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Researchers Create Fastest Swimming Soft Robot Inspired by Manta Rays

A team of researchers has set a new benchmark in robotics by developing the fastest swimming soft robot. This innovation draws inspiration from the swimming techniques of manta rays to enhance the robot’s movement in aquatic environments.

“Two years prior, we unveiled a soft robot that achieved an average swimming speed of 3.74 body lengths per second,” explains Jie Yin, the lead author of the study and an associate professor of mechanical and aerospace engineering at North Carolina State University. “With this latest iteration, we’ve not only improved the speed to an impressive 6.8 body lengths per second, but we’ve also made it more energy-efficient. While the previous model could only operate on the water’s surface, the new design enables vertical mobility throughout the water column.”

The robot features fins modeled after those of manta rays and is constructed from a stable material that performs well when the fins are fully extended. These fins are connected to a pliable silicone body that has an air chamber. By inflating this chamber, the fins bend in a manner akin to the downstroke of a manta ray’s swim. Once the air is released, the fins quickly return to their initial configuration.

“When we pump air into the chamber, it energizes the system,” states Haitao Qing, the primary author of the study and a Ph.D. student at NC State. “The natural tendency of the fins is to revert to their original form, so when we release the air, the energy stored in the fins is also released. This design allows the robot to operate with a single actuator, which facilitates rapid movements.”

The researchers also focused on the fluid dynamics utilized by manta rays to inform the vertical navigation of their soft robot.

“By analyzing the swimming patterns of manta rays, we replicated their movements to enable the robot to ascend, descend, or maintain its position in the water,” describes Jiacheng Guo, a co-author of the study and a Ph.D. student at the University of Virginia. “Manta rays create two jets of water that propel them forward, and they adjust their swimming motion to change direction. We have applied a similar model for controlling the vertical dynamics of our robot while we continue to refine lateral movement techniques.”

“Our simulations and experiments indicated that the downward jet from our robot is stronger than the upward jet,” comments Yuanhang Zhu, a co-author and assistant professor of mechanical engineering at the University of California, Riverside. “When the robot flaps its fins rapidly, it will ascend. Conversely, slowing down the flapping rate allows the robot to sink, facilitating diving or maintaining depth.”

“Additionally, since our robot is powered by compressed air, its buoyancy is influenced by the state of the air chamber,” Qing adds. “With an empty chamber, the robot becomes less buoyant. Hence, when the fins move slowly, the chamber often remains uninflated, allowing for better control of buoyancy during quieter movements.”

The effectiveness of the soft robot has been demonstrated through two experimental setups. In the first instance, the robot successfully navigated an obstacle course situated on both the surface and bottom of a water tank. In another demonstration, the untethered robot managed to transport a payload along the water’s surface, including its own air supply and power source.

“Though our design is intricate, the underlying principles are quite straightforward,” Yin remarks. “With just a single actuator, our robot adeptly traverses complex vertical environments. We are now focusing on enhancing its lateral movements and exploring additional actuation methods to broaden its capabilities—all while preserving the elegance of its design.”

The research is documented in the paper titled “Spontaneous Snapping-Induced Jet Flows for Fast, Maneuverable Surface and Underwater Soft Flapping Swimmer,” published in the journal Science Advances. The study was co-authored by Yinding Chi and Yaoye Hong, both former Ph.D. candidates at NC State, along with Daniel Quinn and Haibo Dong from UVA.

This research received funding from the National Science Foundation through grants 2126072 and 2329674, as well as from the Office of Naval Research under grant N00014-22-1-2616.

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