Fast-Swimming Soft Robotic Fish Actuated by Bionic Muscle.

Journal: Soft robotics
PMID:

Abstract

Soft underwater swimming robots actuated by smart materials have unique advantages in exploring the ocean, such as low noise, high flexibility, and friendly environment interaction ability. However, most of them typically exhibit limited swimming speed and flexibility due to the inherent characteristics of soft actuation materials. The actuation method and structural design of soft robots are key elements to improve their motion performance. Inspired by the muscle actuation and swimming mechanism of natural fish, a fast-swimming soft robotic fish actuated by a bionic muscle actuator made of dielectric elastomer is presented. The results show that by controlling the two independent actuating units of a biomimetic actuator, the robotic fish can not only achieve continuous C-shaped body motion similar to natural fish but also have a large bending angle (maximum unidirectional angle is about 40°) and thrust force (peak thrust is about 14 mN). In addition, the coupling relationship between the swimming speed and actuating parameters of the robotic fish is established through experiments and theoretical analysis. By optimizing the control strategy, the robotic fish can demonstrate a fast swimming speed of 76 mm/s (0.76 body length/s), which is much faster than most of the reported soft robotic fish driven by nonbiological soft materials that swim in body and/or caudal fin propulsion mode. What's more, by applying programmed voltage excitation to the actuating units of the bionic muscle, the robotic fish can be steered along specific trajectories, such as continuous turning motions and an S-shaped routine. This study is beneficial for promoting the design and development of high-performance soft underwater robots, and the adopted biomimetic mechanisms, as well as actuating methods, can be extended to other various flexible devices and soft robots.

Authors

  • Ruiqian Wang
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.
  • Chuang Zhang
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Science, Shenyang, 10016, People's Republic of China. University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China.
  • Yiwei Zhang
    College of Chemical Engineering, Nanjing Forestry University Nanjing 210037 China njfu2304@163.com +86-25-85427396.
  • Lianchao Yang
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.
  • Wenjun Tan
    Key Laboratory of Medical Image Computing, Ministry of Education, Shenyang, China.
  • Hengshen Qin
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.
  • Feifei Wang
    Peking University Institute of Mental Health , Beijing , China.
  • Lianqing Liu