Soft Robotic Sensing, Proprioception via Cable and Microfluidic Transmission

The growing demand for wearable devices, soft robotics, and tissue engineering in recent years has led to an increased effort in the field of soft materials. With the advent of personalized devices, the one-shape-fits-all manufacturing methods may soon no longer be the standard for the rapidly increasing market of soft devices. Recent findings have pushed technology and materials in the area of additive manufacturing (AM) as an alternative fabrication method for soft functional devices, taking geometrical designs and functionality to greater heights. For this reason, this review aims to highlights recent development and advances in AM processable soft materials with self-healing, shape memory, electronic, chromic or any combination of these functional properties. Furthermore, the influence of AM on the mechanical and physical properties on the functionality of these materials is expanded upon. Additionally, advances in soft devices in the fields of soft robotics, biomaterials, sensors, energy harvesters, and optoelectronics are discussed. Lastly, current challenges in AM for soft functional materials and future trends are discussed.

Based on the NASA in-Space Assembled Telescope (iSAT) study (Bulletin of the American Astronomical Society, 2019, 51, 50) which details the design and requirements for a 20-m parabolic in-space telescope, NASA Langley Research Center (LaRC) has been developing structural and robotic solutions to address the needs of building larger in-space assets. One of the structural methods studied involves stackable and collapsible modular solutions to address launch vehicle volume constraints. This solution uses a packing method that stacks struts in a dixie-cup like manner and a chemical composite bonding technique that reduces weight of the structure, adds strength, and offers the ability to de-bond the components for structural modifications. We present in this paper work towards a soft material robot end-effector, capable of suppling the manipulability, pressure, and temperature requirements for the bonding/de-bonding of these conical structural components. This work is done to investigate the feasibility of a hybrid soft robotic end-effector actuated by Twisted and Coiled Artificial Muscles (TCAMs) for in-space assembly tasks. TCAMs are a class of actuator which have garnered significant recent research interest due to their allowance for high force to weight ratio when compared to other popular methods of actuation within the field of soft robotics, and a muscle-tendon actuation design using TCAMs leads to a compact and lightweight system with controllable and tunable behavior. In addition to the muscle-tendon design, this paper also details the early investigation of an induction system for adhesive bonding/de-bonding and the sensors used for benchtop design and testing. Additionally, we discuss the viability of Robotic Operating System 2 (ROS2) and Gazebo modeling environments for soft robotics as they pertain to larger simulation efforts at LaRC. We show real world test results against simulation results for a method which divides the soft, continuous material of the end-effector into discrete links connected by spring-like joints.

In recent years, the field of soft robotics has received considerable attention due to its potential in increasing the safety of human-robot interaction. The design of soft robots possesses great challenges. For example, the longstanding challenge of co-design morphology and actuation makes designing them by hand a trial-and-error process. Earlier work presented by the authors proposes a computational design synthesis (CDS) method for the automated design of virtual, soft locomotion robot morphologies. This work extends the CDS method for morphologies with the automated co-design of actuation. Two methods are considered. In the first method, the actuation of designs is described by parametric actuation curves (PACs) that model feedforward actuation patterns. For every morphology in the design process, a set of PACs is optimized that assumes symmetric and cyclic gaits. The second method, soft actor-critic (SAC) reinforcement learning, removes this assumption as well as models feedback control for comparison. Adding PAC optimization to the CDS method is shown to improve the performance of the resulting designs and to achieve better results within less design iterations. SAC is, however, deemed less effective, due to the need for design specific problem tuning for each new morphology. The SAC experiments also show that the best found soft robot gaits are symmetric and cyclic, although this is not a constraint in the SAC problem formulation, thus verifying the assumptions made in the PAC formulation. To validate the search space modeled in the co-design CDS method, a state-of-the-art soft robot is replicated and compared.

