic simulator for training diagnosis of breast cancer. In: 2015 3rd RSI international conference on robotics and mechatronics (ICROM), IEEE, pp 359–364

  6. Han I, Black JB (2011) Incorporating haptic feedback in simulation for learning physics. Comput Educ 57(4):2281–2290

    Article  Google Scholar 

  7. Sato M, Liu X, Murayama J, Akahane K, Isshiki M (2008) A haptic virtual environment for molecular chemistry education. Trans Ed I:28–39

    Google Scholar 

  8. Clark JE, Provancher WR, Mitiguy P (2005) Theory, simulation, and hardware: Lab design for an integrated system dynamics education. In: ASME 2005 international mechanical engineering congress and exposition, American society of mechanical engineers, pp 147–153

  9. Lopes D, Vaz de Carvalho C (2015) Simulation and haptic devices in engineering education. Elektron Elektrotech 102(6):159–162

    Google Scholar 

  10. Amirkhani S, Nahvi A (2016) Design and implementation of an interactive virtual control laboratory using haptic interface for undergraduate engineering students. Comput Appl Eng Educ 24(4):508–518

    Article  Google Scholar 

  11. Diolaiti N, Niemeyer G, Barbagli F, Salisbury JK (2006) Stability of haptic rendering: discretization, quantization, time delay, and coulomb effects. IEEE Trans Robot 22(2):256–268

    Article  Google Scholar 

  12. Lee K, Lee DY (2009) Adjusting output-limiter for stable haptic rendering in virtual environments. IEEE Trans Control Syst Technol 17(4):768–779

    Article  Google Scholar 

  13. Kim JP, Baek SY, Ryu J (2011) A force bounding approach for stable haptic interaction. In: World Haptics conference (WHC), 2011 IEEE, IEEE, pp 397–402

  14. Kim J-P, Ryu J (2010) Robustly stable haptic interaction control using an energy-bounding algorithm. Int J Robot Res 29(6):666–679

    Article  Google Scholar 

  15. Kim S, Kim J-P, Ryu J (2014) Adaptive energy-bounding approach for robustly stable interaction control of impedance-controlled industrial robot with uncertain environments. IEEE/ASME Trans Mechatron 19(4):1195–1205

    Article  Google Scholar 

  16. Ryu J-H, Yoon M-Y (2014) Memory-based passivation approach for stable haptic interaction. IEEE/ASME Trans Mechatron 19(4):1424–1435

    Article  Google Scholar 

  17. Jafari A, Ryu J-H (2015) 6-dof extension of memory-based passivation approach for stable haptic interaction. Intell Serv Robot 8(1):23–34

    Article  Google Scholar 

  18. Lu X, Song A (2008) Stable haptic rendering with detailed energy-compensating control. Comput Graph 32(5):561–567

    Article  MathSciNet  Google Scholar 

  19. Merlet JP (2006) Structural synthesis and architectures. Parallel Robots 1:19–94

    Google Scholar 

  20. Codourey A, Clavel R, Burckhardt C (1991) Control algorithm and controller for the direct drive delta robot. In: The IFAC Symposium on robot control, pp 169–177

  21. Miller K, Clavel R (1992) The lagrange-based model of delta-4 robot dynamics. Robotersysteme 8(1):49–54

    Google Scholar 

  22. Zhang C-D, Song S-M (1993) An efficient method for inverse dynamics of manipulators based on the virtual work principle. J Robot Syst 10(5):605–627

    Article  MATH  Google Scholar 

  23. Martin S, Hillier N (2009) Characterisation of the novint falcon haptic device for application as a robot manipulator. In: Australasian conference on robotics and automation (ACRA), Citeseer, pp 291–292

  24. Ellis RE, Sarkar N, Jenkins MA (1997) Numerical methods for the force reflection of contact. J Dyn Syst Meas Control 119(4):768–774

    Article  MATH  Google Scholar 

  25. Hannaford B, Ryu J-H (2002) Time-domain passivity control of haptic interfaces. IEEE Trans Robot Autom 18(1):1–10

    Article  Google Scholar 

  26. Part S (1985) Impedance control: an approach to manipulation. J Dyn Syst Meas Control 107:17

    Article  Google Scholar 

  27. Janabi-Sharifi F, Hayward V, Chen C-S (2000) Discrete-time adaptive windowing for velocity estimation. IEEE Trans Control Syst Technol 8(6):1003–1009

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Saeed Amirkhani.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amirkhani, S., Bahadorian, B., Nahvi, A. et al. Stable haptic rendering in interactive virtual control laboratory. Intel Serv Robotics 11, 289–300 (2018). https://doi.org/10.1007/s11370-018-0252-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-018-0252-2

Keywords

Navigation