Advertisement

A Scalable Variable Stiffness Revolute Joint Based on Layer Jamming for Robotic Exoskeletons

Conference paper
  • 405 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12228)

Abstract

Robotic exoskeletons have been a focal point of research due to an ever-increasing ageing population, longer life expectancy, and a desire to further improve the existing capabilities of humans. However, their effectiveness is often limited, with strong rigid structures poorly interfacing with humans and soft flexible mechanisms providing limited forces. In this paper, a scalable variable stiffness revolute joint is proposed to overcome this problem. By using layer jamming, the joint has the ability to stiffen or soften for different use cases. A theoretical and experimental study of maximum stiffness with size was conducted to determine the suitability and scalablity of this technology. Three sizes (50 mm, 37.5 mm, 25 mm diameter) of the joint were developed and evaluated. Results indicate that this technology is most suitable for use in human fingers, as the prototypes demonstrate a sufficient torque (0.054 Nm) to support finger movement.

Keywords

Variable stiffness Layer jamming Exoskeleton 

References

  1. 1.
    Bolton, C.F., Carter, K.M.: Human sensory nerve compound action potential amplitude: variation with sex and finger circumference. J. Neurol. Neurosurg. Psychiatry 43(10), 925–928 (1980)CrossRefGoogle Scholar
  2. 2.
    Bundhoo, V., Park, E.J.: Design of an artificial muscle actuated finger towards biomimetic prosthetic hands. In: Proceedings of 12th International Conference on Advanced Robotics, ICAR 2005, pp. 368–375. IEEE (2005)Google Scholar
  3. 3.
    Butz, K.D., Merrell, G., Nauman, E.A.: A biomechanical analysis of finger joint forces and stresses developed during common daily activities. Comput. Methods Biomech. Biomed. Eng. 15(2), 131–140 (2012)CrossRefGoogle Scholar
  4. 4.
    Carignan, C., Tang, J., Roderick, S.: Development of an exoskeleton haptic interface for virtual task training. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3697–3702. IEEE (2009)Google Scholar
  5. 5.
    Clark, A.B., Rojas, N.: Assessing the performance of variable stiffness continuum structures of large diameter. IEEE Robot. Autom. Lett. 4(3), 2455–2462 (2019)CrossRefGoogle Scholar
  6. 6.
    Clark, A.B., Rojas, N.: Stiffness-tuneable limb segment with flexible spine for malleable robots. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 3969–3975. IEEE (2019)Google Scholar
  7. 7.
    Frey, M., Colombo, G., Vaglio, M., Bucher, R., Jorg, M., Riener, R.: A novel mechatronic body weight support system. IEEE Trans. Neural Syst. Rehabil. Eng. 14(3), 311–321 (2006)CrossRefGoogle Scholar
  8. 8.
    Gopura, R., Kiguchi, K., Bandara, D.: A brief review on upper extremity robotic exoskeleton systems. In: 2011 6th International Conference on Industrial and Information Systems, pp. 346–351. IEEE (2011)Google Scholar
  9. 9.
    In, H., Kang, B.B., Sin, M., Cho, K.J.: Exo-Glove: a wearable robot for the hand with a soft tendon routing system. IEEE Robot. Autom. Mag. 22(1), 97–105 (2015)CrossRefGoogle Scholar
  10. 10.
    Jiang, A., Xynogalas, G., Dasgupta, P., Althoefer, K., Nanayakkara, T.: Design of a variable stiffness flexible manipulator with composite granular jamming and membrane coupling. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2922–2927. IEEE (2012)Google Scholar
  11. 11.
    Kim, Y.J., Cheng, S., Kim, S., Iagnemma, K.: Design of a tubular snake-like manipulator with stiffening capability by layer jamming. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4251–4256. IEEE (2012)Google Scholar
  12. 12.
    Langer, M., Amanov, E., Burgner-Kahrs, J.: Stiffening sheaths for continuum robots. Soft Robot. 5(3), 291–303 (2018)CrossRefGoogle Scholar
  13. 13.
    Lo, H.S., Xie, S.Q.: Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects. Medical Eng. Phys. 34(3), 261–268 (2012)CrossRefGoogle Scholar
  14. 14.
    Morse, J.L., Jung, M.C., Bashford, G.R., Hallbeck, M.S.: Maximal dynamic grip force and wrist torque: the effects of gender, exertion direction, angular velocity, and wrist angle. Appl. Ergon. 37(6), 737–742 (2006)CrossRefGoogle Scholar
  15. 15.
    Park, Y.L., Santos, J., Galloway, K.G., Goldfield, E.C., Wood, R.J.: A soft wearable robotic device for active knee motions using flat pneumatic artificial muscles. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 4805–4810. IEEE (2014)Google Scholar
  16. 16.
    Stienen, A.H., Hekman, E.E., Van Der Helm, F.C., Van Der Kooij, H.: Self-aligning exoskeleton axes through decoupling of joint rotations and translations. IEEE Trans. Robot. 25(3), 628–633 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.REDS Lab, Dyson School of Design Engineering, Imperial College LondonLondonUK

Personalised recommendations