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An SNN-CPG Hybrid Locomotion Control for Biomimetic Robotic Fish

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Abstract

Biomimetic robotic fish that absorbs inspiration from fish has the advantage of high mobility, high efficiency, and low noise. However, it is still challenging to make robotic fish adapt to surrounding aquatic environments autonomously. To achieve this goal, a novel locomotion control method for robotic fish capable of multimode swimming is proposed based on spiking neural networks (SNN) and central pattern generators (CPG) in this study. The proposed method replicates the transmission process of neuron signals when higher organisms move. The spiking neural networks simulate the brain to receive feedback signals and generate motion control commands. Through the bridge of saturation function, spinal cord neurons receive commands and use CPG to generate motor control signals. Repeated and comparative results verify the effectiveness of the hybrid locomotion control method, providing theoretical guidance for the development and control of multimode aquatic robots.

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References

  1. Satish, K.: Neural networks: A classroom approach. Tata McGraw-Hill Education (2004)

  2. Jan, I.A.: Central pattern generators for locomotion control in animals and robots: A review. Neural Networks 21(4), 642–653 (2008). https://doi.org/10.1016/j.neunet.2008.03.014

    Article  Google Scholar 

  3. Marder, E., Bucher, D.: Central pattern generators and the control of rhythmic movements. Current Biology 11(23), R986–R996 (2011). https://doi.org/10.1016/S0960-9822(01)00581-4

    Article  Google Scholar 

  4. Zhang, D., Hu, D., Shen, L., Xie, H.: Design of an artificial bionic neural network to control fish-robot’s locomotion. Neurocomputing 71(4–6), 648–654 (2008). https://doi.org/10.1016/j.neucom.2007.09.007

    Article  Google Scholar 

  5. Zhang, P., Wu, Z., Dong, H., Tan, M., Yu, J.: Reaction-wheel-based roll stabilization for a robotic fish using neural network sliding mode control. IEEE/ASME Transactions on Mechatronics 25 (4), 1904–1911 (2020). https://doi.org/10.1109/TMECH.2020.2992038

    Article  Google Scholar 

  6. Taherkhani, A., Belatreche, A., Li, Y., Cosma, G., Maguire, L.P., McGinnity, T.M.: A review of learning in biologically plausible spiking neural networks. Neural Networks 122, 253–272 (2020). https://doi.org/10.1016/j.neunet.2019.09.036

    Article  Google Scholar 

  7. Clawson, T.S., Ferrari, S., Fuller, S.B., Wood, R.J.: Spiking neural network (SNN) control of a flapping insect-scale robot. In: 2016 IEEE 55th Conference on Decision and Control (CDC), pp. 3381–3388. Las Vegas, USA (2016)

  8. Jiang, Z., Otto, R., Bing, Z., Huang, K., Knoll, A.: Target tracking control of a wheelless snake robot based on a supervised multilayered SNN. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7124–7130. Las Vegas, USA (2020)

  9. Robert, B., Laramee Craig, B, Walker, L., David, S.J.: Evolving spiking neural networks for robot control. Procedia Comput. Sci. 6(1), 329–334 (2011). https://doi.org/10.1016/j.procs.2011.08.060

    Article  Google Scholar 

  10. Nichols, E., McDaid, L.J., Siddique, N.: Biologically inspired SNN for robot control. IEEE Trans. Cybern. 43(1), 115–128 (2013). https://doi.org/10.1109/TSMCB.2012.2200674

    Article  Google Scholar 

  11. Bing, Z., Jiang, Z., Cheng, L., Cai, C., Huang, K., Knoll, A.: End to End Learning of a Multi-Layered SNN Based on R-STDP for a Target Tracking Snake-Like Robot. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 9645–9651, Montreal, Canada. https://doi.org/10.1109/ICRA.2019.8793774(2019)

  12. Wang, M., Yu, J., Tan, M., Wang, H., Li, C.: CPG-based multi-modal swimming control for robotic dolphin. Acta Automatica Sinica 40(9), 1933–1941 (2014). https://doi.org/10.3724/SP.J.1004.2014.01933

    Article  Google Scholar 

  13. Wang, G., Zhang, D., Lin, L., Xie, H., Hu, T., Shen, L.: CPGs control method using a new oscillator in robotic fish. Science China Technological Sciences 53(11), 2914–2919 (2010). https://doi.org/10.1007/s11431-010-4144-8

    Article  MATH  Google Scholar 

  14. Yu, J., Wu, Z., Wang, M., Tan, M.: CPG network optimization for a biomimetic robotic fish via PSO. IEEE Trans. Neural Netw. Learn. Syst. 27(9), 1962–1968 (2016). https://doi.org/10.1109/TNNLS.2015.2459913

    Article  MathSciNet  Google Scholar 

  15. Yu, J., Ming, W., Dong, H., Zhang, Y., Wu, Z.: Motion control and motion coordination of bionic robotic fish: A review. Journal of Bionic Engineering 15(4), 579–598 (2018). https://doi.org/10.1007/s42235-018-0048-2

    Article  Google Scholar 

  16. Yu, J., Tan, M., Chen, J., Zhang, J.: A survey on CPG-inspired control models and system implementation. IEEE Trans. Neural Netw. Learn. Syst. 25(3), 441–456 (2014). https://doi.org/10.1109/TNNLS.2013.2280596

