Abstract
Microscopic robots could perform tasks with high spatial precision, such as acting on precisely-targeted cells in biological tissues. Some tasks may benefit from robots that change shape, such as elongating to improve chemical gradient sensing or contracting to squeeze through narrow channels. This paper evaluates the energy dissipation for shape-changing (i.e., metamorphic) robots whose size is comparable to bacteria. Unlike larger robots, surface forces dominate the dissipation. Theoretical estimates indicate that the power likely to be available to the robots, as determined by previous studies, is sufficient to change shape fairly rapidly even in highly-viscous biological fluids. Achieving this performance will require significant improvements in manufacturing and material properties compared to current micromachines. Furthermore, optimally varying the speed of shape change only slightly reduces energy use compared to uniform speed, thereby simplifying robot controllers.
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Notes
The dissipation shown here is somewhat larger than the value ignoring fluid vorticity [19], due to additional dissipation arising from the velocity gradient at the ends of the treadmill. Dissipation due to this edge-effect depends on the distance at the ends of the tread over which velocity changes. This study uses a distance corresponding to 50 nm bearings for the treadmill [19].
References
Arbuckle D, Requicha AAG (2004) Active self-assembly. In: Tarn TJ, Fukuda T (eds) Proceedings of the IEEE International Conference on Robotics and Automation. IEEE, Los Alamitos, pp 896–901
Berg HC (1993) Random Walks in Biology, 2nd edn. Princeton Univ. Press
Berg HC (2004) E. coli in motion. Springer, New York
Bojinov H, Casal A, Hogg T (2002) Multiagent control of modular self-reconfigurable robots. Artif Intell 142:99–120. arXiv:preprintcs.RO/0006030
Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: From natural to artificial systems. Oxford University Press, Oxford
Castano A, Shen WM, Will P (2000) CONRO: Towards miniature self-sufficient metamorphic robots. Auton Robot 8:309–324
Chan ML et al. (2011) Low friction liquid bearing MEMS micromotor. In: Proceedings of 24th IEEE International Conference on Micro Electro Mechanical Systems (MEMS). doi:10.1109/MEMSYS.2011.5734656, pp 1237–1240
Cumings J, Zettl A (2000) Low-friction nanoscale linear bearing realized from multiwall carbon nanotubes. Science 289:602–604
Drexler KE (1992) Nanosystems: Molecular Machinery, Manufacturing, and Computation. Wiley, New York
Duan X et al. (2012) Intracellular recordings of action potentials by an extracellular nanoscale field-effect transistor. Nat Nanotechnol 7:174–179. doi:10.1038/nnano.2011.223
Dusenbery DB (1998) Fitness landscapes for effects of shape on chemotaxis and other behaviors of bacteria. J Bacteriol 180:5978–5983
Dusenbery DB (2009) Living at micro scale: The unexpected physics of being small. Harvard Univ. Press, Cambridge
Freitas Jr. RA (1999) Nanomedicine: Basic Capabilities, vol I. Landes Bioscience, Georgetown. www.nanomedicine.com/NMI.htm
Freitas Jr RA (2000) Clottocytes: Artificial mechanical platelets IMM Report 18: Nanomedicine, Institute for Molecular Manufacturing, Palo Alto, CA
Gao R, et al. (2012) Outside looking in: Nanotube transistor intracellular sensors. Nano Letters 12:3329–3333
Goldstein SC, et al. (2009) Beyond audio and video: Using claytronics to enable pario. AI Magazine 30 (2):29–45
Happel J, Brenner H (1983) Low Reynolds Number Hydrodynamics, 2nd edn. Kluwer, The Hague
Hernandez-Ortiz JP, Stoltz CG, Graham MD (2005) Transport and collective dynamics in suspensions of confined swimming particles. Phys Rev Lett 95(204):501
