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Design and characterization of a miniature free-swimming robotic fish based on multi-material 3D printing

  • Paul Phamduy
  • Miguel Angel Vazquez
  • Changsu Kim
  • Violet Mwaffo
  • Alessandro Rizzo
  • Maurizio PorfiriEmail author
Regular Paper

Abstract

Research in animal behavior is increasingly benefiting from the field of robotics, whereby robots are being continuously integrated in a number of hypothesis-driven studies. A variety of robotic fish have been designed after the morphophysiology of live fish to study social behavior. Of the current design factors limiting the mimicry of live fish, size is a critical drawback, with available robotic fish generally exceeding the size of popular fish species for laboratory experiments. Here, we present the design and testing of a novel free-swimming miniature robotic fish for animal-robot studies. The robotic fish capitalizes on recent advances in multi-material three-dimensional printing that afford the integration of a range of material properties in a single print task. This capability has been leveraged in a novel design of a robotic fish, where waterproofing and kinematic functionalities are incorporated in the robotic fish. Particle image velocimetry is leveraged to systematically examine thrust production, and independent experiments are conducted in a water tunnel to evaluate drag. This information is utilized to aid the study of the forward locomotion of the robotic fish, through reduced-order modeling and experiments. Swimming efficiency and turning maneuverability is demonstrated through target experiments. This robotic fish prototype is envisaged as a tool for animal-robot interaction studies, overcoming size limitations of current design.

Keywords

Biologically-inspired robots Soft robots Multi-material printing Animal-robot interaction 

Notes

Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant Nos. DRL-1200911, CMMI-1433670, and OISE-1545857. The work of V. Mwaffo was supported in part by a Mitsui USA Foundation scholarship. Alessandro Rizzo acknowledges the support of Compagnia di San Paolo, Italy. The authors would like to thank Gabrielle Cord-Cruz for assisting with the experimental swimming tests.