    Article  Google Scholar 

  17. Santos, C.P., Alves, N., Moreno, J.C.: Biped locomotion control through a biomimetic CPG-based controller. Journal of Intelligent & Robotic Systems: Theory & Application 85(1), 47–70 (2017). https://doi.org/10.1007/s10846-016-0407-3

    Article  Google Scholar 

  18. Wang, Z., Gao, Q., Zhao, H.: CPG-inspired locomotion control for a snake robot basing on nonlinear oscillators. Journal of Intelligent & Robotic Systems 85(2), 209–227 (2017). https://doi.org/10.1007/s10846-016-0373-9

    Article  Google Scholar 

  19. Liu, C., Li, X., Zhang, C., Chen, Q.: Multi-layered CPG for adaptive walking of quadruped robots. Journal of Bionic Engineering 15(2), 341–355 (2018). https://doi.org/10.1007/s42235-018-0026-8

    Article  Google Scholar 

  20. Dariusz, G., Jan, A.: Dynamics stability analysis and control of a mammal-like octopod robot driven by different central pattern generators. Journal of Computational Applied Mechanics 50(1), 76–89 (2019). https://doi.org/10.5772/intechopen.90208

    Article  Google Scholar 

  21. Liu, B., Ma, L., Liu, C., Xu, B.: Locomotion control method for humanoid robot based on united hierarchical reinforcement learning. In: 2020 IEEE 16Th International Conference on Control & Automation (ICCA), pp. 1161–1166, Sapporo, Hokkaido, Japan (2020)

  22. Sanjay, L.A., Fang, Y., Ting, J., Arijit, R.: Learning to walk: Bio-mimetic hexapod locomotion via reinforcement-based spiking central pattern generation. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 10(4), 536–545 (2020). https://doi.org/10.1109/JETCAS.2020.3033135

    Article  Google Scholar 

  23. Patrick, R., Brian, M., Fearghal, M., John, M.: Reconfigurable hardware evolution platform for a spiking neural network robotics controller. International Workshop on Applied Reconfigurable Computing, pp. 373–378, Springer (2007)

  24. Wang, M., Li, X., Zhang, Y., Zheng, C., Yu, J.: Locomotion control of robotic fish with a hierarchical framework combining spiking neural networks and CPGs. In: 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 1187–1190, Suzhou, China. https://doi.org/10.1109/CYBER46603.2019.9066711 (2019)

  25. Izhikevich, E.M.: Which model to use for cortical spiking neurons?. IEEE Transactions on Neural Networks 15(5), 1063–1070 (2004). https://doi.org/10.1109/TNN.2004.832719

    Article  Google Scholar 

  26. Lighthill, M.: Note on the swimming of slender fish. Journal of Fluid Mechanics 9(2), 305–317 (1960). https://doi.org/10.1017/S0022112060001110

    Article  MathSciNet  Google Scholar 

  27. Jan, I.A., Alessandro, C., Dimitri, R., Jean-Marie, C.: From swimming to walking with a salamander robot driven by a spinal cord model. Science 315(5817), 1416–1420 (2007). https://doi.org/10.1126/science.1138353

    Article  Google Scholar 

  28. Ding, S., Su, C., Yu, J.: An optimizing BP neural network algorithm based on genetic algorithm. Artif. Intell. Rev. 36, 153–162 (2011)

    Article  Google Scholar 

  29. Liu, B., Wang, R., Zhao, G., Guo, X., Wang, Y., Li, J., Wang, S.: Prediction of rock mass parameters in the TBM tunnel based on BP neural network integrated simulated annealing algorithm. Tunn. Undergr. Space Technol. 95, 103103 (2020). https://doi.org/10.1016/j.tust.2019.103103

    Article  Google Scholar 

  30. Greff, K., Srivastava, R.K., Koutnk, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems 28(10), 2222–2232 (2017). https://doi.org/10.1109/TNNLS.2016.2582924

    Article  MathSciNet  Google Scholar 

  31. Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena 404(8), 132306 (2020). https://doi.org/10.1016/j.physd.2019.132306

    Article  MathSciNet  MATH  Google Scholar 

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Funding

This work was partly supported by National Natural Science Foundation of China under Grants 62073196, U1806204 (for M. Wang), and U1909206, T2121002 (for J. Yu).

Author information

Authors and Affiliations

  1. School of Information and Electrical Engineering, Shandong Jianzhu University, Fengming Road, Jinan, 250101, Shandong, China

    Ming Wang & Yiyang Zhang

  2. Department of Advanced Manufacturing and Robotics, BIC-ESAT, College of Engineering, Peking University, No. 5 Yiheyuan Road Haidian District, Beijing, 100871, China

    Junzhi Yu

  3. Institute of Automation, Chinese Academy of Sciences, No. 95 East Road of Zhongguancun, Haidian District, Beijing, 100190, China

    Junzhi Yu

Authors
  1. Ming Wang
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  2. Yiyang Zhang
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Contributions

All authors (M. Wang, Y. Zhang, J. Yu) contributed to the study conception and design, data collection, and analysis. The first draft of the manuscript was written by M. Wang and Y. Zhang, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Junzhi Yu.

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The authors declare that they have no conflict of interest.

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Source code during the current study is available from the corresponding author on reasonable request.

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Wang, M., Zhang, Y. & Yu, J. An SNN-CPG Hybrid Locomotion Control for Biomimetic Robotic Fish. J Intell Robot Syst 105, 45 (2022). https://doi.org/10.1007/s10846-022-01664-7

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  • DOI: https://doi.org/10.1007/s10846-022-01664-7

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