Modeling and control of a sperm-inspired robot with helical propulsion.

Journal: Bioinspiration & biomimetics
PMID:

Abstract

Efficient propulsion has been a central focus of research in the field of biomimetic underwater vehicles. Compared to the prevalent fish-like reciprocating flapping propulsion mode, the sperm-like helical propulsion mode features higher efficiency and superior performance in high-viscosity environments. Based on the previously developed sperm-inspired robot, this paper focuses on its dynamic modeling and depth control research. The helical propulsion performance of the sperm-inspired robot is analyzed by resistance-theory-based force analysis, followed by the application of Kirchhoff rod theory to determine the helical waveform parameters. The dynamic model of the sperm-inspired robot is established using the Kirchhoff equation, and its validity is verified through experiments. To enhance the practical application capability of the sperm-inspired robot, this study develops an active disturbance rejection control depth controller for roll-spin coupling motion based on the constructed dynamics model. The effectiveness of the controller is thoroughly validated through a combination of simulation and experiment. Experimental results demonstrate the excellent depth control ability of the robot, with an average depth error controlled within 0.19 cm. This superior performance lays a foundation for the future application of our robot in underwater operations.

Authors

  • Liangwei Deng
    The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
  • Chao Zhou
    Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania.
  • Zhuoliang Zhang
    Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
  • Xiaocun Liao
    Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
  • Junfeng Fan
    Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
  • Xiaofei Wang
    Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
  • Jiaming Zhang
    School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.