logo

SCIENCE CHINA Information Sciences, Volume 62 , Issue 9 : 194201(2019) https://doi.org/10.1007/s11432-018-9649-8

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

More info
  • ReceivedJul 5, 2018
  • AcceptedSep 30, 2018
  • PublishedJul 25, 2019

Abstract

There is no abstract available for this article.


Acknowledgment

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).


Supplement

Videos and other supplemental documents.


References

[1] Wu Z X, Yu J Z, Su Z S. Towards an Esox lucius inspired multimodal robotic fish. Sci China Inf Sci, 2015, 58: 1-13 CrossRef Google Scholar

[2] Liu J, Wu Z, Yu J. Sliding mode fuzzy control-based path-following control for a dolphin robot. Sci China Inf Sci, 2018, 61: 024201 CrossRef Google Scholar

[3] Gemmell B J, Costello J H, Colin S P. Passive energy recapture in jellyfish contributes to propulsive advantage over other metazoans. Proc Natl Acad Sci USA, 2013, 110: 17904-17909 CrossRef PubMed ADS Google Scholar

[4] Villanueva A, Smith C, Priya S. A biomimetic robotic jellyfish (Robojelly) actuated by shape memory alloy composite actuators. Bioinspir Biomim, 2011, 6: 036004 CrossRef PubMed ADS Google Scholar

[5] Yeom S W, Oh I K. A biomimetic jellyfish robot based on ionic polymer metal composite actuators. Smart Mater Struct, 2009, 18: 085002 CrossRef ADS Google Scholar

[6] Godaba H, Li J, Wang Y. A Soft Jellyfish Robot Driven by a Dielectric Elastomer Actuator. IEEE Robot Autom Lett, 2016, 1: 624-631 CrossRef Google Scholar

[7] Sutton R S, Barto A G. Reinforcement Learning: An Introduction. Cambridge: MIT Press, 1998. Google Scholar

[8] Chen C L, Dong D Y, Li H X. Hybrid MDP based integrated hierarchical Q-learning. Sci China Inf Sci, 2011, 54: 2279-2294 CrossRef Google Scholar

[9] Cui R, Yang C, Li Y. Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning. IEEE Trans Syst Man Cybern Syst, 2017, 47: 1019-1029 CrossRef Google Scholar

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备17057255号       京公网安备11010102003388号