Autonomous Agents and Multi-Agent Systems

, Volume 14, Issue 3, pp 271–305 | Cite as

Coordinating microscopic robots in viscous fluids

  • Tad Hogg


Multiagent control provides strategies for aggregating microscopic robots (“nanorobots”) in fluid environments relevant for medical applications. Unlike larger robots, viscous forces and Brownian motion dominate the behavior. Examples range from modified microorganisms (programmable bacteria) to future robots using ongoing developments in molecular computation, sensors and motors. We evaluate controls for locating a cell-sized area emitting a chemical into a moving fluid with parameters corresponding to chemicals released in response to injury or infection in small blood vessels. These control methods are passive Brownian motion, following the chemical concentration gradient, and cooperative behaviors in which some robots use acoustic signals to guide others to the chemical source. Control performance is evaluated using diffusion equations to describe the robot motions and control state transitions. The quantitative results show these control techniques are feasible approaches to the task with trade-offs among fabrication difficulty, response speed, false positive detection rate and energy use. Controlled aggregation at chemically distinctive locations could be useful for sensitive diagnosis, selective changes to biological tissues and forming structures using previous proposals for multiagent control of modular robots.


Multiagent robot control design Nanomedicine Nanotechnology 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adler J.P. (1966). Chemotaxis in bacteria. Science 153, 708–716CrossRefGoogle Scholar
  2. 2.
    Allen T.M., Cullis P.R. (2004) Drug delivery systems: Entering the mainstream. Science 303, 1818–1822CrossRefGoogle Scholar
  3. 3.
    Arbuckle, D., & Requicha, A. A. G. (2004) Active self-assembly. In Proceedings of the IEEE conference on robotics and automation, pp. 896–901.Google Scholar
  4. 4.
    Benenson Y., Gil B., Ben-Dor U., Adar R., Shapiro E. (2004). An autonomous molecular computer for logical control of gene expression. Nature 429, 423–429CrossRefGoogle Scholar
  5. 5.
    Berg, H. C. (1993). Random walks in biology(2nd ed.). Princeton University Press.Google Scholar
  6. 6.
    Berg H.C., Purcell E.M. (1977). Physics of chemoreception. Biophysical Journal 20, 193–219CrossRefGoogle Scholar
  7. 7.
    Bojinov, H., Casal, A., & Hogg, T. (2002). Multiagent control of modular self-reconfigurable robots. Artificial Intelligence, 142, 99–120. Available as preprint cs.RO/0006030.Google Scholar
  8. 8.
    Bonabeau E., Dorigo M., Theraulaz G. (1999). Swarm intelligence: From natural to artificial systems. Oxford, Oxford University PresszbMATHGoogle Scholar
  9. 9.
    Boryczko K., Dzwinel W., Yuen D.A. (2003). Dynamical clustering of red blood cells in capillary vessels. Journal of Molecular Modeling 9, 16–33Google Scholar
  10. 10.
    Brooks R.A. (1992). Artificial life and real robots. In: Varela F.J., Bourgine P (eds) Proceedings of the first European conference on artificial life. Cambridge, MA, MIT Press, pp. 3–10Google Scholar
  11. 11.
    Casal, A., Hogg, T., & Cavalcanti, A. (2003). Nanorobots as cellular assistants in inflammatory responses. In J. Shapiro, (Ed.), Proceedings of the 2003 Stanford biomedical computation symposium (BCATS2003), p. 62, Oct. 2003. Available at Scholar
  12. 12.
    Cassandra, A. R., Kaelbling, L. P., & Littman, M. L. (1994). Acting optimally in partially observable stochastic domains. In Proceedings of the 12th National Conference on artificial intelligence (AAAI94) pp. 1023–1028, Menlo Park, CA, 1994. AAAI Press.Google Scholar
  13. 13.
    Cavalcanti A. (2003). Assembly automation with evolutionary nanorobots and sensor-based control applied to nanomedicine. IEEE Transactions on Nanotechnology 2, 82–87CrossRefGoogle Scholar
  14. 14.
