Fiber Embroidery of Self-Sensing Soft Actuators

Single-vat single-cure grayscale digital light processing 3D printing of materials with large property difference and high stretchability

Multimaterial additive manufacturing has important applications in various emerging fields. However, it is very challenging due to material and printing technology limitations. Here, we present a resin design strategy that can be used for single-vat single-cure grayscale digital light processing (g-DLP) 3D printing where light intensity can locally control the conversion of monomers to form from a highly stretchable soft organogel to a stiff thermoset within in a single layer of printing. The high modulus contrast and high stretchability can be realized simultaneously in a monolithic structure at a high printing speed (z-direction height 1 mm/min). We further demonstrate that the capability can enable previously unachievable or hard-to-achieve 3D printed structures for biomimetic designs, inflatable soft robots and actuators, and soft stretchable electronics. This resin design strategy thus provides a material solution in multimaterial additive manufacture for a variety of emerging applications.

Neural Network-Based Active Load-Sensing Scheme and Stiffness Adjustment for Pneumatic Soft Actuators for Minimally Invasive Surgery Support

To provide a stable surgical view in Minimally Invasive Surgery (MIS), it is necessary for a flexible endoscope applied in MIS to have adjustable stiffness to resist different external loads from surrounding organs and tissues. Pneumatic soft actuators are expected to fulfill this role, since they could feed the endoscope with an internal access channel and adjust their stiffness via an antagonistic mechanism. For that purpose, it is essential to estimate the external load. In this study, we proposed a neural network (NN)-based active load-sensing scheme and stiffness adjustment for a soft actuator for MIS support with antagonistic chambers for three degrees of freedom (DoFs) of control. To deal with the influence of the nonlinearity of the soft actuating system and uncertainty of the interaction between the soft actuator and its environment, an environment exploration strategy was studied for improving the robustness of sensing. Moreover, a NN-based inverse dynamics model for controlling the stiffness of the soft actuator with different flexible endoscopes was proposed too. The results showed that the exploration strategy with different sequence lengths improved the estimation accuracy of external loads in different conditions. The proposed method for external load exploration and inverse dynamics model could be used for in-depth studies of stiffness control of soft actuators for MIS support.

A Learning-Based Approach to Sensorize Soft Robots

Soft actuators and their sensors have always been separate entities with two distinct roles. The omnidirectional compliance of soft robots thus means that multiple sensors have to be used to sense different modalities in the respective planes of motion. With the recent emergence of self-sensing actuators, the two roles have gradually converged to simplify sensing requirements. Self-sensing typically involves embedding a conductive sensing element into the soft actuator and provides multiple state information along the continuum. However, most of these self-sensing actuators are fabricated through manual methods, which results in inconsistent sensing performance. Soft material compliance also imply that both actuator and sensor exhibit nonlinear behaviors during actuation, making sensing more complex. In this regard, machine learning has shown promise in characterizing the nonlinear behavior of soft sensors. Beyond characterization, we show that applying machine learning to soft actuators eliminates the need to implant a sensing element to achieve self-sensing. Fabrication is done using 3D printing, thus ensuring that sensing performance is consistent across the actuators. In addition, our proposed technique is able to estimate the bending curvature of a soft continuum actuator and the external forces applied to the tip of the actuator in real time. Our methodology is generalizable and aims to provide a novel way of multimodal sensing for soft robots across a variety of applications.

Compact Multilayer Extension Actuators for Reconfigurable Soft Robots

Soft robotic pneumatic actuators generally excel in the specific application they were designed for but lack the versatility to be redeployed to other applications. This study presents a novel and versatile soft compact multilayer extension actuator (MEA) to overcome this limitation. We use the MEA linear output in different hybrid configurations to achieve this versatility. The unique design and fabrication of the MEA allow for a compact elastomeric actuator with innate tension, capable of reverting to its initial state without the need for external stimulus. The MEA is made from alternating elastomers with different Young's modulus, bestowing the MEA with high durability, force, and extension capabilities. In addition, the MEA is lightweight at 4 g, capable of a high force-to-weight ratio of 1000 and an extension ratio of 525%. We also explored varying the MEA parameters, such as its material and dimension, which further enhance its properties. Subsequently, we showed four different design configurations encompassing the MEA to produce four basic motions, that is, push, pull, bend, and twist. Finally, we demonstrated three possible hybrid configurations for manipulation, locomotion, and assistive applications that highlight the versatility, manipulability, and modularity of the MEA.

