Skip to main content
Log in

Stress-based navigation for microscopic robots in viscous fluids

  • Research Paper
  • Published:
Journal of Micro-Bio Robotics Aims and scope Submit manuscript

Abstract

Objects moving in fluids experience patterns of stress on their surfaces determined by their motion and the geometry of nearby boundaries. Fish and underwater robots can use these patterns for navigation. This paper extends this stress-based navigation to microscopic robots in tiny vessels, where robots can exploit the physics of fluids at low Reynolds number. This applies, for instance, in vessels with sizes and flow speeds comparable to those of capillaries in biological tissues. We describe how a robot can use simple computations to estimate its motion, orientation and distance to nearby vessel walls from fluid-induced stresses on its surface. Numerically evaluating these estimates for a variety of vessel sizes and robot positions shows they are most accurate when robots are close to vessel walls.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Arlett J, Paul M, Solomon J, Cross M, Fraser S, Roukes M (2007) BioNEMS: Nanomechanical systems for single-molecule biophysics. In: Linke H, Mansson A (eds) Controlled Nanoscale Motion, Lecture Notes in Physics, vol 711. Springer, Berlin, pp 241–270. https://doi.org/10.1007/3-540-49522-3_12

  2. Augustin HG, Koh GY (2017) Organotypic vasculature: From descriptive heterogeneity to functional pathophysiology, vol 357. https://doi.org/10.1126/science.aal2379

    Article  Google Scholar 

  3. Bleckmann H, Zelick R (2009) Lateral line system of fish. Integr Zool 4:13–25. https://doi.org/10.1111/j.1749-4877.2008.00131.x

    Article  Google Scholar 

  4. Boehm F et al (2009) Potential strategies for advanced nanomedical device ingress and egress, natation, mobility, and navigation. In: Schulz MJ (ed) Nanomedicine design of particles, sensors, motors, implants, robots, and devices, chap. 15. Artech House, Boston, pp 393–421

  5. Bouffanais R, Weymouth GD, Yue DKP (2010) Hydrodynamic object recognition using pressure sensing. Proc Royal Soc A 467:19–38. https://doi.org/10.1098/rspa.2010.0095

    Article  MathSciNet  Google Scholar 

  6. Brooks RA (1991) New approaches to robotics. Science 253:1227–1232

    Article  Google Scholar 

  7. Castano A, Shen WM, Will P (2000) CONRO: Towards miniature self-sufficient metamorphic robots. Auton Robot 8:309– 324

    Article  Google Scholar 

  8. Cullinan MA et al (2012) Scaling electromechanical sensors down to the nanoscale. Sensors Actuators A 187:162– 173

    Article  Google Scholar 

  9. Douglas SM, Bachelet I, Church GM (2012) A logic-gated nanorobot for targeted transport of molecular payloads. Science 335:831–834. https://doi.org/10.1126/science.1214081

    Article  Google Scholar 

  10. Drexler KE (1992) Nanosystems: Molecular machinery, manufacturing, and computation. Wiley, NY

    Google Scholar 

  11. Dreyfus R et al (2005) Microscopic artificial swimmers. Nature 437:862–865. https://doi.org/10.1038/nature04090

    Article  Google Scholar 

  12. Dusenbery DB (2009) Living at micro scale: The unexpected physics of being small. Harvard University Press, Cambridge

    Google Scholar 

  13. Ekinci KL, Roukes ML (2005) Nanoelectromechanical systems. Rev Sci Instrum 76:061101

    Article  Google Scholar 

  14. Ferber D (2004) Microbes made to order. Science 303:158– 161

    Article  Google Scholar 

  15. Freitas Jr RA (1999) Nanomedicine, vol I: Basic Capabilities. Landes Bioscience, Georgetown. www.nanomedicine.com/NMI.htm

  16. Happel J, Brenner H (1983) Low Reynolds number hydrodynamics, 2nd edn. Kluwer, The Hague

    MATH  Google Scholar 

  17. Hogg T (2016) Energy dissipation by metamorphic micro-robots in viscous fluids. J Micro-Bio Robot 11:85–95. https://doi.org/10.1007/s12213-015-0086-3

    Article  Google Scholar 

  18. Jain RK, Martin JD, Stylianopoulos T (2014) The role of mechanical forces in tumor growth and therapy. Annu Rev Biomed Eng 16:321–346. https://doi.org/10.1146/annurev-bioeng-071813-105259

    Article  Google Scholar 

  19. Kim S, Karrila SJ (2005) Microhydrodynamics. Dover

  20. Koman VB, et al. (2018) Colloidal nanoelectronic state machines based on 2D materials for aerosolizable electronics. Nature Nanotechnology. https://doi.org/10.1038/s41565-018-0194-z

    Article  Google Scholar 

  21. Korin N et al (2012) Shear-activated nanotherapeutics for drug targeting to obstructed blood vessels. Science 337:738–742. https://doi.org/10.1126/science.1217815

    Article  Google Scholar 

  22. Lai SK, Wang YY, Hanes J (2009) Mucus-penetrating nanoparticles for drug and gene delivery to mucosal tissues. Adv Drug Deliv Rev 61:158–171. https://doi.org/10.1016/j.addr.2008.11.002