Continuum robots play the role of end effectors in various surgical robots and endoscopic devices. While soft continuum robots (SCRs) have proven advantages such as safety and compliance, more research and development are required to enhance their capability for specific medical scenarios. This research aims at designing a soft robot, considering the concepts of geometric and kinematic similarities. The chosen application is a semi-invasive medical application known as transesophageal echocardiography (TEE). The feasibility of fabrication of a soft endoscopic device derived from the Chorda dorsalis paragon was shown empirically by producing a three-segment pneumatic SCR. The main novelties include bioinspired design, modeling, and a navigation control strategy presented as a novel algorithm to maintain a kinematic similarity between the soft robot and the rigid counterpart. The kinematic model was derived based on the method of transformation matrices, and an algorithm based on a self-organizing map (SOM) network was developed and applied to realize kinematic similarity. The simulation results indicate that the control method forces the soft robot tip to follow the path of the rigid probe within the prescribed distance error (5 mm). The solution provides a soft robot that can surrogate and succeed the traditional rigid counterpart owing to size, workspace, and kinematics.

Soft continuum robots are more and more used in various applications due to the multiple advantages offered by the properties of hyper-elastic soft materials, such as resilience, flexibility, etc. Despite all these essential properties that make the soft robots good candidates for some real-life needs (form enclosure grasping, obstacle-free navigation, etc.), controlling their shape remains challenging because of their hyper-redundancies. This letter investigates an inverse dynamics model-based shape control of soft continuum robots in the presence and absence of external efforts. To that end, modeling based on Pythagorean Hodograph (PH) curves with prescribed lengths is combined with Euler-Bernoulli modeling to reconstruct the robot shapes and then calculate the actuator inputs in the case of external efforts. Thus, a relationship between the actuator inputs and the PH control points is obtained. With the above strategy, the actuator inputs can be computed accordingly, and therefore, real-time shape control of the soft robots for various tasks becomes possible. The results of the proposed approach are validated both numerically and experimentally using two classes of soft continuum robots - a Fluidic Elastomeric Actuator (FEA), describing a 2D soft finger robot, with a single and multiple phalanges, and a 3D continuum manipulator, namely the Compact Bionic Handling Assistant (CBHA).

Dielectric elastomer actuators (DEAs) have advantageous characteristics and, therefore, their application is widespread in the field of soft robotics. Their properties can be specifically adapted by both the selection of materials and the manufacturing process. Previous research has shown that fiber reinforcement of the structure can significantly enhance the unidirectional motion. In the presented work an actuator consisting of a silicone film as the dielectric, a textile carbon-fiber-reinforced electrode and a carbon black electrode are used. The electrode based on the carbon fibers additionally serves as unidirectional stiffener. Due to this highly anisotropic textile electrode, the DEA barely contracts in fiber direction. However, the active force of the DEA during actuation can be further increased through an initial pre-stretching. The aim of this work is to investigate the influence of the pre-stretching in fiber direction on the actuator performance and the long-term stability of the pre-stretch. The active force of different actuators is recorded with uniaxial tensile tests over several deformation cycles. This enables the investigation of effects deriving both from the manufacturing process and the layer structure of the textile DEA. The acquired data are evaluated and compared to results of an analytical model. To explore the ability of the fibers to maintain the initial pre-stretching of the DEA during activation, digital image correlation as an in-situ imaging technique is applied. It could be shown shown that there is no change in width due to the anisotropy. The results of the investigations are used to control and improve the manufacturing process of the textile DEA.

The field of soft robotics is rapidly evolving, and there is a growing interest in developing soft robots with bioinspired features for use in various applications. This research presented the design and development of 3D-printed origami actuators for a soft robot with amphibious locomotion and tongue hunting capabilities. Two different types of programmable origami actuators were designed and manufactured, namely Z-shaped and twist tower actuators. In addition, two actuator variations were developed based on the Z-shaped actuator, including the pelvic fin and the coiling/uncoiling types. The Z-shaped actuators were used for the rear legs to facilitate the locomotion of the water-like frogs. Meanwhile, the twisted tower actuators were used for the rotation joints in the forelegs and for locomotion on land. The pelvic fin actuator was developed to imitate the land locomotion of the mudskipper, and the coiling/uncoiling actuator was designed for tongue hunting motion. The origami actuators and soft robot prototype were tested through a series of experiments, which showed that the robot was capable of efficiently moving in water and on land and performing tongue hunting motions. Our results demonstrate the effectiveness of these actuators in producing the desired motions and provide insights into the potential of applying 3D-printed origami actuators in the development of soft robots with bioinspired features.

