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We use magnetohydrodynamic levitation as a means to create a soft, elastomeric, solenoid-driven pump (ESP). We present a theoretical framework and fabrication of a pump designed to address the unique challenges of soft robotics, maintaining pumping performance under deformation. Using a permanent magnet as a piston and ferrofluid as a liquid seal, we model and construct a deformable displacement pump. The magnet is driven back and forth along the length of a flexible core tube by a series of solenoids made of thin conductive wire. The magnet piston is kept concentric within the tube by Maxwell stresses within the ferrofluid and magnetohydrodynamic levitation, as viscous lift pressure is created due to its forward velocity. The centering of the magnet reduces shear stresses during pumping and improves efficiency. We provide a predictive model and capture the transient nonlinear dynamics of the magnet during operation, leading to a parametric performance curve characterizing the ESP, enabling goal-driven design. In our experimental validation, we report a shut-off pressure of 2 to 8 kPa and run-out flow rate of 50 to 320 mL⋅min −1 , while subject to deformation of its own length scale, drawing a total of 0.17 W. This performance leads to the highest reported duty point (i.e., pressure and flow rate provided under load) for a pump that operates under deformation of its own length scale. We then integrate the pump into an elastomeric chassis and squeeze it through a tortuous pathway while providing continuous fluid pressure and flow rate; the vehicle then emerges at the other end and propels itself swimming. 

Herein, complex motion in soft, fluid‐driven actuators composed of elastomer bladders arranged around a neutral plane and connected by slender tubes is demonstrated. Rather than relying on complex feedback control or multiple inputs, the motion is generated with a single pressure input, leveraging viscous flows within the actuator to produce nonuniform pressure between bladders. Using an accurate predictive model coupling with a large deformation Cosserat rod model and low‐Reynolds‐number flow, all dominating dynamic interactions including extension and curvature are captured with two governing equations. Given insights from this model, five design elements are described and demonstrated in practice. By choosing the relative timescales between the solid, fluid, and input pressure cycles, the tip of the actuator can obtain almost any desired trajectory and can be placed anywhere temporarily within its 2D workspace. Finally, the benefits of viscous‐driven soft actuators are showcased in a six‐legged untethered robot able to walk 0.05 body lengths per second. The foundation is laid for a new class of morphologically intelligent, soft robotic actuators that enables complex deformations and multifunctionality without explicit drivers; whereby generating nonuniform pressure distributions, their infinite degrees of freedom can be exploited.

 

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.