Untethered soft robots using compliant lightweight actuators are capable of dynamic locomotion at biologically relevant speeds.

As the field of soft robotics advances, full autonomy becomes highly sought after, especially if robot motion can be powered by environmental energy. This would present a self-sustained approach in terms of both energy supply and motion control. Now, autonomous movement can be realized by leveraging out-of-equilibrium oscillatory motion of stimuli-responsive polymers under a constant light source. It would be more advantageous if environmental energy could be scavenged to power robots. However, generating oscillation becomes challenging under the limited power density of available environmental energy sources. Here, we developed fully autonomous soft robots with self-sustainability based on self-excited oscillation. Aided by modeling, we have successfully reduced the required input power density to around one-Sun level through a liquid crystal elastomer (LCE)–based bilayer structure. The autonomous motion of the low-intensity LCE/elastomer bilayer oscillator “LiLBot” under low energy supply was achieved by high photothermal conversion, low modulus, and high material responsiveness simultaneously. The LiLBot features tunable peak-to-peak amplitudes from 4 to 72 degrees and frequencies from 0.3 to 11 hertz. The oscillation approach offers a strategy for designing autonomous, untethered, and sustainable small-scale soft robots, such as a sailboat, walker, roller, and synchronized flapping wings.

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.

Rod–rod contact is critical in simulating knots and tangles. To simulate contact, typically a contact force is applied to enforce nonpenetration condition. This force is often applied explicitly (Euler forward). At every time-step in a dynamic simulation, the equations of motions are solved over and over again until the right amount of contact force successfully imposes the nonpenetration condition. There are two drawbacks: (1) Explicit implementation brings numerical convergence issues. (2) Solving equations of motion iteratively to find this right contact force slows down the simulation. In this article, we propose a simple, efficient, and fully implicit contact model with high convergence properties. This model is shown to be capable of taking large time-steps without forfeiting accuracy during knot tying simulations when compared to previous methods. We introduce a new contact potential, based on a smoothed segment–segment distance function, that is an analytic function of the four endpoints of the two contacting edges. Since this contact potential is differentiable, we can incorporate its force (negative gradient of the energy) and Jacobian (negative Hessian of the energy) into the elastic rod simulation.