Advertisement

Soft Particles for Granular Jamming

  • Fabrizio PutzuEmail author
  • Jelizaveta Konstantinova
  • Kaspar Althoefer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11650)

Abstract

In the last decade, soft robots demonstrated their distinctive advantages compared to ‘hard’ robots. Soft structures can achieve high dexterity and compliance. However, only low forces can be exerted, and more complicated control strategies are needed. Variable stiffness robots offer an alternative solution to compensate for the downsides of flexible robots. One of the most common approach in the development of variable stiffness robots is the use of granular jamming. In this paper a variable stiffness manipulator based on granular jamming is studied. Here, we propose the use of soft and deformable spherical particles instead of commonly used rigid particles. Further on, we evaluate the performance of the soft particles under vacuum. In addition, a comparison between our approach and the standard approach to granular jamming is presented. The proposed soft particles show good performance in terms of their capability of compacting and squeezing against each other to achieve a high-stiffness robot arm.

Keywords

Soft robotics Stiffness controllability Granular jamming Soft particles 

References

  1. 1.
    Culha, U., Nurzaman, S.G., Clemens, F., Iida, F.: SVAS3: strain vector aided sensorization of soft structures. Sensors 14(7), 12748–12770 (2014)CrossRefGoogle Scholar
  2. 2.
    Pfeifer, R., Gómez, G.: Morphological computation – connecting brain, body, and environment. In: Sendhoff, B., Körner, E., Sporns, O., Ritter, H., Doya, K. (eds.) Creating Brain-Like Intelligence. LNCS (LNAI), vol. 5436, pp. 66–83. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-00616-6_5CrossRefGoogle Scholar
  3. 3.
    Sareh, S., Rossiter, J., Conn, A., Drescher, K., Goldstein, R.E.: Swimming like algae: biomimetic soft artificial cilia. J. R. Soc. Interface rsif20120666 (2012)Google Scholar
  4. 4.
    Stilli, A., Wurdemann, H.A., Althoefer, K.: Shrinkable, stiffness-controllable soft manipulator based on a bio-inspired antagonistic actuation principle. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 2476– 2481. IEEE (2014)Google Scholar
  5. 5.
    Trivedi, D., Rahn, C.D., Kier, W.M., Walker, I.D.: Soft robotics: Biological inspiration, state of the art, and future research. Appl. Bionics Biomech. 5(3), 99–117 (2008)CrossRefGoogle Scholar
  6. 6.
    Laschi, C., Cianchetti, M., Mazzolai, B., Margheri, L., Follador, M., Dario, P.: Soft robot arm inspired by the octopus. Adv. Robot. 26(7), 709–727 (2012)CrossRefGoogle Scholar
  7. 7.
    Pfeifer, R., Lungarella, M., Iida, F.: The challenges ahead for bio-inspired ‘soft’ robotics. Commun. ACM 55(11), 76–87 (2012)CrossRefGoogle Scholar
  8. 8.
    Baisch, A.T., Sreetharan, P.S., Wood, R.J.: Biologically-inspired locomotion of a 2 g hexapod robot. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5360–5365. IEEE (2010)Google Scholar
  9. 9.
    Srivatsan, R.A., Travers, M., Choset, H.: Using lie algebra for shape estimation of medical snake robots. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 3483–3488. IEEE (2014)Google Scholar
  10. 10.
    Tully, S., Kantor, G., Zenati, M.A., Choset, H.: Shape estimation for image-guided surgery with a highly articulated snake robot. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1353–1358. IEEE (2011)Google Scholar
  11. 11.
    Walker, I.D.: Continuous backbone continuum robot manipulators. ISRN Robot. 2013 (2013)Google Scholar
  12. 12.
    Zhang, Z., Shang, J., Seneci, C.¸ Yang, G.-Z.: Snake robot shape sensing using micro-inertial sensors. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 831–836. IEEE (2013)Google Scholar
