Skip to main content
SpringerLink
Log in

Data Processing Methods of Flow Field Based on Artificial Lateral Line Pressure Sensors

  • Research Article
  • Published:
Journal of Bionic Engineering Aims and scope Submit manuscript

Abstract

The estimation of the type and parameter of flow field is important for robotic fish. Recent estimation methods cannot meet the requirements of the robotic fish due to the lack of prior knowledge or the under-fitting of the model. A processing method including data preprocessing, feature extraction, feature selection, flow type classification and flow field parameters estimation, is proposed based on the data of the pressure sensors in an artificial lateral line. Probabilistic Neural Network (PNN) is used to classify the flow field type and the Generalized Regressive Neural Network (GRNN) is the best choice for estimating the flow field parameters. Also, a few filtering methods for data preprocessing, three methods for feature selection and nine parameters estimation methods are analysis for choosing better method. The proposed method is verified by the experiments with both simulation and real data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data Availability

All data generated or analysed during this study are included in this published article.

References

  1. Yu, J. Z., Tan, M., Wang, S., & Chen, E. K. (2004). Development of a biomimetic robotic fish and its control algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(4), 1798–1810.

    Article  Google Scholar 

  2. Mogdans, J., & Bleckmann, H. (2012). Coping with flow: behavior, neurophysiology and modeling of the fish lateral line system. Biological cybernetics, 106(11), 627–642.

    Article  Google Scholar 

  3. Kottapalli, A. G. P., Asadnia, M., Miao, J. M., & Triantafyllou, M. (2014). Soft polymer membrane micro-sensor arrays inspired by the mechanosensory lateral line on the blind cavefish. Journal of Intelligent Material Systems and Structures, 26, 38–46.

    Article  Google Scholar 

  4. Zhai, Y. F., Zheng, X. W., & Xie, G. M. (2021). Fish Lateral Line Inspired Flow Sensors and Flow-aided Control: A Review. Journal of Bionic Engineering, 18(2), 264–291.

    Article  Google Scholar 

  5. Jiang, Y. G., Ma, Z. Q., Fu, J. C., & Zhang, D. Y. (2017). Development of a flexible artificial lateral line canal system for hydrodynamic pressure detection. Sensors, 17(6), 1220.

    Article  Google Scholar 

  6. Devries, L., & Paley, D. A. (2013). Observability-based optimization for flow sensing and control of an underwater vehicle in a uniform flowfield. In American Control Conference.

  7. Ahrari, A., Hong, L., Sharif, M. A., Deb, K., & Tan, X. B. (2016). Design optimization of an artificial lateral line system incorporating flow and sensor uncertainties. Engineering Optimization, 49(2), 328–344.

    Article  Google Scholar 

  8. Nelson, K., & Mohseni, K. (2017). Design of a 3-d printed, modular lateral line sensory system for hydrodynamic force estimation. Marine Technology Society Journal, 51(5), 103–115.

    Article  Google Scholar 

  9. Liu, G. J., Wang, M. M., Wang, A. Y., Wang, S. R., Yang, T. T., Malekian, R., & Li, Z. X. (2018). Research on flow field perception based on artificial lateral line sensor system. Sensors, 18(3), 838.

    Article  Google Scholar 

  10. Xu, D., Lv, Z. Y., & Zeng, H. N. (2019). Sensor placement optimization in the artificial lateral line using optimal weight analysis combining feature distance and variance evaluation. ISA transactions, 86, 110–121.

    Article  Google Scholar 

  11. Salumae, T., & Kruusmaa, M. (2013). Flow-relative control of an underwater robot. proceedings of the royal society a: Mathematical. Physical and Engineering Science, 469.

  12. Venturelli, R., Akanyeti, O., Visentin, F., Ježov, J., Chambers, L. D., Toming, G., Brown, J., Kruusmaa, M., Megill, W. M., & Fiorini, P. (2012). Hydrodynamic pressure sensing with an artificial lateral line in steady and unsteady flows. Bioinspiration & Biomimetics, 7(3), 036004.

