Characterization of Temperature and Humidity Dependence in Soft Elastomer Behavior

Soft robots are predicted to operate well in unstructured environments due to their resilience to impacts, embodied intelligence, and potential ability to adapt to uncertain circumstances. Soft robots are of further interest for space and extraterrestrial missions, owing to their lightweight and compressible construction. Most soft robots in the literature to-date are made of elastomer bodies. However, limited data are available on the material characteristics of commonly used elastomers in extreme environments. In this study, we characterize four commonly used elastomers in the soft robotics literature-EcoFlex 00-30, Dragon Skin 10, Smooth-Sil 950, and Sylgard 184-in a temperature range of -40°C to 80°C and humidity range of 5-95% RH. We perform pull-to-failure, stiffness, and stress-relaxation tests. Furthermore, we perform a case study on soft elastomers used in stretchable capacitive sensors to evaluate the implications of the constituent material behavior on component performance. We find that all elastomers show temperature-dependent behavior, with typical stiffening of the material and a lower strain at failure with increasing temperature. The stress-relaxation response to temperature depends on the type of elastomer. Limited material effects are observed in response to different humidity conditions. The mechanical properties of the capacitive sensors are only dependent on temperature, but the measured capacitance shows changes related to both humidity and temperature changes, indicating that component-specific properties need to be considered in tandem with the mechanical design. This study provides essential insights into elastomer behavior for the design and successful operation of soft robots in varied environmental conditions

Strain Sensor-Embedded Soft Pneumatic Actuators for Extension and Bending Feedback

For soft robots to leave the lab and enter unstructured environments, proprioception is required to understand how interactions in the field affect the soft structure. In this work, we present sensor-embedded soft pneumatic actuators (sSPA) that can observe both extension and bending. The sensors are strain sensitive capacitors, which are bonded to the interior of fiber-reinforced extension actuators on opposing faces. This construction allows extension and bending to be measured by calculating the mean and difference in sensor responses, respectively. The sSPAs are bonded together to form a flat fascicle to increase the force output and prevent buckling under load, and are robust to component failure by incorporating redundancy. In this paper, we discuss the fabrication of the sensors and their subsequent integration into the actuators. We also report the work capacity and sensor. response of the sSPA fascicles under extension, bending, and the combination of both modes of deformation. The sensor- embedded soft pneumatic actuators presented here will advance the field of soft robotics by enabling closed-loop control of soft robots

Universal Mechanical Polycomputation in Granular Matter

Unconventional computing devices are increasingly of interest as they can operate in environments hostile to silicon-based electronics, or compute in ways that traditional electronics cannot. Mechanical computers, wherein information processing is a material property emerging from the interaction of components with the environment, are one such class of devices. This information processing can be manifested in various physical substrates, one of which is granular matter. In a granular assembly, vibration can be treated as the information-bearing mode. This can be exploited to realize "polycomputing": materials can be evolved such that a single grain within them can report the result of multiple logical operations simultaneously at different frequencies, without recourse to quantum effects. Here, we demonstrate the evolution of a material in which one grain acts simultaneously as two different NAND gates at two different frequencies. NAND gates are of interest as any logical operations can be built from them. Moreover, they are nonlinear thus demonstrating a step toward general-purpose, computationally dense mechanical computers. Polycomputation was found to be distributed across each evolved material, suggesting the material's robustness. With recent advances in material sciences, hardware realization of these materials may eventually provide devices that challenge the computational density of traditional computers.Comment: Accepted to the Genetic and Evolutionary Computation Conference 2023 (GECCO '23

An Any-Resolution Distributed Pressure Localization Scheme Using a Capacitive Soft Sensor Skin

We present a method to determine the location of an applied pressure on a large area, monolithic silicone based capacitive sensor. In contrast to pressure sensor arrays composed of n x n discrete sensors, we utilize a single sensor body with a single instrumentation interface to detect n pixels. We interrogate the capacitive sensor at different frequencies, thus modulating the effective length of the sensor. These interrogation frequencies are governed by the sensor’s total capacitance, resistance, and desired spatial resolution of the sensor. We developed an analytical model to calculate the frequency response at different length segments of the sensor and used the results to determine the interrogation frequencies for experimental studies. We performed experimental tests on a 1 x n sensor strip and an n x n sensor sheet and showed that we could attain greater than 90% accuracy in predicting the location of the applied pressure using a model generated by a multi-class kernel support vector machine. This approach towards distributed localization of point pressures greatly reduces the hardware complexity in comparison to discrete sensor arrays and increases the physical robustness of the system