References

  1. Abaid, N., Bartolini, T., Macri, S., Porfiri, M.: Zebrafish responds differentially to a robotic fish of varying aspect ratio, tail beat frequency, noise, and color. Behav. Brain Res. 233(2), 545–553 (2012)CrossRefGoogle Scholar
  2. Aureli, M., Kopman, V., Porfiri, M.: Free-locomotion of underwater vehicles actuated by ionic polymer metal composites. IEEE/ASME Trans. Mechatron. 15(4), 603–614 (2010)CrossRefGoogle Scholar
  3. Bartolini, T., Mwaffo, V., Butail, S., Porfiri, M.: Effect of acute ethanol administration on zebrafish tail beat motion. Alcohol 49(7), 721–725 (2015)CrossRefGoogle Scholar
  4. Bartolini, T., Mwaffo, V., Showler, A., Macrì, S., Butail, S., Porfiri, M.: Zebrafish response to 3D printed shoals of conspecifics: the effect of body size. Bioinspir. Biomim. 11(2), 026003 (2016)CrossRefGoogle Scholar
  5. Bellman, R.E.: Perturbation Techniques in Mathematics, Engineering and Physics. Cour Corp, Mineola (2003)zbMATHGoogle Scholar
  6. Butail, S., Bartolini, T., Porfiri, M.: Collective response of zebrafish shoals to a free-swimming robotic fish. PLoS One 8(10), e76123 (2013)CrossRefGoogle Scholar
  7. Butail, S., Polverino, G., Phamduy, P., Del Sette, F., Porfiri, M.: Influence of robotic shoal size, configuration, and activity on zebrafish behavior in a free-swimming environment. Behav. Brain Res. 275, 269–280 (2014a)CrossRefGoogle Scholar
  8. Butail, S., Ladu, F., Spinello, D., Porfiri, M.: Information flow in animal-robot interactions. Entropy 16(3), 1315–1330 (2014b)CrossRefGoogle Scholar
  9. Butail, S., Abaid, N., Macrì, S., Porfiri, M.: “Fish–robot interactions: robot fish in animal behavioral studies”, in Robot Fish, Berlin, Germany, pp. 359–377. Springer, Heidelberg (2015)Google Scholar
  10. Cen, L., Erturk, A.: Bio-inspired aquatic robotics by untethered piezohydroelastic actuation. Bioinspir. and Biomim. 8(1), 016006 (2013)CrossRefGoogle Scholar
  11. Cha, Y., Laut, J., Phamduy, P., Porfiri, M.: Swimming robots have scaling laws, too. IEEE/ASME Trans. Mechatron. 21(1), 598–600 (2016)CrossRefGoogle Scholar
  12. Chae, W., Cha, Y., Peterson, S.D., Porfiri, M.: Flow measurement and thrust estimation of a vibrating ionic polymer metal composite. Smart Mater. Struct. 24(9), 095018 (2015)CrossRefGoogle Scholar
  13. Chen, Z., Sharata, S., Tan, X.: Modeling of biomimetic robotic fish propelled by an ionic polymer metal composite caudal fin. IEEE/ASME Trans. Mechatron. 15(3), 448–459 (2010)CrossRefGoogle Scholar
  14. Clapham, R., J., and Hu H.: “iSplash-MICRO: A 50 mm robotic fish generating the maximum velocity of real fish”. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, pp. 287–293 (2014)Google Scholar
  15. DEL Imaging Systems. MotionPro Y-Series compact digital cameras. Available: http://www.delimaging.com/products/MotionPro-Y-Series.htm (2014). Accessed 4 Oct 2016
  16. Eastman, A., Kiefer, J., Kimber, M.: Thrust measurements and flow field analysis of a piezoelectrically actuated oscillating cantilever. Exp. Fluids 53(5), 1533–1543 (2012)CrossRefGoogle Scholar
  17. Facci, A.L., Porfiri, M.: Analysis of three-dimensional effects in oscillating cantilevers immersed in viscous fluids. J. Fluids Struct. 38, 205–222 (2013)CrossRefGoogle Scholar
  18. Faria, J.J., Dyer, J.R.G., Clement, R.O., Couzin, I.D., Holt, N., Ward, A.J.W., et al.: A novel method for investigating the collective behaviour of fish: introducing ‘Robofish’. Behav. Ecol. Sociobiol. 64(8), 1211–1218 (2010)CrossRefGoogle Scholar
  19. Fossen, T.I.: Guidance and Control of Ocean Vehicles. Wiley, New York (1994)Google Scholar
  20. Gazzola, M., Argentina, M., Mahadevan, L.: Scaling macroscopic aquatic locomotion. Nat. Phys. 10, 758–761 (2014)CrossRefGoogle Scholar
  21. Higuchi, H., Van Langen, P., Sawada, H., Tinney, C.E.: Axial flow over a blunt circular cylinder with and without shear layer reattachment. J. Fluids Struct. 22(6), 949–959 (2006)CrossRefGoogle Scholar
  22. Hoerner, S.F.: Fluid-Dynamic Drag: Practical Information on Aerodynamic Drag and Hydrodynamic Resistance. Hoerner Fluid Dynamics, Bakersfield (1965)Google Scholar