    Article  Google Scholar 

  23. Li J, de Avila BEF, Gao W, Zhang L, Wang J (2017) Micro/nanorobots for biomedicine: delivery, surgery, sensing, and detoxification. Sci Robot 2:eaam6431. https://doi.org/10.1126/scirobotics.aam6431

    Article  Google Scholar 

  24. Martel S (2007) The coming invasion of the medical nanorobots. Nanotechnol Perceptions 3:165–173

    MathSciNet  Google Scholar 

  25. Martel S et al (2007) Automatic navigation of an untethered device in the artery of a living animal using a conventional clinical magnetic resonance imaging system. Appl Phys Lett 90 :114105

    Article  Google Scholar 

  26. Medina-Sanchez M, Schmidt OG (2017) Medical microbots need better imaging and control. Nature 545:406–408. https://doi.org/10.1038/545406a

    Article  Google Scholar 

  27. Merkle RC, Freitas Jr RA, Hogg T, Moore TE, Moses MS, Ryley J (2018) Mechanical computing systems using only links and rotary joints. ASME Journal on Mechanisms and Robotics

  28. Mohlenkamp MJ (1999) A fast transform for spherical harmonics. J Fourier Anal Appl 5:159–184. https://doi.org/10.1007/BF01261607

    Article  MathSciNet  Google Scholar 

  29. Monroe D (2009) Micromedicine to the rescue. Commun ACM 52:13–15. https://doi.org/10.1145/1516046.1516051

    Article  Google Scholar 

  30. Nagy JA, Chang SH, Dvorak AM, Dvorak HF (2009) Why are tumour blood vessels abnormal and why is it important to know? Br J Cancer 100:865–869. https://doi.org/10.1038/sj.bjc.6604929

    Article  Google Scholar 

  31. Orszag SA (1974) Fourier series on spheres. Mon Weather Rev 102:56–75. https://doi.org/10.1175/1520-0493(1974)102<0056:FSOS>2.0.CO;2

    Article  Google Scholar 

  32. Park JH et al (2008) Magnetic iron oxide nanoworms for tumor targeting and imaging. Adv Mater 20:1630–1635

    Article  Google Scholar 

  33. Pawlik G, Rackl A, Bing RJ (1981) Quantitative capillary topography and blood flow in the cerebral cortex of cats: an in vivo microscopic study. Brain Res 208:35–58. https://doi.org/10.1016/0006-8993(81)90619-3

    Article  Google Scholar 

  34. Purcell EM (1977) Life at low Reynolds number. Am J Phys 45:3–11

    Article  Google Scholar 

  35. Rahmer J, Stehning C, Gleich B (2017) Spatially selective remote magnetic actuation of identical helical micromachines. Sci Robot 2:eaal2845. https://doi.org/10.1126/scirobotics.aal2845

    Article  Google Scholar 

  36. Schulz MJ, Shanov VN, Yun Y (eds) (2009) Nanomedicine Design of Particles, Sensors, Motors, Implants, Robots, and Devices. Engineering in Medicine and Biology. Artech House, Boston

  37. Sershen S, Westcott S, Halas NJ, West J (2000) Temperature-sensitive polymer-nanoshell composite for photothermally modulated drug delivery. J Biomed Mater Res 51:293–298

    Article  Google Scholar 

  38. Sichert AB, Bamler R, van Hammen JL (2009) Hydrodynamic object recognition: When multipoles count. Phys Rev Lett 102:058104. https://doi.org/10.1103/PhysRevLett.102.058104

    Article  Google Scholar 

  39. Trouilloud R et al (2008) Soft swimming: Exploiting deformable interfaces for low Reynolds number locomotion. Phys Rev Lett 101:048102. https://doi.org/10.1103/PhysRevLett.101.048102

    Article  Google Scholar 

  40. Vollmayr AN et al (2014) Snookie: An autonomous underwater vehicle with artificial lateral-line system. In: Bleckmann H et al (eds) Flow Sensing in Air and Water, chap. 20. Springer, pp 521–562. https://doi.org/10.1007/978-3-642-41446-6_20

    Chapter  Google Scholar 

  41. Yang Y et al (2006) Distant touch hydrodynamic imaging with an artificial lateral line. Proc Natl Acad Sci USA 103:18891–18895. https://doi.org/10.1073/pnas.0609274103

    Article  Google Scholar 

  42. Yesin KB, Exner P, Vollmers K, Nelson BJ (2005) Design and control of in-vivo magnetic microrobots. In: Duncan J, Gerig G (eds) Proceedings of 8th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2005). Springer, Berlin, pp 819–826. https://doi.org/10.1007/11566465_101

    Google Scholar 

Download references

Acknowledgements

I thank Robert A. Freitas Jr., Ralph C. Merkle, Matthew S. Moses, Daniel Reda and James Ryley for helpful discussions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tad Hogg.

Additional information

Publisher’s Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

(PDF 1.49 MB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hogg, T. Stress-based navigation for microscopic robots in viscous fluids. J Micro-Bio Robot 14, 59–67 (2018). https://doi.org/10.1007/s12213-018-0109-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12213-018-0109-y

Keywords

Navigation