Hogg T (2014) Using surface-motions for locomotion of microscopic robots in viscous fluids. J Micro-Bio Robotics 9:61–77. doi:10.1007/s12213-014-0074-z
Hogg T, Freitas Jr. R.A (2010) Chemical power for microscopic robots in capillaries. Nanomedicine: Nanotechnology Biology, and Medicine 6:298–317. doi:10.1016/j.nano.2009.10.002
Hogg T, Freitas Jr RA (2012) Acoustic communication for medical nanorobots. Nano Communication Networks 3:83–102. doi:10.1016/j.nancom.2012.02.002
Kim S, Karrila SJ (2005) Microhydrodynamics. Dover
Kotay K, Rus D, Vona M, McGray C (1998) The self-reconfiguring robotic molecule. In: Proc. of the Conference on Robotics and Automation (ICRA98), p. 424. IEEE
Kotay K, Rus D, Vona M, McGray C (1998) The self-reconfiguring robotic molecule: Design and control algorithms. In: Proceedings of Workshop on Algorithmic Foundations of Robotics
Krim J (2002) Surface science and the atomic-scale origins of friction. Surf Sci 500:741–758
Kubica J, Rieffel E (2002) Creating a smarter membrane: Automatic code generation for modular self-reconfigurable robots. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’02), p 793–800. doi:10.1109/ROBOT.2002.1013455
Lahann J, et al. (2003) A reversibly switching surface. Science 299:371–374
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. doi:10.1016/j.addr.2008.11.002
Lauga E, Powers TR (2009) The hydrodynamics of swimming microorganisms. Rep Prog Phys 72:096,601. doi:10.1088/0034-4885/72/9/096601
Leshansky AM et al. (2007) A frictionless microswimmer. New J Phys 9:145. doi:10.1088/1367-2630/9/5/145
McDonald NV (2005) Diffusion interactions for a pair of reactive spheres. Ph.D. thesis, Univ. of Notre Dame
Purcell EM (1977) Life at low Reynolds number. Am J Phys 45:3–11
Riedel IH et al. (2005) A self-organized vortex array of hydrodynamically entrained sperm cells. Science 309:300–303
Rubenstein M, Cornejo A, Nagpal R (2014) Programmable self-assembly in a thousand-robot swarm. Science 345:795–799. doi:10.1126/science.1254295
Rus D, Vona M (1999) Self-reconfiguration planning with compressible unit modules. In: Proceedings of the Conference on Robotics and Automation (ICRA99), vol. 4, pp. 2513–2520. IEEE
Salemi B, Shen WM, Will P (2001) Hormone controlled metamorphic robots. In: Proceedings of the International Conference on Robotics and Automation (ICRA2001), vol. 4, pp. 4194–4199. IEEE
Santagati GE, Melodia T (2014) Sonar inside your body: Prototyping ultrasonic intra-body sensor networks. In: Proceedings of INFOCOM 2014, pp. 2679–2687. IEEE. doi:10.1109/INFOCOM.2014.6848216
Sitti M et al. (2015) Biomedical applications of untethered mobile milli/microrobots. Proc IEEE 103:205–224. doi:10.1109/JPROC.2014.2385105
Vanossi A et al. (2013) Modeling friction: From nanoscale to mesoscale. Rev Mod Phys 85:529–552. doi:10.1103/RevModPhys.85.529
Werfel J, Petersen K, Nagpal R (2014) Enzyme kinetics, past and present. Science 343:754–758. doi:10.1126/science.1245842
White FM (2005) Viscous Fluid Flow, 3rd edn, McGraw-Hill
Yang J et al. (2013) Observation of high-speed microscale superlubricity in graphite. Phys Rev Lett 110:255,504. doi:10.1103/PhysRevLett.110.255504
Yim M, Duff DG, Roufas KD (2000) PolyBot: A modular reconfigurable robot. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA2000), p 514–520
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Hogg, T. Energy dissipation by metamorphic micro-robots in viscous fluids. J Micro-Bio Robot 11, 85–95 (2016). https://doi.org/10.1007/s12213-015-0086-3
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DOI: https://doi.org/10.1007/s12213-015-0086-3