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Coordinating microscopic robots in viscous fluids

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Abstract

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.

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References

  1. Adler J.P. (1966). Chemotaxis in bacteria. Science 153, 708–716

    Article  Google Scholar 

  2. Allen T.M., Cullis P.R. (2004) Drug delivery systems: Entering the mainstream. Science 303, 1818–1822

    Article  Google Scholar 

  3. Arbuckle, D., & Requicha, A. A. G. (2004) Active self-assembly. In Proceedings of the IEEE conference on robotics and automation, pp. 896–901.

  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, 423–429

    Article  Google Scholar 

  5. Berg, H. C. (1993). Random walks in biology(2nd ed.). Princeton University Press.

  6. Berg H.C., Purcell E.M. (1977). Physics of chemoreception. Biophysical Journal 20, 193–219

    Article  Google Scholar 

  7. Bojinov, H., Casal, A., & Hogg, T. (2002). Multiagent control of modular self-reconfigurable robots. Artificial Intelligence, 142, 99–120. Available as arxiv.org preprint cs.RO/0006030.

    Google Scholar 

  8. Bonabeau E., Dorigo M., Theraulaz G. (1999). Swarm intelligence: From natural to artificial systems. Oxford, Oxford University Press

    MATH  Google Scholar 

  9. Boryczko K., Dzwinel W., Yuen D.A. (2003). Dynamical clustering of red blood cells in capillary vessels. Journal of Molecular Modeling 9, 16–33

    Google Scholar 

  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. 3–10

    Google Scholar 

  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 http://bcats.stanford.edu.

  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.

  13. Cavalcanti A. (2003). Assembly automation with evolutionary nanorobots and sensor-based control applied to nanomedicine. IEEE Transactions on Nanotechnology 2, 82–87

    Article  Google Scholar 

  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, 743–746

    Google Scholar 

  15. Clearwater S.H. (Ed.). (1996). Market-based control: A paradigm for distributed resource allocation. World Scientific, Singapore

    Google Scholar 

  16. Collier C.P. et al. (1999). Electronically configurable molecular-based logic gates. Science 285, 391–394

    Article  Google Scholar 

  17. Craighead H.G. (2001). Nanoelectromechanical systems. Science 290, 1532–1535

    Article  Google Scholar 

  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.

  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.

  20. Eric Drexler K. (1992). Nanosystems: Molecular machinery, manufacturing, and computation. NY, John Wiley

    Google Scholar 

  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. David B. Dusenbery. Spatial sensing of stimulus gradients can be superior to temporal sensing for free-swimming bacteria. Biophysical Journal, 74:2272–2277, 1998.

  23. Freitas, R. A. Jr. (1999). Nanomedicine, Volume I. Georgetown, TX: Landes Bioscience. Available at www.nanomedicine.com.

  24. Freitas R.A. Jr. (2003). Nanomedicine, Volume IIA: Biocompatibility. Georgetown TX, Landes Bioscience

    Google Scholar 

  25. Fritz J. et al. (2000). Translating biomolecular recognition into nanomechanics. Science 288, 316–318

    Article  Google Scholar 

  26. Fung Y.C. (1997). Biomechanics: Circulation (2nd ed). NY, Springer

    Google Scholar 

  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.

  28. Gazi V., Passino K.M. (2004). Stability analysis of social foraging swarms. IEEE Transactions on Systems, Man and Cybernetics B34, 539–557

    Google Scholar 

  29. Ghosh S. et al. (2003). Carbon nanotube flow sensors. Science 299, 1042–1044

    Article  Google Scholar 

  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.

  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. 295–315

    Google Scholar 

  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.

  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.

  34. Howard J. (1997). Molecular motors: Structural adaptations to cellular functions. Nature 389, 561–567

    Article  Google Scholar 

  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.

  36. Janeway, C. A. et al. (2001). Immunobiology: The immune system in health and disease (5th ed.). Garland.

  37. Karniadakis G.E.M., Beskok A. (2002). Micro flows: Fundamentals and simulation. Berlin, Springer

    MATH  Google Scholar 

  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.

  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.

  40. Lerman K. et al. (2001). A macroscopic analytical model of collaboration in distributed robotic systems. Artificial Life 7, 375–393

    Article  Google Scholar 

  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.

  42. William McCurdy, C. et al. (2002). Theory and modeling in nanoscience. Workshop report,www.science.doe.gov/bes/reports/files/tmn_rpt.pdf, US Dept. of Energy.

  43. Miller M.B., Bassler B.L. (2001). Quorum sensing in bacteria. Annual Review of Microbiology 55, 165–199

    Article  Google Scholar 

  44. Montemagno C., Bachand G. (1999). Constructing nanomechanical devices powered by biomolecular motors. Nanotechnology 10, 225–231

    Article  Google Scholar 

  45. Morris, K. (2001). Macrodoctor, come meet the nanodoctors. The Lancet, 357, 778, March 10, 2001.

  46. NIH. (2003). National Institutes of Health roadmap: Nanomedicine. Available at http://nihroadmap.nih.gov/nanomedicine/index.asp.

  47. Patolsky F., Lieber C.M. (2005). Nanowire nanosensors. Materials Today 8, 20–28

    Article  Google Scholar 

  48. Purcell E.M. (1977). Life at low Reynolds number. American Journal of Physics 45, 3–11

    Article  Google Scholar 

  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.

  50. Requicha A.A.G. (2003). Nanorobots, NEMS and nanoassembly. Proceedings of the IEEE 91, 1922–1933

    Article  Google Scholar 

  51. Riedel I.H. et al. (2005). A self-organized vortex array of hydrodynamically entrained sperm cells. Science 309, 300–303

    Article  Google Scholar 

  52. Salemi, B., Shen, W.-M., & Will, P. (2001). Hormone controlled metamorphic robots. In Proceedings of the international conference on robotics and automation (ICRA2001).

  53. Shannon C.E., Weaver W. (1963). The mathematical theory of communication. Chicago, Univ. of Illinois Press

    MATH  Google Scholar 

  54. Sheehan P.E., Whitman L.J. (2005). Detection limits for nanoscale biosensors. Nano Letters 5(4): 803–807

    Article  Google Scholar 

  55. Soong et al. R.K. (2000). Powering an inorganic nanodevice with a biomolecular motor. Science 290, 1555–1558

    Article  Google Scholar 

  56. Sretavan D., Chang W., Keller C., Kliot M. (2005) Microscale surgery on axons for nerve injury treatment. Neurosurgery 57(4): 635–646

    Article  Google Scholar 

  57. Vogel, S. (1994). Life in moving fluids (2nd ed.). Princeton Univ. Press.

  58. Wang, S.-Y., & Stanley Williams, R. (Eds.) (2005). Nanoelectronics, Volume 80. Springer, March 2005. Special issue of Applied Physics A

  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.

  60. Weiss, R., & Knight, T. F. Jr. (2000). Engineered communications for microbial robotics. In Proceedings of sixth international meeting on DNA based computers (DNA6).

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Hogg, T. Coordinating microscopic robots in viscous fluids. Auton Agent Multi-Agent Syst 14, 271–305 (2007). https://doi.org/10.1007/s10458-006-9004-3

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