Designing the pressure-dependent shear modulus using tessellated granular metamaterials

Jammed packings of granular materials display complex mechanical response. For example, the ensemble-averaged shear modulus $\left\langle G \right\rangle$ increases as a power-law in pressure $p$ for static packings of soft spherical particles that can rearrange during compression. We seek to design granular materials with shear moduli that can either increase {\it or} decrease with pressure without particle rearrangements even in the large-system limit. To do this, we construct {\it tessellated} granular metamaterials by joining multiple particle-filled cells together. We focus on cells that contain a small number of bidisperse disks in two dimensions. We first study the mechanical properties of individual disk-filled cells with three types of boundaries: periodic boundary conditions (PBC), fixed-length walls (FXW), and flexible walls (FLW). Hypostatic jammed packings are found for cells with FLW, but not in cells with PBC and FXW, and they are stabilized by quartic modes of the dynamical matrix. The shear modulus of a single cell depends linearly on $p$. We find that the slope of the shear modulus with pressure, $\lambda_c < 0$ for all packings in single cells with PBC where the number of particles per cell $N \ge 6$. In contrast, single cells with FXW and FLW can possess $\lambda_c > 0$, as well as $\lambda_c < 0$, for $N \le 16$. We show that we can force the mechanical properties of multi-cell granular metamaterials to possess those of single cells by constraining the endpoints of the outer walls and enforcing an affine shear response. These studies demonstrate that tessellated granular metamaterials provide a novel platform for the design of soft materials with specified mechanical properties

Real2Sim2Real Transfer for Control of Cable-driven Robots via a Differentiable Physics Engine

Tensegrity robots, composed of rigid rods and flexible cables, exhibit high strength-to-weight ratios and extreme deformations, enabling them to navigate unstructured terrain and even survive harsh impacts. However, they are hard to control due to their high dimensionality, complex dynamics, and coupled architecture. Physics-based simulation is one avenue for developing locomotion policies that can then be transferred to real robots, but modeling tensegrity robots is a complex task, so simulations experience a substantial sim2real gap. To address this issue, this paper describes a Real2Sim2Real strategy for tensegrity robots. This strategy is based on a differential physics engine that can be trained given limited data from a real robot (i.e. offline measurements and one random trajectory) and achieve a high enough accuracy to discover transferable locomotion policies. Beyond the overall pipeline, key contributions of this work include computing non-zero gradients at contact points, a loss function, and a trajectory segmentation technique that avoid conflicts in gradient evaluation during training. The proposed pipeline is demonstrated and evaluated on a real 3-bar tensegrity robot.Comment: Submitted to ICRA202

Robotic Skins That Turn Inanimate Objects Into Multifunctional Robots

Presented on September 12, 2018 from 12:15 p.m.-1:15 p.m. in the Marcus Nanotechnology Building, Rooms 1116-1118, Georgia Tech.Rebecca Kramer-Bottiglio is an assistant professor of Mechanical Engineering and Materials Science at Yale University. She earned a B.S. from the Johns Hopkins University, an M.S. from U.C. Berkeley, and a Ph.D. from Harvard University. Prior to joining the faculty at Yale, she was an assistant professor of Mechanical Engineering at Purdue University for four years. Kramer-Bottiglio currently serves as an associate editor of Frontiers in Robotics and AI: Soft Robotics, IEEE Robotics and Automation Letters, and IOPscience Multifunctional Materials. She is the recipient of the NSF CAREER Award, the NASA Early Career Faculty Award, the AFOSR Young Investigator Award, the ONR Young Investigator Award, and was named to Forbes’ 2015 “30 under 30 list.”Runtime: 61:26 minutesRobots generally excel at specific tasks in structured environments, but lack the versatility and adaptability required to interact with and locomote within the natural world. To increase versatility in robot design, my research group is developing robotic skins that can wrap around arbitrary deformable objects to induce the desired motions and deformations. Robotic skins integrate actuation and sensing into a single conformable material, and may be applied to, removed from, and transferred between different objects to create a multitude of controllable robots with different functions to accommodate the demands of different environments. We have shown that attaching the same robotic skin to a deformable object in different ways, or to different objects, leads to unique motions. Further, we have shown that combining multiple robotic skins enables complex motions and functions. During this talk, I will demonstrate the versatility of this soft robot design approach by showing robotic skins in a wide range of applications—including manipulation tasks, locomotion, and wearables—using the same 2D robotic skins reconfigured on the surface of various 3D soft, inanimate objects