Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed electrically-driven soft valves to control hydraulic soft actuators. These valves could be used in assistive and therapeutic devices, bio-inspired soft robots, soft grippers, surgical robots, and more.

The research was published in theProceedings of the National Academy of Sciences (PNAS).

“今天的刚性大大lim监管系统it the adaptability and mobility of fluid-driven soft robots,” said Robert J. Wood, the Harry Lewis and Marlyn McGrath Professor of Engineering and Applied Sciences at SEAS and senior author of the paper. “Here, we have developed soft and lightweight valves to control soft hydraulic actuators that open up possibilities for soft on-board controls for future fluidic soft robots.”

Soft valves aren’t new but so far none have achieved the pressure or flow rates required by many existing hydraulic actuators. To overcome those limitations, the team developed new electrically powered dynamic dielectric elastomer actuators (DEAs). These soft actuators have ultra-high power density, are lightweight, and can run for hundreds of thousands of cycles. The team combined these new dielectric elastomer actuators with a soft channel, resulting in a soft valve for fluidic control.

“这些软阀有一个快速响应时间和一个re able to control fluidic pressure and flow rates that match the needs of hydraulic actuators,” said Siyi Xu, a graduate student at SEAS and first author of the paper. “These valves give us fast, powerful control of macro-and small-scale hydraulic actuators with internal volume ranging from hundreds of microliters to tens of milliliters.”

Using the DEA soft valves, the researchers demonstrated control of hydraulic actuators of different volumes and achieved independent control of multiple actuators powered by a single pressure source.

“This compact and light-weight DEA valve is capable of unprecedented electrical control of hydraulic actuators, showing the potential for future on-board motion control of soft fluid-driven robots,” said Xu.

The research was co-authored by Yufeng Chen, Nak-Seung Patrick Hyun, and Kaitlyn Becker. It was supported by the National Science Foundation and the National Robotic Initiative under award CMMI-1830291.