Living organisms intertwine soft (e.g., muscle) and hard (e.g., bones) materials, giving them an intrinsic flexibility and resiliency often lacking in conventional rigid robots. The emerging field of soft robotics seeks to harness these same properties to create resilient machines. The nature of soft materials, however, presents considerable challenges to aspects of design, construction, and control—and up until now, the vast majority of gaits for soft robots have been hand-designed through empirical trial-and-error. This article describes an easy-to-assemble tensegrity-based soft robot capable of highly dynamic locomotive gaits and demonstrating structural and behavioral resilience in the face of physical damage. Enabling this is the use of a machine learning algorithm able to discover effective gaits with a minimal number of physical trials. These results lend further credence to soft-robotic approaches that seek to harness the interaction of complex material dynamics to generate a wealth of dynamical behaviors.

Soft robots are increasingly being explored and developed in various settings that demand safe and adaptable interactions between robots and their environments. In addition, soft robots exhibit passive compliant behavior and generate continuous deformations when engaging with the environment. This imposes challenges on achieving active, on-demand interaction force control, especially when feedback force-sensing devices are not available. Consequently, there is a need to explore new model-based force control paradigms for soft robots. In this article, we propose a (quasi-)static force control approach for soft robots based on compliance modeling, avoiding the necessity for feedback control loops or extensive training data collection. The proposed approach can deliver contact force control along three Cartesian axes when the robot is actuated into various configurations. The compliance matrix is derived from the robot configuration, which allows the calculation of desired deflection displacements needed to generate on-demand forces. The resulting force control is achieved by solving inverse kinematics problems based on these deflection displacements. The efficacy of our proposed controller is validated through experiments with both one- and two-segment pneumatic-driven soft continuum robots. The results demonstrate effective static force control performance, with mean control errors below 5% of the desired peak forces.

Dielectric elastomer actuators (DEA) have gained increased attention in the design of electrically driven soft and lightweight robotic devices. They rely on Coulomb forces for their actuation and are able to undergo large displacements as a function of the applied electrical field. Fabricating DEAs with additive manufacturing opens the way to a personalized and cost‐efficient fabrication of soft active devices. In particular, extrusion‐based techniques provide a versatile strategy to multi‐material 3D print DEAs by using electrically conductive and soft filaments with high elongation. One key aspect of DEAs is the electrode's performance, which has to provide a high electrical conductivity and a good adhesion to the sandwiched membrane. Herein, selected 3D printing parameters were varied to optimize the electrode infill density and electrode infill direction. Mechanical and electrical properties, as well as actuation performance, of fully 3D‐printed DEA demonstrators were studied comprehensively. With the optimized parameter set, a maximum DEA displacement of 91% was achieved with respect to the free length of the actuator. This is comparable to the performance of non‐3D‐printed DEAs and demonstrates the potential of 3D printing for the production of DEAs undergoing large deformations while leveraging the design and manufacturing freedom of multi‐material 3D printing.

The goal of the robotics industry has been to build devices that mirror the remarkable powers of the human body ever since it was founded. In an attempt to emulate the compliance and deformability of genuine biological tissue, efforts have been made to construct actuators and electronics out of elastomers, textiles, and other soft materials as early as the 1940s. Since then, the field of soft robotics has advanced extraordinarily, with recent work concentrating on actuation, sensing, and application. In this review, recent advancement in the soft robotically field from the perspective of soft actuators, soft material, and biomedical application is highlighted. Soft actuators had gone decent development along with different advantages and setbacks. The need for chemistry and material science has been increasingly large in recent years and the material for 4D printing manufacturing is a major focus. Biomedical applications can be advanced by introducing soft robotics combined with existing technology. The biocompatibility issue is a major challenge to be faced, and perspectives on such regard will be discussed.

