Shulou(Shulou.com)11/24 Report--

CTOnews.com Researchers at Queen Mary University of London have made significant progress in the field of bionics by developing a new type of electrically variable stiffness artificial muscle with self-sensing capabilities. This innovative technology has important implications for soft robotics and medical applications, bringing human-machine fusion a step closer. The study was published July 8 in the journal Advanced Intelligent Systems.

The artificial muscle mimics the characteristics of natural muscle, seamlessly transitioning between soft and hard states, while sensing force and deformation through resistance changes. The artificial muscle has flexibility and stretchability similar to natural muscle, suitable for integration with complex soft robot systems, and can adapt to various geometric shapes. Through different voltages, the stiffness can be quickly adjusted to achieve continuous adjustment, stiffness changes more than 30 times. In addition, the technology also has the advantage of fast response time.

CTOnews.com understands that the manufacturing process of this self-sensing artificial muscle is simple and reliable. Carbon nanotubes are mixed with liquid silica gel using ultrasonic dispersion technology and uniformly coated using a coater to form a thin cathode that can also be used as a sensing part of artificial muscle. The anode is cut directly from a soft metal mesh, and the actuating layer is sandwiched between the cathode and anode. After the liquid material solidifies, a complete self-sensing variable stiffness artificial muscle is formed.

The potential applications of the artificial muscle are very wide, ranging from soft robotics to medical applications. Seamless integration with the human body offers the possibility of helping disabled or sick people perform basic daily tasks. By integrating self-sensing artificial muscles, wearable robotic devices can monitor patient activity and provide resistance by adjusting stiffness to facilitate muscle function recovery in rehabilitation training. The research provides a blueprint for the future development of soft robots and wearable robots.

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