Shielded soft force sensors.

Force and strain sensors made of soft materials enable robots to interact intelligently with their surroundings. Capacitive sensing is widely adopted thanks to its low power consumption, fast response, and facile fabrication. Capacitive sensors are, however, susceptible to electromagnetic interference and proximity effects and thus require electrical shielding. Shielding has not been previously implemented in soft capacitive sensors due to the parasitic capacitance between the shield and sensing electrodes, which changes when the sensor is deformed. We address this crucial challenge by patterning the central sensing elastomer layer to control its compressibility. One design uses an ultrasoft silicone foam, and the other includes microchannels filled with liquid metal and air. The force resolution is sub-mN both in normal and shear directions, yet the sensor withstands large forces (>20 N), demonstrating a wide dynamic range. Performance is unaffected by nearby high DC and AC electric fields and even electric sparks.

Toward Stimuli-Responsive Soft Robots with 3D Printed Self-Healing Konjac Glucomannan Gels.

Significant progress in fabricating new multifunctional soft materials and the advances of additive manufacturing technologies have given birth to a new generation of soft robots with complex capabilities, such as crawling, swimming, jumping, gripping, and releasing. Within this vast array of responsive soft materials, hydrogels receive considerable attention due to their fascinating properties, including biodegradable, self-healing, stimuli-responsive, and large volume transformation. Konjac glucomannan (KGM) is an edible polysaccharide that forms a pH-responsive, self-healing hydrogel when crosslinked with borax, and it is the focus of this study. A novel KGM-Borax ink for three-dimensional (3D) printing of free-form structures and soft robots at room temperature is presented. A complete process from ink preparation to the fabrication of a completely cross-linked part is demonstrated. Print setting parameters, rheological properties of the ink and crosslinked gels were characterized. Print quality was found to be consistent across a wide range of print settings. The minimum line width achieved is 650 µm. Tensile testing was carried out to validate the self-healing capability of the KGM-Borax gel. Results show that KGM-Borax has a high self-healing efficiency of 98%. Self-healing underwater was also demonstrated, a rarity for crosslinked gels. The means to form complex structures via 3D printing, reacting to environmental stimuli and the resilience against damage, make this new KGM-Borax gel a promising candidate for the fabrication of the next generation of soft robots.

Quantifying Dynamic Shapes in Soft Morphologies.

Soft materials are driving the development of a new generation of robots that are intelligent, versatile, and adept at overcoming uncertainties in their everyday operation. The resulting soft robots are compliant and deform readily to change shape. In contrast to rigid-bodied robots, the shape of soft robots cannot be described easily. A numerical description is needed to enable the understanding of key features of shape and how they change as the soft body deforms. It can also quantify similarity between shapes. In this article, we use a method based on elliptic Fourier descriptors to describe soft deformable morphologies. We perform eigenshape analysis on the descriptors to extract key features that change during the motion of soft robots, showing the first analysis of this type on dynamic systems. We apply the method to both biological and soft robotic systems, which include the movement of a passive tentacle, the crawling movement of two species of caterpillar (Manduca sexta and Sphacelodes sp.), the motion of body segments in the M. sexta, and a comparison of the motion of a soft robot with that of a microorganism (euglenoid, Eutreptiella sp.). In the case of the tentacle, we show that the method captures differences in movement in varied media. In the caterpillars, the method illuminates a prominent feature of crawling, the extension of the terminal proleg. In the comparison between the robot and euglenoids, our method quantifies the similarity in shape to ∼85%. Furthermore, we present a possible method of extending the analysis to three-dimensional shapes.

EuMoBot: replicating euglenoid movement in a soft robot.

Swimming is employed as a form of locomotion by many organisms in nature across a wide range of scales. Varied strategies of shape change are employed to achieve fluidic propulsion at different scales due to changes in hydrodynamics. In the case of microorganisms, the small mass, low Reynolds number and dominance of viscous forces in the medium, requires a change in shape that is non-invariant under time reversal to achieve movement. The Euglena family of unicellular flagellates evolved a characteristic type of locomotion called euglenoid movement to overcome this challenge, wherein the body undergoes a giant change in shape. It is believed that these large deformations enable the organism to move through viscous fluids and tiny spaces. The ability to drastically change the shape of the body is particularly attractive in robots designed to move through constrained spaces and cluttered environments such as through the human body for invasive medical procedures or through collapsed rubble in search of survivors. Inspired by the euglenoids, we present the design of EuMoBot, a multi-segment soft robot that replicates large body deformations to achieve locomotion. Two robots have been fabricated at different sizes operating with a constant internal volume, which exploit hyperelasticity of fluid-filled elastomeric chambers to replicate the motion of euglenoids. The smaller robot moves at a speed of [Formula: see text] body lengths per cycle (20 mm min-1 or 2.2 cycles min-1) while the larger one attains a speed of [Formula: see text] body lengths per cycle (4.5 mm min-1 or 0.4 cycles min-1). We show the potential for biomimetic soft robots employing shape change to both replicate biological motion and act as a tool for studying it. In addition, we present a quantitative method based on elliptic Fourier descriptors to characterize and compare the shape of the robot with that of its biological counterpart. Our results show a similarity in shape of 85% and indicate that this method can be applied to understand the evolution of shape in other nonlinear, dynamic soft robots where a model for the shape does not exist.