The current boom in soft robotics development has spurred extensive research into these flexible, deformable, and adaptive robotic systems. However, the unique characteristics of soft materials, such as non-linearity and hysteresis, present challenges in modeling, calibration, and control, laying the foundation for a compelling exploration based on finite element analysis (FEA), machine learning (ML), and digital twins (DT). Therefore, in this review paper, we present a comprehensive exploration of the evolving field of soft robots, tracing their historical origins and current status. We explore the transformative potential of FEA and ML in the field of soft robotics, covering material selection, structural design, sensing, control, and actuation. In addition, we introduce the concept of DT for soft robots and discuss its technical approaches and integration in remote operation, training, predictive maintenance, and health monitoring. We address the challenges facing the field, map out future directions, and finally conclude the important role that FEA, ML, and DT play in shaping the future of soft robots.

SummaryA design and manufacturing method is described for creating a motor tendon–actuated soft foam robot. The method uses a castable, light, and easily compressible open-cell polyurethane foam, producing a structure capable of large (~70% strain) deformations while requiring low torques to operate (<0.2 N·m). The soft robot can change shape, by compressing and folding, allowing for complex locomotion with only two actuators. Achievable motions include forward locomotion at 13 mm/s (4.3% of body length per second), turning at 9◦/s, and end-over-end flipping. Hard components, such as motors, are loosely sutured into cavities after molding. This reduces unwanted stiffening of the soft body. This work is the first demonstration of a soft open-cell foam robot locomoting with motor tendon actuators. The manufacturing method is rapid (~30 min per mold), inexpensive (under $3 per robot for the structural foam), and flexible, and will allow a variety of soft foam robotic devices to be produced.

Today's use of large-scale industrial robots is enabling extraordinary achievement on the assembly line, but these robots remain isolated from the humans on the factory floor because they are very powerful, and thus dangerous to be around. In contrast, the soft robotics research community has proposed soft robots that are safe for human environments. The current state of the art enables the creation of small-scale soft robotic devices. In this article we address the gap between small-scale soft robots and the need for human-sized safe robots by introducing a new soft robotic module and multiple human-scale robot configurations based on this module. We tackle large-scale soft robots by presenting a modular and reconfigurable soft robotic platform that can be used to build fully functional and untethered meter-scale soft robots. These findings indicate that a new wave of human-scale soft robots can be an alternative to classic rigid-bodied robots in tasks and environments where humans and machines can work side by side with capabilities that include, but are not limited to, autonomous legged locomotion and grasping.

The soft robotics field is moving toward increasingly complex and integrated systems, which will contain interfaces between soft components and other soft, compliant, and/or rigid components. Although many soft interfaces leverage adhesion, soft robot designers currently have limited information for selecting appropriate materials and fabrication techniques. Through experimental testing, this article characterizes how the substrate material and bonding process influence the performance of soft-soft silicone [i.e., polydimethylsiloxane-based interfaces], provides a framework for approaching this analysis, and contextualizes the data to provide initial insights into material selection for soft-soft interfaces by showing how the data could be used to guide design decisions. Specifically, this article characterizes five addition-curing silicone rubbers and five bonding processes, and it defines performance using quantitative metrics relating to desirable qualitative behaviors: toughness (adhesive fracture energy), flexibility (maximum localized strain during peeling), and strength (ratio of initial-to-average force and magnitude of initial peak peel force). Together, the substrate material and bonding method jointly determine the failure behavior of soft-soft silicone interfaces, influencing both the achievable performance (toughness, strength, flexibility) and characteristic failure modes (adhesive, cohesive, mixed-mode). Understanding characteristic failure modes can inform design strategies to mitigate interfacial failure, enabling higher-capability soft robots with improved operating loads and component lifetimes.