International Journal of Social Robotics

, Volume 8, Issue 2, pp 223–235 | Cite as

Optimisation of Reference Gait Trajectory of a Lower Limb Exoskeleton

  • Rui Huang
  • Hong Cheng
  • Yi Chen
  • Qiming Chen
  • Xichuan Lin
  • Jing Qiu
Article

Abstract

Lower limb exoskeletons have gained considerable interest in recent years as a research topic for creating aids for people with walking disabilities and strength augmenters for pilot walkers. A crucial practical problem, however, is generating the reference trajectory of the joints. In this paper, we solve the reference trajectory problem by a novel approach which obtains the angle trajectories of knee joints from the hip joints. The relationship between the angle trajectories of the knee and hip joints is acquired through kinematic models of the lower limb exoskeleton. In these models, the parameters of the joint position trajectories are optimised by a swarm fish algorithm with variable population. The proposed approach is validated in virtual simulations and a physical prototype of an exoskeleton system. The experimental results confirm that the reference trajectory generation approach accurately reproduces human walking.

Keywords

Lower limb exoskeleton Kinematic model Trajectory generation Swarm fish algorithm 

References

  1. 1.
    Sankai Y (2011) HAL: hybrid assistive limb based on cybernics. In: International symposium on robotics reasearch, pp 25–34Google Scholar
  2. 2.
    Sakurai T, Sankai Y (2009) Development of motion instruction system with interactive robot suit HAL. In: IEEE international conference on robotics and biomimetics, pp 1141–1147Google Scholar
  3. 3.
    Bogue R (2009) Exoskeletons and robotic prosthetics: a review of recent developments. Ind Robot 36(5):421–427CrossRefGoogle Scholar
  4. 4.
    Strausser KA, Kazerooni H (2011) The development and testing of a human machine interface for a mobile medical exoskeleton. In: IEEE/RSJ international conference on robots and systems, pp 4911–4916Google Scholar
  5. 5.
    Dollar AM, Herr H (2008) Lower extremity exoskeletons and active orthoses: challenges and state-of-the-art. IEEE Trans Robot 24(1):144–158CrossRefGoogle Scholar
  6. 6.
    Kazerooni H, Steger R, Huang L (2006) Hybrid control of the Berkeley lower extremity exoskeleton (BLEEX). Int J Robot Res 25(5–6):561–573CrossRefGoogle Scholar
  7. 7.
    Walsh CJ, Pasch K, Herr H (2006) An antonomous, under-actuated exoskeleton for load-carrying augmentation. In: IEEE international conference on robotics and automation, pp 1410–1415Google Scholar
  8. 8.
    Kazerooni H, Chu A, Steger R (2007) That which does not stabilize, will only make us stronger. Int J Robot Res 26(1):75–89CrossRefMATHGoogle Scholar
  9. 9.
    Zoss A, Kazerooni H, Chu A (2005) On the mechanical design of the Berkeley lower extremity exoskeleton (BLEEX). In: IEEE/RSJ international conference on intelligent robots and systems pp 3465–3472Google Scholar
  10. 10.
    Li Z, Ge SS (2013) Adaptive robust controls of biped robots. IET Control Theory Appl 7(2):161–175MathSciNetCrossRefGoogle Scholar
  11. 11.
    Li Z, Su CY (2013) Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertanties. IEEE Trans Neural Netw Learn Syst 24(9):1400–1413CrossRefGoogle Scholar
  12. 12.
    Wang C, Li Y, Ge SS, Lee TH (2015) Optimal critic learning for Robot control in time-varying environments. IEEE Trans Neural Netw Learn Syst 26(10):2301–2310CrossRefGoogle Scholar
  13. 13.
    He W, Chen Y, Yin Z (2015) Adaptive neural network control of an uncertain robot with full-state constraints. In: IEEE transactions on cybernetics (in press). DOI:10.1109/TCYB.2015.2411285
  14. 14.
    He W, Ge SS, Li Y et al (2012) Impedance control of a rehabilitation robot for interactive training. In: Social robotics. Springer, Heidelberg, pp 526–535Google Scholar
  15. 15.
    He W, Ge SS, Li Y et al (2014) Neural network control of a rehabilitation robot by state and output feedback. J Intell Robot Syst, pp 1–17Google Scholar
  16. 16.
    Huang Q, Yokoi K, Kajita S, Kaneko K, Arai H, Koyachi N, Tanie K (2001) Planning walking patterns for a biped robot. IEEE Trans Robot Autom 17(3):280–289CrossRefGoogle Scholar
  17. 17.
    Vermenulen J, Verrelst B, Vanderborght B, Lefeber D, Guillaume P (2006) Trajectory planning for the walking biped ‘Lucy”. Int J Robot Res 25(9):867–887CrossRefGoogle Scholar
  18. 18.
    Udai A (2008) Optimum hip trajectory generation of a biped robot during single support phase using genetic algorithm. In: International conference on emerging trends in engineering and technology, pp 739–774Google Scholar
  19. 19.
    Yang C, Burdet E (2011) A model of reference trajectory adaptation for interaction with objects of arbitary shape and impedance. In: IEEE/RSJ international conference on robots and systems, pp. 4121–4126Google Scholar
  20. 20.
    Yang C, Ganesh G, Haddadin S et al (2011) Human-like adaptation of force and impedance in stable and unstable interactions. IEEE Trans Robot 27(5):918–930CrossRefGoogle Scholar
  21. 21.
    Silva F, Machado J (1997) Kinematic aspects of robotic biped locomotion systems. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems, pp. 266–272Google Scholar
  22. 22.
    Zijlstra W, Hof A (1997) Displacement of the pelvis during human walking: experimental data and model predictions. Gait Posture 6(3):249–262CrossRefGoogle Scholar
  23. 23.
    Yoon J, Kumar R, Özer A (2011) An adaptive foot device for increased gait and postural stability in lower limb Orthoses and exoskeletons. Int J Control Autom Syst 9(3):515–524CrossRefGoogle Scholar
  24. 24.
    Herman I (2008) Physics of the human body. Springer, New YorkGoogle Scholar
  25. 25.
    Chen Y, Zhang GF, Li YY, Ding Y, Zheng B, Miao Q (2013) Quantitative analysis of dynamic behaviours of rural areas at provincial level using public data of gross domestic product. Entropy 15(1):10–31Google Scholar
  26. 26.
    Chen Y, Miao Q, Zheng B, Wu SM, Pecht M (2013) Quantitative analysis of lithium-ion battery capacity prediction via adaptive bathtub-shaped function. Energies 6(6):3082–3096CrossRefGoogle Scholar
  27. 27.
    Chen Y (2011) SwarmFish-The artificial fish swarm algorithm. Available via online http://www.mathworks.com/matlabcentral/fileexchange/32022
  28. 28.
    Rahmfeld R (2003) Modeling of hydraulic mechanical systems cosimulation between ADAMS and Matlab/Simulink. Techinische Universitaet Hamburg-Harburg, HamburgGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Rui Huang
    • 1
  • Hong Cheng
    • 1
  • Yi Chen
    • 1
  • Qiming Chen
    • 1
  • Xichuan Lin
    • 1
  • Jing Qiu
    • 1
  1. 1.Center for Robotics, School of Automation EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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