    Cavalcanti A., Freitas R.A. Jr. (2002). Autonomous multi-robot sensor-based cooperation for nanomedicine. International Journal of Nonlinear Sciences and Numerical Simulation 3, 743–746Google Scholar
  15. 15.
    Clearwater S.H. (Ed.). (1996). Market-based control: A paradigm for distributed resource allocation. World Scientific, SingaporeGoogle Scholar
  16. 16.
    Collier C.P. et al. (1999). Electronically configurable molecular-based logic gates. Science 285, 391–394CrossRefGoogle Scholar
  17. 17.
    Craighead H.G. (2001). Nanoelectromechanical systems. Science 290, 1532–1535CrossRefGoogle Scholar
  18. 18.
    Dhariwal, A., Sukhatme, G. S., & Requicha, A. A. G. (2004). Bacterium-inspired robots for environmental monitoring. In Proceedings of the IEEE international conference on robotics and automation.Google Scholar
  19. 19.
    Dorigo, M. (2005). Swarm-bot: An experiment in swarm robotics. In P. Arabshahi & A. Martinoli (Eds.), Proceedings of the IEEE swarm intelligence symposium (SIS2005), pp. 192–200.Google Scholar
  20. 20.
    Eric Drexler K. (1992). Nanosystems: Molecular machinery, manufacturing, and computation. NY, John WileyGoogle Scholar
  21. 21.
    Dusenbery, D. B. (1997). Minimum size limit for useful locomotion by free-swimming microbes. Proceedings of Natural Academic Science USA, 94, 10949–10954.Google Scholar
  22. 22.
    David B. Dusenbery. Spatial sensing of stimulus gradients can be superior to temporal sensing for free-swimming bacteria. Biophysical Journal, 74:2272–2277, 1998.Google Scholar
  23. 23.
    Freitas, R. A. Jr. (1999). Nanomedicine, Volume I. Georgetown, TX: Landes Bioscience. Available at Scholar
  24. 24.
    Freitas R.A. Jr. (2003). Nanomedicine, Volume IIA: Biocompatibility. Georgetown TX, Landes BioscienceGoogle Scholar
  25. 25.
    Fritz J. et al. (2000). Translating biomolecular recognition into nanomechanics. Science 288, 316–318CrossRefGoogle Scholar
  26. 26.
    Fung Y.C. (1997). Biomechanics: Circulation (2nd ed). NY, SpringerGoogle Scholar
  27. 27.
    Galstyan, A., Hogg, T., & Lerman, K. (2005). Modeling and mathematical analysis of swarms of microscopic robots. In P. Arabshahi & A. Martinoli (Eds.), Proceedings of the IEEE swarm intelligence symposium (SIS2005), pp. 201–208.Google Scholar
  28. 28.
    Gazi V., Passino K.M. (2004). Stability analysis of social foraging swarms. IEEE Transactions on Systems, Man and Cybernetics B34, 539–557Google Scholar
  29. 29.
    Ghosh S. et al. (2003). Carbon nanotube flow sensors. Science 299, 1042–1044CrossRefGoogle Scholar
  30. 30.
    Goldman, C. V., & Zilberstein, S. (2003). Optimizing information exchange in cooperative multi-agent systems. In Proceedings of the 2nd international conference on autonomous agents and multiagent systems, pp. 137–144.Google Scholar
  31. 31.
    Hogg T., Huberman B.A. (2004). Dynamics of large autonomous computational systems. In: Tumer K., Wolpert D. (eds) Collectives and the design of complex systems. New York, Springer, pp. 295–315Google Scholar
  32. 32.
    Hogg, T., & Sretavan, D. W. (2005). Controlling tiny multi-scale robots for nerve repair. In Proceedings of the 20th national conference on artificial intelligence (AAAI2005), pp. 1286–1291. AAAI Press.Google Scholar
  33. 33.
    Hogg, T., & Zhang, K. (2004). Secure multi-agent communication for microscopic robots. In C. Ortiz (Ed.), Proceedings of the AAAI spring symposium on bridging the multi-agent and multi-robotic research gap, pp. 22–26, March 2004.Google Scholar
  34. 34.
    Howard J. (1997). Molecular motors: Structural adaptations to cellular functions. Nature 389, 561–567CrossRefGoogle Scholar
  35. 35.
    Jakobi, N., Husbands, P., & Harvey, I. (1995). Noise and the reality gap: The use of simulation in evolutionary robotics. In F. Moran et al. (Eds.), Advances in artificial Life: Proceedings of the 3rd European conference on artificial life (pp. 704–720). Springer-Verlag.Google Scholar
  36. 36.
    Janeway, C. A. et al. (2001). Immunobiology: The immune system in health and disease (5th ed.). Garland.Google Scholar