  23. Hu, H.: Biologically inspired design of autonomous robotic fish at Essex. In: Proceedings of the IEEE SMC UK-RI Chapter Conference, Sheffield, UK, pp. 3–8 (2006)Google Scholar
  24. Kalueff, A.V., Stewart, A.M., Gerlai, R.: Zebrafish as an emerging model for studying complex brain disorders. Trends Pharmacol. Sci. 35(2), 63–75 (2014)CrossRefGoogle Scholar
  25. Keane, R.D., Adrian, R.J.: Theory of cross-correlation analysis of PIV images. Appl. Sci. Res. 49(3), 191–215 (1992)CrossRefGoogle Scholar
  26. Kitzhofer, J., Ergin, F., G., and Jaunet, V.: “2D least squares matching applied to PIV challenge data (Part 1)”. In Proceedings of the International Symposium on Applications of Laser Techniques to Fluid Mechanics, Lisbon, pp. 9–12 (2012)Google Scholar
  27. Kopman, V., Porfiri, M.: Design, modeling, and characterization of a miniature robotic fish for research and education in biomimetics and bioinspiration. IEEE/ASME Trans. Mechatron. 18(2), 471–483 (2013)CrossRefGoogle Scholar
  28. Kopman, V., Laut, J.W., Polverino, G., Porfiri, M.: Closed-loop control of zebrafish response using a bioinspired robotic-fish in a preference test. J. R. Soc. Interface 10(78), 20120540 (2013)CrossRefGoogle Scholar
  29. Kopman, V., Laut, J., Acquaviva, F., Rizzo, A., Porfiri, M.: Dynamic modeling of a robotic fish propelled by a compliant tail. IEEE J. Ocean. Eng. 40(1), 209–221 (2015)CrossRefGoogle Scholar
  30. Krause, J., Winfield, A.F., Deneubourg, J.L.: Interactive robots in experimental biology. Trends Ecol. Evol. 26(7), 369–375 (2011)CrossRefGoogle Scholar
  31. Ladu, F., Mwaffo, V., Li, J., Macrì, S., Porfiri, M.: Acute caffeine administration affects zebrafish response to a robotic stimulus. Behav. Brain Res. 289, 48–54 (2015)CrossRefGoogle Scholar
  32. Landgraf, T., Bierbach, D., Nguyen, H., Muggelberg, N., Romanczuk, P., Krause, J.: RoboFish: increased acceptance of interactive robotic fish with realistic eyes and natural motion patterns by live Trinidadian guppies. Bioinspir. Biomim. 11(1), 015001 (2016)CrossRefGoogle Scholar
  33. Lighthill, M.J.: Large-amplitude elongated-body theory of fish locomotion. Proc. R. Soc. Lond. B. Biol. Sci. 179(1055), 125–138 (1971)CrossRefGoogle Scholar
  34. Marchese, A.D., Onal, C.D., Rus, D.: Autonomous soft robotic fish capable of escape maneuvers using fluidic elastomer actuators. Soft Robot 1(1), 75–87 (2014)CrossRefGoogle Scholar
  35. Marras, S., Porfiri, M.: Fish and robots swimming together: attraction towards the robot demands biomimetic locomotion. J. R. Soc. Interface 9(73), 1856–1868 (2012)CrossRefGoogle Scholar
  36. Morrison, F.A.: An Introduction to Fluid Mechanics. Cambridge University Press, New York (2013)CrossRefzbMATHGoogle Scholar
  37. Nakayama, Y., Boucher, R.: Introduction to Fluid Mechanics. Butterworth-Heinemann, Oxford (1998)Google Scholar
  38. Stratasys. Objet500 and Objet350 Connex3. Available: http://www.stratasys.com/3d-printers/production-series/connex3-systems (2016). Accessed 4 Oct 2016
  39. Peterson, S.D., Porfiri, M., Rovardi, A.: A particle image velocimetry study of vibrating ionic polymer metal composites in aqueous environments. IEEE/ASME Trans. Mechatron. 14(4), 474–483 (2009)CrossRefGoogle Scholar
  40. Phamduy, P., Polverino, G., Fuller, R.C., Porfiri, M.: Fish and robot dancing together: bluefin killifish females respond differently to the courtship of a robot with varying color morphs. Bioinspir. Biomim. 9(3), 036021 (2014)CrossRefGoogle Scholar
  41. Phamduy, P., LeGrand, R., Porfiri, M.: Design and characterization of an interactive iDevice-controlled robotic fish for informal science education. IEEE Robot. Autom. Mag. 22(1), 86–96 (2015)CrossRefGoogle Scholar
  42. Phamduy, P., Cheong, J., Porfiri, M.: An autonomous charging system for a robotic fish. IEEE/ASME Trans. Mechatron. 21(6), 2953–2963 (2016)CrossRefGoogle Scholar
  43. Polverino, G., Porfiri, M.: Mosquitofish (Gambusia affinis) responds differentially to a robotic fish of varying swimming depth and aspect ratio. Behav. Brain Res. 250(1), 133–138 (2013a)CrossRefGoogle Scholar
  44. Polverino, G., Porfiri, M.: Zebrafish (Danio rerio) behavioural response to bioinspired robotic fish and mosquitofish (Gambusia affinis). Bioinspir. Biomim. 8(4), 044001 (2013b)CrossRefGoogle Scholar
