Haptic perception using optoelectronic robotic flesh for embodied artificially intelligent agents.

Flesh encodes a variety of haptic information including deformation, temperature, vibration, and damage stimuli using a multisensory array of mechanoreceptors distributed on the surface of the human body. Currently, soft sensors are capable of detecting some haptic stimuli, but whole-body multimodal perception at scales similar to a human adult (surface area ~17,000 square centimeters) is still a challenge in artificially intelligent agents due to the lack of encoding. This encoding is needed to reduce the wiring required to send the vast amount of information transmitted to the processor. We created a robotic flesh that could be further developed for use in these agents. This engineered flesh is an optical, elastomeric matrix "innervated" with stretchable lightguides that encodes haptic stimuli into light: temperature into wavelength due to thermochromic dyes and forces into intensity due to mechanical deformation. By exploiting the optical properties of the constitutive materials and using machine learning, we infer spatiotemporal, haptic information from light that is read by an image sensor. We demonstrate the capabilities of our system in various assemblies to estimate temperature, contact location, normal and shear force, gestures, and damage from temporal snapshots of light coming from the entire haptic sensor with errors <5%.

Acoustophoretic Liquefaction for 3D Printing Ultrahigh-Viscosity Nanoparticle Suspensions.

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 (µapp  > 3700 Pa s at shear rates γ ˙  = 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-1 at 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.

Autonomic perspiration in 3D-printed hydrogel actuators.

In both biological and engineered systems, functioning at peak power output for prolonged periods of time requires thermoregulation. Here, we report a soft hydrogel-based actuator that can maintain stable body temperatures via autonomic perspiration. Using multimaterial stereolithography, we three-dimensionally print finger-like fluidic elastomer actuators having a poly-N-isopropylacrylamide (PNIPAm) body capped with a microporous (~200 micrometers) polyacrylamide (PAAm) dorsal layer. The chemomechanical response of these hydrogel materials is such that, at low temperatures (<30°C), the pores are sufficiently closed to allow for pressurization and actuation, whereas at elevated temperatures (>30°C), the pores dilate to enable localized perspiration in the hydraulic actuator. Such sweating actuators exhibit a 600% enhancement in cooling rate (i.e., 39.1°C minute-1) over similar non-sweating devices. Combining multiple finger actuators into a single device yields soft robotic grippers capable of both mechanically and thermally manipulating various heated objects. The measured thermoregulatory performance of these sweating actuators (~107 watts kilogram-1) greatly exceeds the evaporative cooling capacity found in the best animal systems (~35 watts kilogram-1) at the cost of a temporary decrease in actuation efficiency.

Optical Lace for Synthetic Afferent Neural Networks.

While vision dominates sensing in robots, animals with limited vision deftly navigate their environment using other forms of perception such as touch. Efforts have been made to apply artificial skins with tactile sensing to robots for similarly sophisticated mobile and manipulative skills. The ability to functionally mimic the afferent sensory neural network, required for distributed sensing and communication networks throughout the body is still missing. This limitation is partially due to the lack of cointegration of the mechanosensors in the body of the robot. In this paper, lacings of stretchable optical fibers distributed throughout 3D printed elastomer frameworks create a cointegrated body, sensing, and communication network. This soft, functional structure can localize deformation with sub-millimeter positional accuracy (Err = 0.71 mm), and sub-Newton force resolution (~0.3 N).

Elastomeric passive transmission for autonomous force-velocity adaptation applied to 3D-printed prosthetics.

The force, speed, dexterity, and compact size required of prosthetic hands present extreme design challenges for engineers. Current prosthetics rely on high-quality motors to achieve adequate precision, force, and speed in a small enough form factor with the trade-off of high cost. We present a simple, compact, and cost-effective continuously variable transmission produced via projection stereolithography. Our transmission, which we call an elastomeric passive transmission (EPT), is a polyurethane composite cylinder that autonomously adjusts its radius based on the tension in a wire spooled around it. We integrated six of these EPTs into a three-dimensionally printed soft prosthetic hand with six active degrees of freedom. Our EPTs provided the prosthetic hand with about three times increase in grip force without compromising flexion speed. This increased performance leads to finger closing speeds of ~0.5 seconds (average radial velocity, ~180 degrees second-1) and maximum fingertip forces of ~32 newtons per finger.