Controlling the deformation space of soft membranes using fiber reinforcement

Recent efforts in soft-body control have been hindered by the infinite dimensionality of soft bodies. Without restricting the deformation space of soft bodies to desired degrees of freedom, it is difficult, if not impossible, to guarantee that the soft body will remain constrained within a desired operating range. In this article, we present novel modeling and fabrication techniques for leveraging the reorientation of fiber arrays in soft bodies to restrict their deformation space to a critical case. Implementing this fiber reinforcement introduces unique challenges, especially in complex configurations. To address these challenges, we present a geometric technique for modeling fiber reinforcement on smooth elastomeric surfaces and a two-stage molding process to embed the fiber patterns dictated by that technique into elastomer membranes. The variable material properties afforded by fiber reinforcement are demonstrated with the canonical case of a soft, circular membrane reinforced with an embedded, intersecting fiber pattern such that it deforms into a prescribed hemispherical geometry when inflated. It remains constrained to that configuration, even with an additional increase in internal pressure. Furthermore, we show that the fiber-reinforced membrane is capable of maintaining its hemispherical shape under a load, and we present a practical application for the membrane by using it to control the buoyancy of a bioinspired autonomous underwater robot developed in our lab. An additional experiment on a circular membrane that inflates to a conical frustum is presented to provide additional validation of the versatility of the proposed model and fabrication techniques.

Design and Computational Modeling of Fabric Soft Pneumatic Actuators for Wearable Assistive Devices

Assistive wearable soft robotic systems have recently made a surge in the field of biomedical robotics, as soft materials allow safe and transparent interactions between the users and devices. A recent interest in the field of soft pneumatic actuators (SPAs) has been the introduction of a new class of actuators called fabric soft pneumatic actuators (FSPAs). These actuators exploit the unique capabilities of different woven and knit textiles, including zero initial stiffness, full collapsibility, high power-to-weight ratio, puncture resistant, and high stretchability. By using 2D manufacturing methods we are able to create actuators that can extend, contract, twist, bend, and perform a combination of these motions in 3D space. This paper presents a comprehensive simulation and design tool for various types of FSPAs using finite element method (FEM) models. The FEM models are developed and experimentally validated, in order to capture the complex non-linear behavior of individual actuators optimized for free displacement and blocked force, applicable for wearable assistive tasks.

Soft Robotics

In Nature, the adaptability of many organisms and their capability to survive in challenging and dynamically changing environments are closely linked to their characteristics and the morphology of their body parts [...].

Force-Amplified Soft Electromagnetic Actuators

Electrically-driven direct current (DC) motors are the core component of conventional robots thanks to the ease of computer control and high torque for their size. However, DC motors are often manually attached and soldered into robotic assemblies, and they are not flexible. For soft robotics, researchers have looked to new, compliant materials that are compatible with 3-D printing or other automated assembly methods. In this work we use a computer-controlled embroidery machine to create flat motor windings in flexible fabrics. We model their electromagnetic fields and present them as linear actuators that move a permanent magnet attached to a cable. The fabrication method puts some constraints on the coil design, which are discussed. However, the planar nature of the embroidered sheets enables the designer to use laminar fabrication methods, such as stacking or layering into parts, during 3-D printing. The soft motor windings produced static holding forces of up to 0.25 N and could lift a 0.3 g mass several cm using direct drive. A 3-D printed mechanical amplifier with two stages was able to quadruple the lifting mass, reducing the travel by a factor of 4. Machine embroidery-installed cables and motor coils could lead to “bolts and nuts free” fabrication of thin, electrically-driven cable actuators.