Spikebot: A Multigait Tensegrity Robot with Linearly Extending Struts.

Numerous recent research efforts have leveraged networks of rigid struts and flexible cables, called tensegrity structures, to create highly resilient and packable mobile robots. However, the locomotion of existing tensegrity robots is limited in terms of both speed and number of distinct locomotion modes, restricting the environments that a robot is capable of exploring. In this study, we present a tensegrity robot inspired by the volumetric expansion of Tetraodontidae. The robot, referred to herein as Spikebot, employs pneumatically actuated rigid struts to expand its global structure and produce diverse gaits. Spikebot is composed of linear actuators that dually serve as rigid struts linked by elastic cables for stability. The linearly actuating struts can selectively protrude to initiate thrust- and instability-driven locomotion primitives. Such motion primitives allow Spikebot to reliably locomote, achieving rolling, lifting, and jumping. To highlight Spikebot's potential for robotic exploration, we demonstrate how it achieves multi-dimensional locomotion over varied terrestrial conditions.

Are Liquid Metals Bulk Conductors?

Stretchable electronics have potential in wide-reaching applications including wearables, personal health monitoring, and soft robotics. Many recent advances in stretchable electronics leverage liquid metals, particularly eutectic gallium-indium (EGaIn). A variety of EGaIn electromechanical behaviors have been reported, ranging from bulk conductor responses to effectively strain-insensitive responses. However, numerous measurement techniques have been used throughout the literature, making it difficult to directly compare the various proposed formulations. Here, the electromechanical responses of EGaIn found in the literature is reviewed and pure EGaIn is investigated using three electrical resistance measurement techniques: four point probe, two point probe, and Wheatstone bridge. The results indicate substantial differences in measured electromechanical behavior between the three methods, which can largely be accounted for by correcting for a fixed offset corresponding to the resistances of various parts of the measurement circuits. Yet, even accounting for several of these sources of experimental error, the average relative change in resistance of EGaIn is found to be lower than that predicted by the commonly used bulk conductor assumption, referred to as Pouillet's law. Building upon recent theories proposed in the literature, possible explanations for the discrepancies are discussed. Finally, suggestions are provided on experimental design to enable reproducible and interpretable research.

Tensegrity Robotics.

Numerous recent advances in robotics have been inspired by the biological principle of tensile integrity-or "tensegrity"-to achieve remarkable feats of dexterity and resilience. Tensegrity robots contain compliant networks of rigid struts and soft cables, allowing them to change their shape by adjusting their internal tension. Local rigidity along the struts provides support to carry electronics and scientific payloads, while global compliance enabled by the flexible interconnections of struts and cables allows a tensegrity to distribute impacts and prevent damage. Numerous techniques have been proposed for designing and simulating tensegrity robots, giving rise to a wide range of locomotion modes, including rolling, vibrating, hopping, and crawling. In this study, we review progress in the burgeoning field of tensegrity robotics, highlighting several emerging challenges, including automated design, state sensing, and kinodynamic motion planning.

Reprogrammable soft actuation and shape-shifting via tensile jamming.

The emerging generation of robots composed of soft materials strives to match biological motor adaptation skills via shape-shifting. Soft robots often harness volumetric expansion directed by strain limiters to deform in complex ways. Traditionally, strain limiters have been inert materials embedded within a system to prescribe a single deformation. Under changing task demands, a fixed deformation mode limits adaptability. Recent technologies for on-demand reprogrammable deformation of soft bodies, including thermally activated variable stiffness materials and jamming systems, presently suffer from long actuation times or introduce unwanted bending stiffness. We present fibers that switch tensile stiffness via jamming of segmented elastic fibrils. When jammed, tensile stiffness increases more than 20× in less than 0.1 s, but bending stiffness increases only 2×. When adhered to an inflating body, jamming fibers locally limit surface tensile strains, unlocking myriad programmable deformations. The proposed jamming technology is scalable, enabling adaptive behaviors in emerging robotic materials that interact with unstructured environments.

Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses.

Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed in a low-throughput manner, such as on Petri plates. Additionally, the BR pathway affects drought responses, but drought experiments are time consuming and difficult to control. To mitigate these issues and increase throughput, we developed the Robotic Assay for Drought (RoAD) system to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a robotic arm, a rover, a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction to accurately measure traits including plant area, plant volume, leaf length, and leaf width. We then applied machine learning algorithms that utilize the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought-treated, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants in 3D, providing insights into the BR-mediated control of plant growth and stress responses.

