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Journal of Bionic Engineering

, Volume 15, Issue 2, pp 270–282 | Cite as

A Stiffness Adjustment Mechanism Based on Negative Work for High-efficient Propulsion of Robotic Fish

  • Dong Xu
  • Haining Zeng
  • Xiang Peng
  • Ziqing Zhao
  • Jingmeng Liu
Article

Abstract

The applications of robotic fish require high propulsive efficiency mechanism to prolong the mission time. Though many methods were applied, robotic fish still suffers from low efficiency. To improve the efficiency of robotic fish, this paper proposes a variable stiffness mechanism which is based on the negative work. The live fish adjusts its body stiffness to save energy when the muscles do negative work. Inspired by the live fish, a control mechanism based on negative work is proposed to change the stiffness of the robotic fish for higher efficiency. Changing the stiffness of the robotic fish is to change the joint-stiffness. A fuzzy controller is introduced to mimic the variable stiffness mechanism of the fish and depicts the relationship between the stiffness and the negative work. To evaluate the performance of this controller, a two-joint robotic fish model is established based on its kinematic model and hydrodynamic model. The evaluation results show that the robotic fish reduces the energy consumption and improves the propulsion efficiency when introducing the variable stiffness mechanism. Different environments with the control mechanism impact differently on propulsive efficiency. This mechanism may provide a high efficient propulsion control method for the robotic fish.

Keywords

robotic fish propulsion efficiency negative work stiffness adjustment 

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Notes

Acknowledgment

This work is supported by National Natural Science Foundation of China (under Grant Nos.: 61203353 and 61573038).