  13. 13.
    Manfredi, L., Putzu, F., Guler, S., Huan, Y., Cuschieri, A.: 4 DOFs hollow soft pneumatic actuator – HOSE. Mater. Res. Express (2018)Google Scholar
  14. 14.
    Stilli, A., Wurdemann, H., Althoefer, K.: A novel concept for safe, stiffness-controllable robot links. Soft Robot. 4(1), 16–22 (2017)CrossRefGoogle Scholar
  15. 15.
    Althoefer, K.: Antagonistic actuation and stiffness control in soft inflatable robots. Nat. Rev. Mater. 3, 76–77 (2018)CrossRefGoogle Scholar
  16. 16.
    Althoefer, K.: Neuro-fuzzy path planning for robotic manipulators. Ph.D. thesis, King’s College London (1996)Google Scholar
  17. 17.
    Degani, A., et al.: Highly articulated robotic probe for minimally invasive surgery. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vols. 1–8, pp. 3273–3276 (2008)Google Scholar
  18. 18.
    Simaan, N.: Snake-like units using flexible backbones and actuation redundancy for enhanced miniaturization. In: 2005 IEEE International Conference on Robotics and Automation (ICRA), vols. 1–4, pp. 3012–3017 (2005)Google Scholar
  19. 19.
    Camarillo, D.B., et al.: Mechanics modeling of tendon-driven continuum manipulators. IEEE Trans. Robot. 24, 1262–1273 (2008)CrossRefGoogle Scholar
  20. 20.
    Ning, K., Worgotter, F.: A novel concept for building a hyper-redundant chain robot. IEEE Trans. Robot. 25, 1237–1248 (2009)CrossRefGoogle Scholar
  21. 21.
    Cheng, N., et al.: Design and analysis of a soft mobile robot composed of multiple thermally activated joints driven by a single actuator. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 5207–5212 (2010)Google Scholar
  22. 22.
    Dupont, P.E., et al.: Design and control of concentric-tube robots. IEEE Trans. Robot. 26, 209–225 (2010)CrossRefGoogle Scholar
  23. 23.
    Jiang, A., et al.: Robotic granular jamming: does the membrane matter? Soft Robot. 1(3), 192–201 (2014)CrossRefGoogle Scholar
  24. 24.
    Amend, J., Brown, E., Rodenberg, N., Jaeger, H., Lipson, H.: A positive pressure universal gripper based on the jamming of granular material. IEEE Trans. Robot. 28(2), 341–350 (2012)CrossRefGoogle Scholar
  25. 25.
    Stanley, A.A., Gwilliam, J.C., Okamura, A.M.: Haprtic jamming: a deformable geometry, variable stiffness tactile display using pneumatics and particle jamming. In: World Haptics Conference (WHC), 14–17 April 2013Google Scholar
  26. 26.
    Li, M., et al.: Multi-fingered haptic palpation utilizing granular jamming stiffness feedback actuators. Smart Mater. Struct. 23(9), 095007 (2014)CrossRefGoogle Scholar
  27. 27.
    Cheng, N., et al.: Design and analysis of roust, low-cost, highly articulated manipulator enabled by jamming of granular media. In: International Conference on Robotics and Automation (ICRA), 14–18 May 2012Google Scholar
  28. 28.
    Zuriguel, I., Garcimartín, A., Maza, D., Pugnaloni, L.A., Pastor, J.M.: Jamming during the discharge of granular matter from a silo. Phys. Rev. E 71 (2005)Google Scholar
  29. 29.
    Roux, J.-N.: Geometric origin of mechanical properties of granular materials. Phys. Rev. E 61(6), 6802–6836 (2000)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Corwin, E., Jaeger, H., Nagel, S.: Structural signature of jamming in granular media. Nature 435(7045), 1075–1078 (2005)CrossRefGoogle Scholar
  31. 31.
    van Hecke, M.: Jamming of soft particles: geometry, mechanics, scaling and isostaticity. J. Phys. Condens. Matter 22(3) (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Fabrizio Putzu
    • 1
    Email author
  • Jelizaveta Konstantinova
    • 1
  • Kaspar Althoefer
    • 1
  1. 1.Centre for Advanced Robotics @ Queen MaryQueen Mary University of LondonLondonUK

Personalised recommendations