  45. Polverino, G., Phamduy, P., Porfiri, M.: Fish and robots swimming together in a water tunnel: robot color and tail-beat frequency influence fish behavior. PLoS One 8(10), e77589 (2013)CrossRefGoogle Scholar
  46. Prince, C., Lin, W., Lin, J., Peterson, S.D., Porfiri, M.: Temporally-resolved hydrodynamics in the vicinity of a vibrating ionic polymer metal composite. J. Appl. Phys. 107(9), 094908 (2010)CrossRefGoogle Scholar
  47. Raj, A., Thakur, A.: Fish-inspired robots: design, sensing, actuation, and autonomy—a review of research. Bioinspir. Biomim. 11(3), 031001 (2016)CrossRefGoogle Scholar
  48. Roper, D., T., Sharma, S., Sutton, R., and Culverhouse, P.: “A review of developments towards biologically inspired propulsion systems for autonomous underwater vehicles”. In: Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, London, pp. 77–96 (2011)Google Scholar
  49. Rossi, C., Colorado, J., Coral, W., Barrientos, A.: Bending continuous structures with SMAs: a novel robotic fish design. Bioinspir. And Biomim. 6(4), 045005 (2011)CrossRefGoogle Scholar
  50. Ruberto, T., Mwaffo, V., Singh, S., Neri, D., Porfiri, M.: Zebrafish response to a robotic replica in three dimensions. R. Soc. Open Sci. 3(10), 160505 (2016)CrossRefGoogle Scholar
  51. Russell, W.M.S., Burch, R.L., Hume, C.W.: The Principles of Humane Experimental Technique. Johns Hopkins Center for Alternatives to Animal Testing, Baltimore (1959)Google Scholar
  52. Scarano, F., Riethmuller, M.L.: Iterative multigrid approach in PIV image processing with discrete window offset. Exp. Fluids 26(6), 513–523 (1999)CrossRefGoogle Scholar
  53. Spinello, C., Macrì, S., Porfiri, M.: Acute ethanol administration affects zebrafish preference for a biologically-inspired robot. Alcohol 47(5), 391–398 (2013)CrossRefGoogle Scholar
  54. Swain, D.T., Couzin, I.D., Leonard, N.E.: Real-time feedback-controlled robotic fish for behavioral experiments with fish schools. Proc. IEEE 100, 150–163 (2012)CrossRefGoogle Scholar
  55. Tan, X., Carpenter, M., Thon, J., and Alequin-Ramos, F.: “Analytical modeling and experimental studies of robotic fish turning”. In Proceedings of the International Conference on Robotics and Automation, Anchorage, AK, USA, 2010, pp. 102–108Google Scholar
  56. Thielicke, W., and Stamhuis, E.: “PIVlab–Towards user-friendly, affordable and accurate digital particle image velocimetry in MATLAB”. J. Open Res. Softw. 2(1), 1–10 (2014)Google Scholar
  57. Wang, J., Tan, X.: A dynamic model for tail-actuated robotic fish with drag coefficient adaptation. Mechatronics 23(6), 659–668 (2013)CrossRefGoogle Scholar
  58. Wang, J., Tan, X.: Averaging tail-actuated robotic fish dynamics through force and moment scaling. IEEE Trans. Rob. 31(4), 906–917 (2015)CrossRefGoogle Scholar
  59. Wang, J., McKinley, P.K., Tan, X.: Dynamic modeling of robotic fish with a base-actuated flexible tail. ASME J. Dyn. Syst. Meas. Control 137(1), 011004 (2015)CrossRefGoogle Scholar
  60. Yan, Q., Han, Z., Zhang, S.W., Yang, J.: Parametric research of experiments on a carangiform robotic fish. J. Bionic Eng. 5(2), 95–101 (2008)CrossRefGoogle Scholar
  61. Yu, J., Tan, M., Wang, S., Chen, E.: Development of a biomimetic robotic fish and its control algorithm. IEEE Trans. Syst. Man Cybern. B Cybern. 34(4), 1798–1810 (2004)CrossRefGoogle Scholar
  62. Xcitex. ProAnalyst motion analysis software description. Available: http://www.xcitex.com/proanalyst-motion-analysis-software.php (2016). Accessed 4 Oct 2016

Copyright information

© Springer Singapore 2017

Authors and Affiliations

  • Paul Phamduy
    • 1
  • Miguel Angel Vazquez
    • 1
  • Changsu Kim
    • 1
  • Violet Mwaffo
    • 1
  • Alessandro Rizzo
    • 2
    • 3
  • Maurizio Porfiri
    • 1
    Email author
  1. 1.Department of Mechanical and Aerospace EngineeringNew York University Tandon School of Engineering, Six MetroTech CenterBrooklynUSA
  2. 2.Office of InnovationNew York University Tandon School of Engineering, Six MetroTech CenterBrooklynUSA
  3. 3.Dipartimento di Automatica e InformaticaPolitecnico di TorinoTurinItaly

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