References

  1. [1]
    Wen L, Lauder G V. Understanding undulatory locomotion in fishes using an inertia-compensated flapping foil robotic device. Bioinspiration & Biomimetics, 2013, 8, 046013.CrossRefGoogle Scholar
  2. [2]
    Yu J Z, Tan M, Wang S. Development of a biomimetic robotic fish and its control algorithm. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 2004, 34, 1798–1810.CrossRefGoogle Scholar
  3. [3]
    Witt C, Wen L, Lauder G V. Hydrodynamics of C-start escape responses of fish as studied with simple physical models. Integrative and Comparative Biology, 2015, 54, 728–739.CrossRefGoogle Scholar
  4. [4]
    Nguyen P L, Do V P, Lee B R. Dynamic modeling of a non-uniform flexible tail for a robotic fish. Journal of Bionic Engineering, 2013, 10, 201–209.CrossRefGoogle Scholar
  5. [5]
    Yu J Z, Su Z S, Wu Z X, Tan M. Development of a fast-swimming dolphin robot capable of leaping. IEEE/ASME Transactions on Mechatronics, 2016, 21, 2307–2316.CrossRefGoogle Scholar
  6. [6]
    Ren Q Y, Xu J X, Fan L P, Niu X L. A GIM-based biomimetic learning approach for motion generation of a multi-joint robotic fish. Journal of Bionic Engineering, 2013, 10, 423–433.CrossRefGoogle Scholar
  7. [7]
    Ren Q Y, Xu J X, Li X F. A data-driven motion control approach for a robotic fish. Journal of Bionic Engineering, 2015, 12, 381–394.CrossRefGoogle Scholar
  8. [8]
    Nakabayashi M, Kobayashi R, Kobayashi S, Morikawa H. Bioinspired propulsion mechanism using a fin with a dynamic variable-effective-length spring: Evaluation of thrust characteristics and flow around a fin in a uniform flow. Journal of Biomechanical Science and Engineering, 2009, 4, 82–93.CrossRefGoogle Scholar
  9. [9]
    Liu J D, Hu H S. Biological inspiration: From carangiform fish to multi-joint robotic fish. Journal of Bionic Engineering, 2010, 7, 35–48.CrossRefGoogle Scholar
  10. [10]
    Wang J X, McKinley P K, Tan X B. Dynamic modeling of robotic fish with a base-actuated flexible tail. Journal of Dynamic Systems Measurement and Control-Transactions of the ASME, 2015, 137, 011004.CrossRefGoogle Scholar
  11. [11]
    Esposito C J, Tangorra J L, Flammang B E, Lauder G V. A robotic fish caudal fin: Effects of stiffness and motor program on locomotor performance. The Journal of Experimental Biology, 2012, 215, 56–67.CrossRefGoogle Scholar
  12. [12]
    Feilich K L, Lauder G V. Passive mechanical models of fish caudal fins: Effects of shape and stiffness on self-propulsion. Bioinspiration & Biomimetics, 2015, 10, 036002.CrossRefGoogle Scholar
  13. [13]
    Kahn J, Peretz D J, Tangorra J L. Predicting propulsive forces using distributed sensors in a compliant, high DOF, robotic fin. Bioinspiration & Biomimetics, 2015, 10, 82–93.CrossRefGoogle Scholar
  14. [14]
    Kopman V, Laut J, Porfiri M, Acquaviva F, Rizzo A. Dynamic modeling of a robotic fish propelled by a compliant tail. IEEE Journal of Oceanic Engineering, 2015, 40, 209–221.CrossRefGoogle Scholar
  15. [15]
    Korkmaz D, Akpolat Z H, Soyguder S, Alli H. Dynamic simulation model of a biomimetic robotic fish with multi-joint propulsion mechanism. Transactions of the Institute of Measurement and Control, 2015, 37, 684–695.CrossRefGoogle Scholar
  16. [16]
    Cochran J, Kanso E, Kelly S D. Xiong H L, Krstic M. Source seeking for two nonholonomic models of fish locomotion. IEEE Transactions on Robotics, 2009, 25, 1166–1176.Google Scholar
  17. [17]
    Wang M, Yu J Z, Tan M. CPG-based sensory feedback control for bio-inspired multimodal swimming. International Journal of Advanced Robotic Systems, 2014, 11, 1–11.CrossRefGoogle Scholar
  18. [18]
    Chowdhury A R, Kumar V, Prasad B, Kumar R, Panda S. Model-based control of a BCF mode carangiform bioinspired robotic fish. Marine Technology Society Journal, 2014, 48, 36–50.CrossRefGoogle Scholar
  19. [19]
    Koca G O, Korkmaz D, Bal C, Akpolat Z H, Ay M. Implementations of the route planning scenarios for the autonomous robotic fish with the optimized propulsion mechanism. Measurement, 2016, 93, 232–242.CrossRefGoogle Scholar
  20. [20]
    Xu D, Zhang S G, Wen L. A Stiffness-adjusting method to improve thrust efficiency of a two-joint robotic fish. Advances in Mechanical Engineering, 2014, 2, 1–7.CrossRefGoogle Scholar
  21. [21]
    Van Leeuwen J L. The action of muscles in swimming fish. Experimental Physiology, 1995, 80, 177–191.CrossRefGoogle Scholar
  22. [22]
    Lauder G V, Flammang B E, Alben S. Passive robotic models of propulsion by the bodies and caudal fins of fish. Integrative and Comparative Biology, 2012, 52, 576–587.CrossRefGoogle Scholar
  23. [23]
    Chowdhury A R, Sasidhar S, Panda S K. Bio-harmonized control experiments of a carangiform robotic fish underwater vehicle. Advanced Robotics, 2015, 30, 1–14.Google Scholar
  24. [24]
    Wardle C S, Videler J J, Altringham J D. Tuning in to fish swimming waves: Body form, swimming mode and muscle function. The Journal of Experimental Biology, 1995, 198, 1629–1636.Google Scholar
  25. [25]
    Mchenry M J, Pell C, Jr J H L. Mechanical control of swimming speed: Stiffness and axial wave form in undulating fish models. The Journal of Experimental Biology, 1995, 198, 2293–2305.Google Scholar
  26. [26]
    Wen L, Wang T M, Wu G H, Liang J H. Quantitative thrust efficiency of a self-propulsive robotic fish: Experimental method and hydrodynamic investigation. IEEE/ASME Transactions on Mechatronics, 2013, 18, 1027–1038.CrossRefGoogle Scholar
  27. [27]
    Lee P J, Lee M S, Wang R C. A Fuzzy control based robotic fish with multiple actuators. International Journal of Fuzzy Systems, 2012, 14, 45–53.Google Scholar

Copyright information

© Jilin University 2018

Authors and Affiliations

  • Dong Xu
    • 1
  • Haining Zeng
    • 1
  • Xiang Peng
    • 1
  • Ziqing Zhao
    • 1
  • Jingmeng Liu
    • 1
  1. 1.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina

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