    Article  Google Scholar 

  13. Chambers, L. D., Akanyeti, O., Venturelli, R., Ježov, J., Brown, J., Kruusmaa, M., Fiorini, P., & Megill, W. (2014). A fish perspective: detecting flow features while moving using an artificial lateral line in steady and unsteady flow. Journal of The Royal Society Interface, 11(99), 20140467.

    Article  Google Scholar 

  14. Fukuda, S., Tuhtan, J. A., Fuentes-Perez, J. F., Schletterer, M., & Kruusmaa, M. (2016). Random forests hydrodynamic flow classification in a vertical slot fishway using a bioinspired artificial lateral line probe. In International Conference on Intelligent Robotics and Applications, pages 297–307. Springer.

  15. Akanyeti, O., Chambers, L. D., Ježov, J., Brown, J., Venturelli, R., Kruusmaa, M., Megill, W. M., & Fiorini, P. (2013). Self-motion effects on hydrodynamic pressure sensing: part i. forward-backward motion. Bioinspiration & biomimetics, 8(2), 026001.

    Article  Google Scholar 

  16. Wang, W., Li, Y., Zhang, X. X., Wang, C., Chen, S. M., & Xie, G. M. (2016). Speed evaluation of a freely swimming robotic fish with an artificial lateral line. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages 4737–4742. IEEE.

  17. Devries, L., Lagor, F. D., Lei, H., Tan, X. B., & Paley, D. A. (2015). Distributed flow estimation and closed-loop control of an underwater vehicle with a multi-modal artificial lateral line. Bioinspiration & Biomimetics, 10(2), 025002.

    Article  Google Scholar 

  18. Fuentes, J. F., Tuhtan, J. A., & Carbonell, R. (2015). Current velocity estimation using a lateral line probe. Ecological Engineering, 85, 296–300.

    Article  Google Scholar 

  19. Zheng, X. W., Wang, W., Xiong, M. L., & Xie, G. M. (2020). Online state estimation of a fin-actuated underwater robot using artificial lateral line system. IEEE Transactions on robotics, 36(2), 472–487.

    Article  Google Scholar 

  20. Ma, Z. Q., Xu, Y. H., Jiang, Y. G., Hu, X. H., & Zhang, D. Y. (2020). BTO/P (VDF-TrFE) nanofiber-based artificial lateral line sensor with drag enhancement structures. Journal of Bionic Engineering, 17(1), 64–75.

    Article  Google Scholar 

  21. Strokina, N., Kämäräinen, J. K., Tuhtan, J. A., Fuentes, J. F., & Kruusmaa, M. (2015). Joint estimation of bulk flow velocity and angle using a lateral line probe. Transactions on Instrumentation and Measurement, 65(99), 1–13.

    Google Scholar 

  22. Yang, B., Zhang, T., Liang, Z. Y., & Lu, C. F. (2019). Research on an artificial lateral line system based on a bionic hair sensor with resonant readout. Micromachines, 10, 736.

    Article  Google Scholar 

  23. Tuhtan, J. A., Fuentes-Pérez, J. F., Toming, G., & Kruusmaa, M. (2016). Flow velocity estimation using a fish-shaped lateral line probe with product-moment correlation features and a neural network. Flow Measurement & Instrumentation, 54, S0955598616302266.

    Google Scholar 

  24. Salumäe, T., Ranó, I., Akanyeti, O., & Kruusmaa, M. (2012). Against the flow: A braitenberg controller for a fish robot. In 2012 IEEE International Conference on Robotics and Automation, pages 4210–4215. IEEE.

  25. Zheng, X. W., Wang, C., Fan, R. F., & Xie, G. M. (2017). Artificial lateral line based local sensing between two adjacent robotic fish. Bioinspiration & biomimetics, 13(1), 016002.

