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.
Similar content being viewed by others
References
Wen L, Lauder G V. Understanding undulatory locomotion in fishes using an inertia-compensated flapping foil robotic device. Bioinspiration & Biomimetics, 2013, 8, 046013.
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.
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.
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.
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.
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.
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.
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.
Liu J D, Hu H S. Biological inspiration: From carangiform fish to multi-joint robotic fish. Journal of Bionic Engineering, 2010, 7, 35–48.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Van Leeuwen J L. The action of muscles in swimming fish. Experimental Physiology, 1995, 80, 177–191.
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.
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.
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.
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.
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.
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.
Acknowledgment
This work is supported by National Natural Science Foundation of China (under Grant Nos.: 61203353 and 61573038).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Xu, D., Zeng, H., Peng, X. et al. A Stiffness Adjustment Mechanism Based on Negative Work for High-efficient Propulsion of Robotic Fish. J Bionic Eng 15, 270–282 (2018). https://doi.org/10.1007/s42235-018-0021-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42235-018-0021-0