Fig. 3: Programming and control of the folding process.
a Computational pipeline of shape-programming process by control of temperature (T) distribution. The first step involves decoding of the morphing command into the set of modularized variables (i). Proportional-integral (PI) control on the morphing instruction modulates the temperature distribution (ii), based on the RNI temperature measurement (iii). b Schematic illustration of pulse-width-modulation (PWM)-based morphing basis recruitment. Genetic algorithm decodes the morphing bases into the set of electrodal voltage V that is normalized by maximum voltage level Vmax. c Schematic illustration of the driving principle of the RNI-based temperature measurement. Resistive changes ΔR (normalized by base level R0) are reconstructed. d Demonstration of dynamic shape-programming process in response to sequential user intents, with a photograph of sheet operation (i), IR camera image (ii) and RNI temperature estimation (iii). The below inset shows the schematic illustration for the temporal flow of morphing basis recruitment within a selected morphing scenario. The morphing bases decoded by genetic algorithm are presented. (For the data on the rest morphing scenarios, see Supplementary Fig. 18a). Scale bars, 15 mm.