    Article  Google Scholar 

  26. Montgomery, J., Baker, C., & Carton, A. (1997). The lateral line can mediate rheotaxis in fish. Nature, 389(6654), 960–963.

    Article  Google Scholar 

  27. Asadnia, M., Kottapalli, A. G. P., Shen, Z. Y., Miao, J. M., & Triantafyllou, M. (2013). Flexible and surface-mountable piezoelectric sensor arrays for underwater sensing in marine vehicles. IEEE Sensors Journal, 13(10), 3918–3925.

    Article  Google Scholar 

  28. Yang, Y. C., Nguyen, N., Chen, N. N., Lockwood, M., Tucker, C., Hu, H., Bleckmann, H., Liu, C., & Jones, D. L. (2010). Artificial lateral line with biomimetic neuromasts to emulate fish sensing. Bioinspiration & Biomimetics, 5(1), 16001.

    Article  Google Scholar 

  29. Mchenry, M. J., Strother, J. A., & Netten, Sietse M.V.. (2008). Mechanical filtering by the boundary layer and fluid-structure interaction in the superficial neuromast of the fish lateral line system. Journal of Comparative Physiology, 194(9), 795–810.

    Article  Google Scholar 

  30. Liu, H., & Motoda, H. (2012). Feature selection for knowledge discovery and data mining, volume 454. Springer Science & Business Media.

  31. Jolliffe, I. T. (2002). Principal component analysis. Journal of Marketing Research, 87(4), 513.

    MATH  Google Scholar 

  32. Scialfa, C. T., & Games, P. A. (1987). Problems with step-wise regression in research on aging and recommended alternatives. Journal of Gerontology, 6, 579–83.

    Article  Google Scholar 

  33. Specht, D. F. (1998). Probabilistic neural networks for classification, mapping, or associative memory. In IEEE International Conference on Neural Networks, pages 525–532.

  34. Everitt, B. S. (2005). Classification and regression trees. Encyclopedia of Statistics in Behavioral Science.

  35. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.

    Article  MATH  Google Scholar 

  36. Svetnik, V. (2003). Random forest: A classification and regression tool for compound classification and qsar modeling. Journal of chemical information and computer sciences, 43(6), 1947–1958.

    Article  Google Scholar 

  37. Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.

    Article  MATH  Google Scholar 

  38. Collins, M., Schapire, R. E., & Singer, Y. (2002). Logistic regression, adaboost and bregman distances. Machine Learning, 48(1–3), 253–285.

    Article  MATH  Google Scholar 

  39. Friedman, J. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232.

    Article  MathSciNet  MATH  Google Scholar 

  40. Denoeux, T. (2008). A k-nearest neighbor classification rule based on Dempster-Shafer theory. In Classic works of the Dempster-Shafer theory of belief functions, pages 737–760. Springer.

  41. Rizzi, F., Qualtieri, A., & Dattoma, T. (2015). Biomimetics of underwater hair cell sensing. Microelectronic Engineering, 132, 90–97.

    Article  Google Scholar 

  42. Chagnaud, B. P., Bleckmann, H., & Hofmann, M. H. (2007). Kármán vortex street detection by the lateral line. Journal of Comparative Physiology A, 193(7), 753–763.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) under Grant 62073017.

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Beihang University, Beijing, 100191, Beijing, China

    Bing Sun & Yi Xu

  2. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, Beijing, China

    Shuhang Xie, Dong Xu & Yupu Liang

Authors
  1. Bing Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuhang Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yupu Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dong Xu.

Ethics declarations

Conflict of Interest

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, B., Xu, Y., Xie, S. et al. Data Processing Methods of Flow Field Based on Artificial Lateral Line Pressure Sensors. J Bionic Eng 19, 1797–1815 (2022). https://doi.org/10.1007/s42235-022-00232-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42235-022-00232-x

Keywords

Search

Navigation