Skip to main content
Springer Nature Link
Log in

Development of an embedded sensor system for pneumatic artificial muscle proprioceptors

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

Spinal reflexes greatly contribute to the control of fast physical interactions (e.g., catching a moving ball) without the influence of higher level control systems involved in human motor control. Therefore, to realize the interactions in robots, it is a useful approach to mimic the nervous system controlling spinal reflexes. To this end, as the starting point for creating spinal reflexes, sensors that measure and encode body movements similar to human proprioceptors are needed to generate signals for the spinal reflexes. In this study, we developed artificial muscle proprioceptors to reproduce spinal reflexes in robots. In particular, we focused on pneumatic artificial muscles and designed an artificial muscle spindle and an artificial Golgi tendon organ, which were integrated with a pneumatic artificial muscle. A compact local measuring system consisting of a microcomputer and amplifiers was developed to easily install and organize the sensors.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Pfeifer R, Bongard J (2006) How the body shapes the way we think: a new view of intelligence. MIT press, Cambridge

  2. Ikemoto S, Kimoto Y, Hosoda K (2015) Shoulder complex linkage mechanism for human-like musculoskeletal robot arms. Bioinspiration Biomim 10(6):066009 (IOP Publishing)

  3. Marques HG, Antsch M, Wittmeier S, Holland O, Alessandro C, Diamond A, Lungarella M, Knight R (2010) ECCE1: the first of a series of anthropomimetic musculoskeletal upper torsos. In: Proceedings of the IEEE-RAS international conference on humanoid robots

  4. Shin H, Ikemoto S, Hosoda K (2015) Understanding function of gluteus medius in human walking from constructivist approach. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems

  5. Asano Y, Mizoguchi H, Kozuki T, Motegi Y, Osada M, Urata J, Nakanishi Y, Okada K, Inaba M (2012) Lower thigh design of detailed musculoskeletal humanoid Kenshiro. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems

  6. Nakanishi Y, Ohta S, Shirai T, Asano Y, Kozuki T, Kakehashi Y, Mizoguchi H, Kurotobi T, Motegi Y, Sasabuchi K, et al (2013) Design approach of biologically-inspired musculoskeletal humanoids. Int J Adv Robot Syst 10

  7. Purves D, Augustine GJ, Fitzpatrick D, Katz LC, Lamantia AS, McNamara JO, Williams SM (2001) Neuroscience, 2nd edn. Sinauer Associates, Sunderland

    Google Scholar 

  8. Kandel ER, Schwartz JH, Jessell TM et al (2000) Principles of neural science, 4th edn. McGraw-hill, New York

    Google Scholar 

  9. Bekey, G A, Tomovic R (1986) Robot control by reflex actions: In: Proceedings of the IEEE international conference on robotics and automation

  10. Folgheraiter M, Gini G (2000) Blackfingers an artificial hand that copies human hand in structure, size, and function: In: Proceedings of the IEEE international conference on humanoids

  11. Folgheraiter M, Gini G (2004) Human-like reflex control for an artificial hand. BioSystems 76(1):65–74 (Elsevier)

  12. Rosendo A, Liu X, Shimizu M, Hosoda K (2015) Stretch reflex improves rolling stability during hopping of a decerebrate biped system. Bioinspiration Biomim 10(1):016008 (IOP Publishing)

  13. Ikemoto S(2015) Mutual inhibition and co-contraction of a musculoskeletal robot arm using artificial muscle spindles. Workshop at the computational neuroscience meeting CNS

  14. Hunt CC, Ottoson D (1975) Impulse activity and receptor potential of primary and secondary endings of isolated mammalian muscle spindles. J Physiol 252(1):259–281

    Article  Google Scholar 

  15. Houk J, Henneman E (1967) Responses of Golgi tendon organs to active contractions of the soleus muscle of the cat. J Neurophysiol 30(3):466–481

    Google Scholar 

  16. Mileusnic MP, Loeb GE (2006) Mathematical models of proprioceptors. II. Structure and function of the Golgi tendon organ. J Neurophysiol 96(4):1789–1802

    Article  Google Scholar 

  17. Smith AM (1981) The coactivation of antagonist muscles. Can J Physiol Pharmacol 59(7):733–747

    Article  Google Scholar 

  18. Frysinger RC, Bourbonnais D, Kalaska JF, Smith AM (1984) Cerebellar cortical activity during antagonist cocontraction and reciprocal inhibition of forearm muscles. J Neurophysiol 51(1):32–49

    Google Scholar 

  19. Wakimoto S, Suzumori K, Kanda T (2005) Development of intelligent McKibben actuator. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems

  20. Kuriyama S, Ding M, Kurita Y, Ogasawara T, Ueda J (2009) Flexible sensor for Mckibben pneumatic actuator. In: Proceedings of the IEEE sensors 10

  21. Aliff M, Dohta S, Akagi T, Li H (2012) Development of a simple-structured pneumatic robot arm and its control using low-cost embedded controller. J Procedia Eng 41:134–142

    Article  Google Scholar 

  22. Nakamoto H, Oida S, Ootaka H, Hirata I, Tada M, Kobayashi F, Kojima F (2015) Design and response performance of capacitance meter for stretchable strain sensor. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems

  23. Crowe A (1968) A mechanical model of the mammalian muscle spindle. J Theor Biol 21(1):21–41

    Article  Google Scholar 

  24. Rudjord T (1970) A second order mechanical model of muscle spindle primary endings. Kybernetik 6(6):205–213

    Article  Google Scholar 

  25. Shadmehr R, Wise SP (2005) The computational neurobiology of reaching and pointing: a foundation for motor learning. MIT press, Cambridge

Download references

Author information

Authors and Affiliations

  1. Dept. of Systems Innovation, Graduate School of Engineering Science, Osaka University, Toyonaka, Japan

    Hirofumi Shin, Hajime Saitoh, Takahiko Kawakami, Satoshi Yamanishi, Shuhei Ikemoto & Koh Hosoda

Authors
  1. Hirofumi Shin
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Hajime Saitoh
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Takahiko Kawakami
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Satoshi Yamanishi
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Shuhei Ikemoto
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Koh Hosoda
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Hirofumi Shin.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shin, H., Saitoh, H., Kawakami, T. et al. Development of an embedded sensor system for pneumatic artificial muscle proprioceptors. Artif Life Robotics 21, 486–492 (2016). https://doi.org/10.1007/s10015-016-0290-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-016-0290-9

Keywords