Soft robotics promises developments in the research areas of safety, bio-mimicry, manipulation, human-robot interaction, and alternative locomotion techniques. The research presented here is directed towards developing an improved, low-cost, and open-source method for soft robotic control using electrorheological fluids in compact, 3D-printed electroactive hydraulic valves. We construct high-pressure electrorheological valves and deformable actuators using only commercially available materials and accessible fabrication methods. The printed valves were characterized with industrial-grade electrorheological fluid (RheOil 3.0), but the design is generalizable to other electrorheological fluids. Valve performance was shown to be an improvement over comparable work with demonstrated higher yield pressures at lower voltages (up to 230 kPa), larger flow rates (up to 15 ml/min) and lower response times (1 to 3 seconds, depending on design). The resulting valve and actuator systems enable future novel applications of electrorheological fluid-based control and hydraulics in soft robotics and other disciplines.

In this work, we present a novel approach for modeling, and classifying between, the system load states introduced when constructing staged soft arm configurations. Through a two stage approach: (1) an LSTM calibration routine is used to identify the current load state then (2) a control input generation step combines a generalized quasistatic model with the learned load model. Our experiments show that accounting for system load allows us to more accurately control tapered arm configurations. We analyze the performance of our method using soft robotic actuators and show it is capable of classifying between different arm configurations at a rate greater than 95%. Additionally, our method is capable of reducing the end-effector error of quasistatic model only control to within 1 cm of our controller baseline.

For the state-of-the-art of soft robotics, the current actuation mechanisms cannot produce shear forces, neither are the current stiffening mechanisms adaptive to various deformations. Consequently, the soft robots gain strength at the price of losing flexibility. To fill this gap, we proposed a new mechanism based on the muscle arrangements and incompressible property identified in biological hydrostatic skeletons. Beside longitudinal and transverse muscles, the proposed mechanism includes the oblique arrangement which is proved to play an indispensable role of producing shear forces. The effectiveness of the new mechanism is demonstrated through a benchmark problem - carrying a distributed load at the initial horizontal configuration, thus indicating an improved direction to realise shape-independent load-carrying capability of soft robotics. Furthermore, the proposed mechanism may explain how elephants coordinate the two contradicting properties, strength and flexibility, during their trunk manipulations.

Compliant materials used to create soft robots can better replicate biological structures than typical rigid materials. We can look to nature for developing locomotion strategies for these soft-bodied robots. In this work, snakes were used as inspiration to create an inextensible, soft robot which was used as a platform to test gaits in terrain composed of granular media ranging from fine sand to stone. Snakes vary the speed and amplitude of the traveling wave used in lateral undulation to navigate different environments. We used these gait parameters to develop and test a set of custom gaits that varied the phase offset of the sequence of waves as well as using the best performing gait to test how the amplitude of the wave effects locomotion over the selected terrains. These tests provide preliminary evidence that altering these parameters effects the robot's ability to traverse different terrains. The developed robot is also tested in environments specific to applications for snake robots to show how a soft snake robot can be potentially more effective in these environment. The highest performing gait-curvature combination was the half-activation gait (where the back actuator was activated half as long as the front)with a 135° swept angle. It reached a velocity of 2.2 mm/s or 0.011 body-lengths/s on paper, which was the best performing terrain.

The ability to measure human hand motions and interaction forces is critical to improving our understanding of manual gesturing and grasp mechanics. This knowledge serves as a basis for developing better tools for human skill training and rehabilitation, exploring more effective methods of designing and controlling robotic hands, and creating more sophisticated human-computer interaction devices which use complex hand motions as control inputs. This paper presents work on the design, fabrication, and experimental validation of a soft sensor-embedded glove which measures both hand motion and contact pressures during human gesturing and manipulation tasks. We design an array of liquid-metal embedded elastomer sensors to measure up to hundreds of Newtons of interaction forces across the human palm during manipulation tasks and to measure skin strains across phalangeal and carpal joints for joint motion tracking. The elastomeric sensors provide the mechanical compliance necessary to accommodate anatomical variations and permit a normal range of hand motion. We explore methods of assembling this soft sensor glove from modular, individually fabricated pressure and strain sensors and develop design guidelines for their mechanical integration. Experimental validation of a soft finger glove prototype demonstrates the sensitivity range of the designed sensors and the mechanical robustness of the proposed assembly method, and provides a basis for the production of a complete soft sensor glove from inexpensive modular sensor components.

Motion sensing has played an important role in the study of human biomechanics as well as the entertainment industry. Although existing technologies, such as optical or inertial based motion capture systems, have relatively high accuracy in detecting body motions, they still have inherent limitations with regards to mobility and wearability. In this paper, we present a soft motion sensing suit for measuring lower extremity joint motion. The sensing suit prototype includes a pair of elastic tights and three hyperelastic strain sensors. The strain sensors are made of silicone elastomer with embedded microchannels filled with conductive liquid. To form a sensing suit, these sensors are attached at the hip, knee, and ankle areas to measure the joint angles in the sagittal plane. The prototype motion sensing suit has significant potential as an autonomous system that can be worn by individuals during many activities outside the laboratory, from running to rock climbing. In this study we characterize the hyperelastic sensors in isolation to determine their mechanical and electrical responses to strain, and then demonstrate the sensing capability of the integrated suit in comparison with a ground truth optical motion capture system. Using simple calibration techniques, we can accurately track joint angles and gait phase. Our efforts result in a calculated trade off: with a maximum error less than 8%, the sensing suit does not track joints as accurately as optical motion capture, but its wearability means that it is not constrained to use only in a lab.

This paper presents analysis and results for a small and agile wall climbing robot's ability to regain lost adhesion due to degradation of dry fibrillar adhesives. To regain the lost adhesion, two feet are set to the surface and the robot performs a rocking motion on the side where the adhesion has dropped below a safety threshold. The rocking motion applies normal forces to preload the front and rear feet without letting the other foot detach from the surface by alternating the direction of the motor and only allowing small rotation of the leg. Experimental results show that the rocking motion is successful in regaining lost adhesion while using dry fibrillar adhesives on a smooth, vertical acrylic surface. The performance of the fibers over time limits the adhesion that can possibly be mechanically regained and as a result the fibers are over-designed, which gives rise to the need for a power efficient peeling mechanism. The peeling mechanism uses a conditionally locked ankle, implemented with magnets, and a slot to allow the axle to change a pulling force normal to the surface to be a pulling force perpendicular to the surface, which peels the fibers using the uneven loading. Experimental results illustrate that a passive peeling mechanism is successful in reducing the required power to peel. The presented advancements can be applied to other climbing robots using adhesives to allow for safer, more efficient climbing.