  37. 37.
    Karniadakis G.E.M., Beskok A. (2002). Micro flows: Fundamentals and simulation. Berlin, SpringerzbMATHGoogle Scholar
  38. 38.
    Keller, K. H. (1971). Effect of fluid shear on mass transport in flowing blood. In Proceedings of federation of american societies for experimental biology, pp. 1591–1599, Sept.–Oct. 1971.Google Scholar
  39. 39.
    Keszler, B. L., Majoros, I. J., & Baker, J. R. Jr. (2001). Molecular engineering in nanotechnology: Structure and composition of multifunctional devices for medical application. In Proceedings of the ninth foresight conference on molecular nanotechnology.Google Scholar
  40. 40.
    Lerman K. et al. (2001). A macroscopic analytical model of collaboration in distributed robotic systems. Artificial Life 7, 375–393CrossRefGoogle Scholar
  41. 41.
    Mataric, M. (1992). Minimizing complexity in controlling a mobile robot population. In Proceedings of the 1992 IEEE international conference on robotics and automation, pp. 830–835.Google Scholar
  42. 42.
    William McCurdy, C. et al. (2002). Theory and modeling in nanoscience. Workshop report,, US Dept. of Energy.Google Scholar
  43. 43.
    Miller M.B., Bassler B.L. (2001). Quorum sensing in bacteria. Annual Review of Microbiology 55, 165–199CrossRefGoogle Scholar
  44. 44.
    Montemagno C., Bachand G. (1999). Constructing nanomechanical devices powered by biomolecular motors. Nanotechnology 10, 225–231CrossRefGoogle Scholar
  45. 45.
    Morris, K. (2001). Macrodoctor, come meet the nanodoctors. The Lancet, 357, 778, March 10, 2001.Google Scholar
  46. 46.
    NIH. (2003). National Institutes of Health roadmap: Nanomedicine. Available at Scholar
  47. 47.
    Patolsky F., Lieber C.M. (2005). Nanowire nanosensors. Materials Today 8, 20–28CrossRefGoogle Scholar
  48. 48.
    Purcell E.M. (1977). Life at low Reynolds number. American Journal of Physics 45, 3–11CrossRefGoogle Scholar
  49. 49.
    Pynadath, D. V., & Tambe, M. (2002). Multiagent teamwork: Analyzing the optimality and complexity of key theories and models. In Proceedings of the international joint conference on autonomous agents and multiagent systems, pp. 873–880.Google Scholar
  50. 50.
    Requicha A.A.G. (2003). Nanorobots, NEMS and nanoassembly. Proceedings of the IEEE 91, 1922–1933CrossRefGoogle Scholar
  51. 51.
    Riedel I.H. et al. (2005). A self-organized vortex array of hydrodynamically entrained sperm cells. Science 309, 300–303CrossRefGoogle Scholar
  52. 52.
    Salemi, B., Shen, W.-M., & Will, P. (2001). Hormone controlled metamorphic robots. In Proceedings of the international conference on robotics and automation (ICRA2001).Google Scholar
  53. 53.
    Shannon C.E., Weaver W. (1963). The mathematical theory of communication. Chicago, Univ. of Illinois PresszbMATHGoogle Scholar
  54. 54.
    Sheehan P.E., Whitman L.J. (2005). Detection limits for nanoscale biosensors. Nano Letters 5(4): 803–807CrossRefGoogle Scholar
  55. 55.
    Soong et al. R.K. (2000). Powering an inorganic nanodevice with a biomolecular motor. Science 290, 1555–1558CrossRefGoogle Scholar
  56. 56.
    Sretavan D., Chang W., Keller C., Kliot M. (2005) Microscale surgery on axons for nerve injury treatment. Neurosurgery 57(4): 635–646CrossRefGoogle Scholar
  57. 57.
    Vogel, S. (1994). Life in moving fluids (2nd ed.). Princeton Univ. Press.Google Scholar
  58. 58.
    Wang, S.-Y., & Stanley Williams, R. (Eds.) (2005). Nanoelectronics, Volume 80. Springer, March 2005. Special issue of Applied Physics A Google Scholar
  59. 59.
    Weiss, R., Homsy, G. E., & Knight, T. F. Jr. (1999). Toward in vivo digital circuits. In Proceedings of DIMACS workshop on evolution as computation.Google Scholar
  60. 60.
    Weiss, R., & Knight, T. F. Jr. (2000). Engineered communications for microbial robotics. In Proceedings of sixth international meeting on DNA based computers (DNA6).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  1. 1.HP LabsPalo AltoUSA

Personalised recommendations