Reconfigurable Growth of Engineered Living Materials.

The growth of multicellular organisms is a process akin to additive manufacturing where cellular proliferation and mechanical boundary conditions, among other factors, drive morphogenesis. Engineers have limited ability to engineer morphogenesis to manufacture goods or to reconfigure materials comprised of biomass. Herein, a method that uses biological processes to grow and regrow magnetic engineered living materials (mELMs) into desired geometries is reported. These composites contain Saccharomyces cerevisiae and magnetic particles within a hydrogel matrix. The reconfigurable manufacturing process relies on the growth of living cells, magnetic forces, and elastic recovery of the hydrogel. The mELM then adopts a form in an external magnetic field. Yeast within the material proliferates, resulting in 259 ± 14% volume expansion. Yeast proliferation fixes the magnetic deformation, even when the magnetic field is removed. The shape fixity can be up to 99.3 ± 0.3%. The grown mELM can recover up to 73.9 ± 1.9% of the original form by removing yeast cell walls. The directed growth and recovery process can be repeated at least five times. This work enables ELMs to be processed and reprocessed into user-defined geometries without external material deposition.

Dynamic simulation of articulated soft robots.

Soft robots are primarily composed of soft materials that can allow for mechanically robust maneuvers that are not typically possible with conventional rigid robotic systems. However, owing to the current limitations in simulation, design and control of soft robots often involve a painstaking trial. With the ultimate goal of a computational framework for soft robotic engineering, here we introduce a numerical simulation tool for limbed soft robots that draws inspiration from discrete differential geometry based simulation of slender structures. The simulation incorporates an implicit treatment of the elasticity of the limbs, inelastic collision between a soft body and rigid surface, and unilateral contact and Coulombic friction with an uneven surface. The computational efficiency of the numerical method enables it to run faster than real-time on a desktop processor. Our experiments and simulations show quantitative agreement and indicate the potential role of predictive simulations for soft robot design.