Science China Information Sciences

, 62:194201 | Cite as

Design and attitude control of a novel robotic jellyfish capable of 3D motion

  • Junzhi YuEmail author
  • Xiangbin Li
  • Lei Pang
  • Zhengxing Wu



This work was supported by National Natural Science Foundation of China (Grant Nos. 61725305, 61633020, 61633017, 61573226) and Key Project of Frontier Science Research of Chinese Academy of Sciences (Grant No. QYZDJ-SSW-JSC004).

Supplementary material

Supplementary material, approximately 21 MB.

11432_2018_9649_MOESM2_ESM.pdf (1.5 mb)
Design and attitude control of a novel robotic jellyfish capable of 3D motion


  1. 1.
    Wu Z X, Yu J Z, Su Z S, et al. Towards an Esox lucius inspired multimodal robotic fish. Sci China Inf Sci, 2015, 58: 052203Google Scholar
  2. 2.
    Liu J C, Wu Z X, Yu J Z, et al. Sliding mode fuzzy control-based path-following control for a dolphin robot. Sci China Inf Sci, 2018, 61: 024201MathSciNetCrossRefGoogle Scholar
  3. 3.
    Gemmell B J, Costello J H, Colin S P, et al. Passive energy recapture in jellyfish contributes to propulsive advantage over other metazoans. Proc Natl Acad Sci USA, 2013, 110: 17904–17909CrossRefGoogle Scholar
  4. 4.
    Villanueva A, Smith C, Priya S. A biomimetic robotic jellyfish (Robojelly) actuated by shape memory alloy composite actuators. Bioinspir Biomim, 2011, 6: 036004CrossRefGoogle Scholar
  5. 5.
    Yeom S W, Oh I K. A biomimetic jellyfish robot based on ionic polymer metal composite actuators. Smart Mater Struct, 2009, 18: 085002CrossRefGoogle Scholar
  6. 6.
    Godaba H, Li J S, Wang Y Z, et al. A soft jellyfish robot driven by a dielectric elastomer actuator. IEEE Robot Autom Lett, 2016, 1: 624–631CrossRefGoogle Scholar
  7. 7.
    Sutton R S, Barto A G. Reinforcement Learning: An Introduction. Cambridge: MIT Press, 1998zbMATHGoogle Scholar
  8. 8.
    Chen C L, Dong D Y, Li H X, et al. Hybrid MDP based integrated hierarchical Q-learning. Sci China Inf Sci, 2011, 54: 2279–2294MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Cui R X, Yang C G, Li Y, et al. Adaptive neural network control of AUVs with control input nonlinearities using reinforcement learning. IEEE Trans Syst Man Cybern Syst, 2017, 47: 1019–1029CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Junzhi Yu
    • 1
    • 2
    Email author
  • Xiangbin Li
    • 1
    • 2
  • Lei Pang
    • 1
    • 2
  • Zhengxing Wu
    • 1
    • 2
  1. 1.State Key Laboratory of Management and Control for Complex Systems, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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