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The locomotion of soft snake robots is dependent on frictional interactions with the environment. Frictional anisotropy is a morphological characteristic of snakeskin that allows snakes to engage selectively with surfaces and generate propulsive forces. The prototypical slithering gait of most snakes is lateral undulation, which requires a significant lateral resistance that is lacking in artificial skins of existing soft snake robots. We designed a set of kirigami lattices with curvilinearly-arranged cuts to take advantage of in-plane rotations of the 3D structures when wrapped around a soft bending actuator. By changing the initial orientation of the scales, the kirigami skin produces high lateral friction upon engagement with surface asperities, with lateral to cranial anisotropic friction ratios above 4. The proposed design increased the overall velocity of the soft snake robot more than fivefold compared to robots without skin. 

Abstract Soft robots join body and actuation, forming their structure from the same elements that induce motion. Soft actuators are commonly modeled or characterized as primary movers, but their second role as support structure introduces strain–pressure combinations outside of normal actuation. This article examines a more complete set of possible strain–pressure combinations for McKibben actuators, including passive or unpressurized, deformation, pressurized extension and compression of a pressurized actuator beyond the maximum actuation strain. Each region is investigated experimentally, and empirical force–displacement–pressure relationships are identified. Particular focus is placed on ensuring that empirical relationships are consistent at boundaries between an actuator’s strain–pressure regions. The presented methodology is applied to seven McKibben actuator designs, which span variations in wall thickness, enclosure material, and actuator diameter. Empirical results demonstrate a trade-off between maximum contraction strain and force required to passively extend. The results also show that stiffer elastomers require an extreme increase in pressure to contract without a compensatory increase in maximum achieved force. Empirical force–displacement–pressure models were developed for each variant across all the studied strain–pressure regions, enabling future design variation studies for soft robots that use actuators as structures. 

Current models of bending in soft arms are formulated in terms of experimentally determined, arm-specific parameters, which cannot evaluate fundamental differences in soft robot arm design. Existing models are successful at improving control of individual arms but do not give insight into how the structure of the arm affects the arm’s capabilities. For example, omnidirectional soft robot arms most frequently have three parallel actuators, but may have four or more, while common biological arms, including octopuses, have tens of distinct longitudinal muscle bundles. This article presents a quasi-static analytical model of soft arms bent with longitudinal actuators, based on equilibrium principles and assuming an unknown neutral axis location. The model is presented as a generalizable framework and specifically implemented for an arm with [Formula: see text] fluid-driven actuators, a subset of which are pressurized to induce a bend with a certain curvature and direction. The presented implementation is validated experimentally using planar (2D) and spatial (3D) bends. The planar model is used to initially estimate pressure for a closed-loop curvature control system and to bound the accessible configurations for a rapidly-exploring random trees (RRT) motion planner. A three-segment planar arm is demonstrated to navigate along a planned trajectory through a gap in a wall. Finally, the model is used to explore how the arm morphology affects maximum curvature and directional resolution. This research analytically connects soft arm structure and actuator behavior to unloaded arm performance, and the results may be used to methodically design soft robot arms. 

An acoustic liquefaction approach to enhance the flow of yield stress fluids during Digital Light Processing (DLP)‐based 3D printing is reported. This enhanced flow enables processing of ultrahigh‐viscosity resins (μ_app > 3700 Pa s at shear rates  = 0.01 s^–1) based on silica particles in a silicone photopolymer. Numerical simulations of the acousto–mechanical coupling in the DLP resin feed system at different agitation frequencies predict local resin flow velocities exceeding 100 mm s^–1at acoustic transduction frequencies of 110 s^–1. Under these conditions, highly loaded particle suspensions (weight fractions, ϕ = 0.23) can be printed successfully in complex geometries. Such mechanically reinforced composites possess a tensile toughness 2000% greater than the neat photopolymer. Beyond an increase in processible viscosities, acoustophoretic liquefaction DLP (AL‐DLP) creates a transient reduction in apparent viscosity that promotes resin recirculation and decreases viscous adhesion. As a result, acoustophoretic liquefaction Digital Light Processing (AL‐DLP) improves the printed feature resolution by more than 25%, increases printable object sizes by over 50 times, and can build parts >3 × faster when compared to conventional methodologies.