Scientists have created a lightweight fluidic engine to provide power to soft robots that mimic muscles for use in assistive devices. The unique feature of this new engine is its ability to produce substantial force without the need for an external power supply.
“Soft robots driven by fluid engines, such as hydraulic or pneumatic mechanisms, can imitate muscle behavior in ways that rigid robots cannot,” said Hao Su, the lead author of the study and an associate professor of mechanical and aerospace engineering at North Carolina State University. “This makes such robots highly suitable for enhancing people’s mobility in their upper or lower limbs.”
Traditionally, fluid engines are directly connected to external power sources like large air compressors, limiting their flexibility. Previous untethered fluid engines were not able to generate significant force, which restricted their practical applications.
“Our research addresses both these challenges,” Su explained. “Our fluidic engine operates independently without an external source and can produce up to 580 Newtons of force.”
The new engine functions by circulating oil into and out of a chamber within a soft robot, simulating the contraction and relaxation of muscles. A battery-powered high-torque motor drives the pump of the fluidic engine, enabling it to achieve high pressure and allowing the artificial muscle to exert strong force.
In experimental trials, the researchers evaluated both the force generation capabilities of the new engine and its efficiency in converting electrical power into fluidic power.
“We discovered that our untethered engine can generate an unprecedented amount of force while remaining lightweight,” said Antonio Di Lallo, the primary author of the study and a postdoctoral researcher at NC State. “Furthermore, our fluidic engine exhibits higher maximum efficiency compared to previous portable, untethered engines.”
The study titled “Untethered Fluidic Engine for High-Force Soft Wearable Robots” is openly accessible in the journal Advanced Intelligent Systems. The research was a collaborative effort involving Shuangyue Yu, a former postdoctoral researcher at NC State; Jie Yin, an associate professor of mechanical and aerospace engineering at NC State; Jonathon Slightam from Sandia National Laboratories; and Grace Gu from the University of California, Berkeley.
This project received funding from the National Science Foundation through grants 2026622 and 1944655; the National Institute on Disability, Independent Living, and Rehabilitation Research through grant 90DPGE0011; and Amazon Robotics.
