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exhibits
ACCelerate_2022

What can robots learn
from fish?

University of Virginia

Underwater robots are essential tools for our quest to understand our planet’s oceans. To be effective, these robots should be fast, efficient, maneuverable, and quiet. Some robots have one or two of these traits; fish have all four. Our mission is to help close this performance gap by studying fish-like robots. We are currently studying robots that are tuna-inspired and stingray-inspired. Inspired by tuna, we built a robot that could flap its tail 6 times per second, dart from side to side, and adjust the stiffness of its body in realtime. Inspired by stingrays, we built a robot that can undulate its fins to swim forward or backward along the seafloor. Through a combination of modeling and water channel experiments, we show how bio-inspired motions can double the efficiency of underwater robots. Our models offer clues about how fish move so gracefully—often in tight passageways or coordinated schools with thousands of members.

Team:

  • Jody Kielbas
  • Daniel Quinn
  • Qiang Zhong
  • Leo Liu
  • Joe Zh
  • Carl White