Inspired by natural muscle, a key challenge in soft robotics is to develop self-contained electrically driven soft actuators with high strain density. Various characteristics of existing technologies, such as the high voltages required to trigger electroactive polymers ( > 1KV), low strain ( < 10%) of shape memory alloys and the need for external compressors and pressure-regulating components for hydraulic or pneumatic fluidicelastomer actuators, limit their practicality for untethered applications. Here we show a single self-contained soft robust composite material that combines the elastic properties of a polymeric matrix and the extreme volume change accompanying liquid–vapor transition. The material combines a high strain (up to 900%) and correspondingly high stress (up to 1.3 MPa) with low density (0.84 g cm−3). Along with its extremely low cost (about 3 cent per gram), simplicity of fabrication and environment-friendliness, these properties could enable new kinds of electrically driven entirely soft robots.
Ultrasonic additive manufacturing (UAM) is a solid state manufacturing process for joining thin metal tapes using principles of ultrasonic metal welding. The process operates at low temperatures, enabling dissimilar material welds without generating harmful intermetallic compounds. In this study, a 9 kW UAM system was used to create joints of Al 1100 and commercially pure titanium. Viable process parameters were identified through pilot weld studies via controlled variation of weld force, amplitude and weld speed. Push-pin delamination tests and shear tests were performed, comparing as-built, heat treated and spark plasma sintering treated samples. Heat treated and spark plasma sintering treated samples yielded mechanical strengths over twice that of as-built samples. Electron backscatter diffraction measurements show that deformation and grain refinement only take place in the aluminium layers. Heat treated samples exhibit a thin intermetallic layer, which is hypothesised as constraining the interface, leading to the improved strength.
Synthesizing robots via physical artificial intelligence is a multidisciplinary challenge for future robotics research. An education methodology is needed for researchers to develop a combination of skills in physical artificial intelligence.Akin to biological organisms, the next generations of robots are expected to act autonomously in the unstructured environment of the real world and be self-sustained in controller adaptation and learning 1 , physical resilience to damages 2 and integration with collective systems 3 . These future robots will decide and adapt in real time on navigation, locomotion and manipulation by processing combinations of signals, simultaneously sent from multiple sensors in their "body"
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