Printed and Laser-Activated Liquid Metal-Elastomer Conductors Enabled by Ethanol/PDMS/Liquid Metal Double Emulsions.

Soft electronic systems require stretchable, printable conductors for applications in soft robotics, wearable technologies, and human-machine interfaces. Gallium-based room-temperature liquid metals (LMs) have emerged as promising candidates, and recent liquid metal-embedded elastomers (LMEEs) have demonstrated favorable properties such as stable conductivity during strain, cyclic durability, and patternability. Here, we present an ethanol/polydimethylsiloxane/liquid metal (EtOH/PDMS/LM) double emulsion ink that enables a fast, scalable method to print LM conductors with high conductivity (7.7 × 105 S m-1), small resistance change when strained, and consistent cyclic performance (over 10,000 cycles). EtOH, the carrier solvent, is leveraged for its low viscosity to print the ink onto silicone substrates. PDMS resides at the EtOH/LM interface and cures upon deposition and EtOH evaporation, consequently bonding the LM particles to each other and to the silicone substrate. The printed PDMS-LM composite can be subsequently activated by direct laser writing, forming high-resolution electrically conductive pathways. We demonstrate the utility of the double emulsion ink by creating intricate electrical interconnects for stretchable electronic circuits. This work combines the speed, consistency, and precision of laser-assisted manufacturing with the printability, high conductivity, strain insensitivity, and mechanical robustness of the PDMS-LM composite, unlocking high-yield, high-throughput, and high-density stretchable electronics.

Highly stretchable multilayer electronic circuits using biphasic gallium-indium.

Stretchable electronic circuits are critical for soft robots, wearable technologies and biomedical applications. Development of sophisticated stretchable circuits requires new materials with stable conductivity over large strains, and low-resistance interfaces between soft and conventional (rigid) electronic components. To address this need, we introduce biphasic Ga-In, a printable conductor with high conductivity (2.06 × 106 S m-1), extreme stretchability (>1,000%), negligible resistance change when strained, cyclic stability (consistent performance over 1,500 cycles) and a reliable interface with rigid electronics. We employ a scalable transfer-printing process to create various stretchable circuit board assemblies that maintain their performance when stretched, including a multilayer light-emitting diode display, an amplifier circuit and a signal conditioning board for wearable sensing applications. The compatibility of biphasic Ga-In with scalable manufacturing methods, robust interfaces with off-the-shelf electronic components and electrical/mechanical cyclic stability enable direct conversion of established circuit board assemblies to soft and stretchable forms.

Surface Actuation and Sensing of a Tensegrity Structure Using Robotic Skins.

Tensegrity robots comprising solid rods connected by tensile cables are of interest due to their flexible and robust nature, which potentially makes them suitable for uneven and unpredictable environments where traditional robots often struggle. Much progress has been made toward attaining locomotion with tensegrity robots. However, measuring the shape of a dynamic tensegrity without the use of external hardware remains a challenge. Here we show how robotic skins may be attached around the exterior of a tensegrity structure, to both control and measure its shape from its surface. The robotic skins are planar, skin-like membranes with integrated actuators and sensors, which we use to transform a passive tensegrity structure into an active tensegrity robot that performs tasks such as locomotion. In addition, sensors placed on the ends of the tensegrity rods are used to directly measure orientation relative to the ground. The hardware and algorithms presented herein thus provide a platform for surface-driven actuation and intrinsic state estimation of tensegrity structures, which we hope will enable future tensegrity robots to execute precise closed-loop motions in real-world environments.

Shape Changing Robots: Bioinspiration, Simulation, and Physical Realization.

One of the key differentiators between biological and artificial systems is the dynamic plasticity of living tissues, enabling adaptation to different environmental conditions, tasks, or damage by reconfiguring physical structure and behavioral control policies. Lack of dynamic plasticity is a significant limitation for artificial systems that must robustly operate in the natural world. Recently, researchers have begun to leverage insights from regenerating and metamorphosing organisms, designing robots capable of editing their own structure to more efficiently perform tasks under changing demands and creating new algorithms to control these changing anatomies. Here, an overview of the literature related to robots that change shape to enhance and expand their functionality is presented. Related grand challenges, including shape sensing, finding, and changing, which rely on innovations in multifunctional materials, distributed actuation and sensing, and somatic control to enable next-generation shape changing robots are also discussed.

