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NAU mechanical engineers develop new high-performance artificial

Within the discipline of robotics, researchers are often looking for the fastest, most robust, best, and lowest-cost methods, or actions to allow robots to maintain their capabilities.

The pursuit of brand new and highly actuation applied science and “gentle” robotics is usually based on the laws of biomimetics, during which machine parts are designed to simulate the motion of human muscle tissue – and ideally , to make them perform better.

Despite the efficiency of actuators such as electric motors and hydraulic pistons, their inflexible type limits how often they are deployed. As robots transition to additional organic types and as people demand additional biomimetic prostheses, actuators must evolve.

Affiliated professor and alumnus Michael Schaefer and Professor Heidi Feigenbaum of Northern Arizona College’s Department of Mechanical Engineering, together with graduate scholar researcher Diego Higueras-Ruiz, revealed a paper in Science Robotics in which they developed a new, high-performance synthetic Introduced muscle specialization. Dynamic Active Systems Laboratory.

Titled “Cavatappi Synthetic Muscle Tissue Drawing, From Twisting and Polymer Tubes”, it describes how the new technology allows for additional human-like movement as a result of its flexibility and flexibility, though outperforms human skeletal muscle in many ways. . Metrics

“We name these new linear actuator cavatappi synthetic muscle tissues primarily based on their resemblance to Italian pasta,” noted Schafer.

Because of their coiled, or helical, construction, actuators can generate additional energy, making them an excellent specialization for bioengineering and robotics tasks. Within the staff’s initial work, they demonstrated that cavatappi synthetic muscle tissue exhibits specialized function and energy metrics, increased by ten and 5-fold, respectively, compared to human skeletal muscle tissue, and as they progressed to growth. , they are relied upon to provide an increased range of efficiency.

“Cavatappi synthetic muscle tissues are primarily based on Twisted Polymer Actuators (TPAs), which have been quite revolutionary since they first came out because they are highly effective, light weight and low cost. However, they are very inefficient and They have been sluggish to work because you needed them to warm up and funky. Plus, their effectiveness is barely around 2%,” Schafer said.

“For Cavatappi, we achieve this through the use of pressurized fluids to actuate, so we predict that these gadgets are more likely to be adopted. These gadgets respond as quickly as possible. The quicker we are able to pump fluid. The big advantage is their effectiveness. We have demonstrated compressive effectiveness of up to about 45%, which is a very high amount in the area of ​​sappy actuation.”

Engineers believe this expertise could be used in lightweight robotics tasks, specialized robotic actuators similar to walking robots, or perhaps in assistive applied science such as exoskeletons or prostheses.

“We are confident that future work will include the use of Cavatappi synthetic muscle tissues as a result of their simplicity, low cost, light weight, flexibility, effectiveness and pressure vitality restoration properties, among other benefits,” Shafer mentioned.

Information on licensing, partnership options available

Working with the NAU corrections staff, the inventors have taken steps to protect their mental assets. The expertise has entered the security and early commercialization phase and is out there for licensing and partnership options. For additional data, contact NAU Innovation.

Schaefer joined NAU in 2013. His various analytical works are associated with vitality harvesting, wildlife telemetry programs and unmanned aerial programs.

Feigenbaum joined NAU in 2007, and her other research work includes ratcheting in metals and fair supply. Higueras-Ruiz received a diploma in mechanical engineering from NAU in 2018 and can complete his Ph.D. in Bioengineering within the fall. This work is supported by a grant from NAU’s Analysis and Improvement Early Research Program.

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