Sustainable manufacturing of sensors onto soft systems using self-coagulating conductive Pickering emulsions.

Compliant sensors based on composite materials are necessary components for geometrically complex systems such as wearable devices or soft robots. Composite materials consisting of polymer matrices and conductive fillers have facilitated the manufacture of compliant sensors due to their potential to be scaled in printing processes. Printing composite materials generally entails the use of solvents, such as toluene or cyclohexane, to dissolve the polymer resin and thin down the material to a printable viscosity. However, such solvents cause swelling and decomposition of most polymer substrates, limiting the utility of the composite materials. Moreover, many such conventional solvents are toxic or otherwise present health hazards. Here, sustainable manufacturing of sensors is reported, which uses an ethanol-based Pickering emulsion that spontaneously coagulates and forms a conductive composite. The Pickering emulsion consists of emulsified polymer precursors stabilized by conductive nanoparticles in an ethanol carrier. Upon evaporation of the ethanol, the precursors are released, which then coalesce amid nanoparticle networks and spontaneously polymerize in contact with the atmospheric moisture. We printed the self-coagulating conductive Pickering emulsion onto a variety of soft polymeric systems, including all-soft actuators and conventional textiles, to sensitize these systems. The resulting compliant sensors exhibit high strain sensitivity with negligible hysteresis, making them suitable for wearable and robotic applications.

Electronic skins and machine learning for intelligent soft robots.

Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges of real-world environments.

Uniform conductivity in stretchable silicones via multiphase inclusions.

Many soft robotic components require highly stretchable, electrically conductive materials for proper operation. Often these conductive materials are used as sensors or as heaters for thermally responsive materials. However, there is a scarcity of stretchable materials that can withstand the high strains typically experienced by soft robots, while maintaining the electrical properties necessary for Joule heating (e.g., uniform conductivity). In this work, we present a silicone composite containing both liquid and solid inclusions that can maintain a uniform conductivity while experiencing 200% linear strains. This composite can be cast in thin sheets enabling it to be wrapped around thermally responsive soft materials that increase their volume or stretchability when heated. We show how this material opens up possibilities for electrically controllable shape changing soft robotic actuators, as well as all-silicone actuation systems powered only by electrical stimulus. Additionally, we show that this stretchable composite can be used as an electrode material in other applications, including a strain sensor with a linear response up to 200% strain and near-zero signal noise.

Assessing plant performance in the Enviratron.

BACKGROUND: Assessing the impact of the environment on plant performance requires growing plants under controlled environmental conditions. Plant phenotypes are a product of genotype × environment (G × E), and the Enviratron at Iowa State University is a facility for testing under controlled conditions the effects of the environment on plant growth and development. Crop plants (including maize) can be grown to maturity in the Enviratron, and the performance of plants under different environmental conditions can be monitored 24 h per day, 7 days per week throughout the growth cycle. RESULTS: The Enviratron is an array of custom-designed plant growth chambers that simulate different environmental conditions coupled with precise sensor-based phenotypic measurements carried out by a robotic rover. The rover has workflow instructions to periodically visit plants growing in the different chambers where it measures various growth and physiological parameters. The rover consists of an unmanned ground vehicle, an industrial robotic arm and an array of sensors including RGB, visible and near infrared (VNIR) hyperspectral, thermal, and time-of-flight (ToF) cameras, laser profilometer and pulse-amplitude modulated (PAM) fluorometer. The sensors are autonomously positioned for detecting leaves in the plant canopy, collecting various physiological measurements based on computer vision algorithms and planning motion via "eye-in-hand" movement control of the robotic arm. In particular, the automated leaf probing function that allows the precise placement of sensor probes on leaf surfaces presents a unique advantage of the Enviratron system over other types of plant phenotyping systems. CONCLUSIONS: The Enviratron offers a new level of control over plant growth parameters and optimizes positioning and timing of sensor-based phenotypic measurements. Plant phenotypes in the Enviratron are measured in situ-in that the rover takes sensors to the plants rather than moving plants to the sensors.

OmniSkins: Robotic skins that turn inanimate objects into multifunctional robots.

Robots generally excel at specific tasks in structured environments but lack the versatility and the adaptability required to interact with and locomote within the natural world. To increase versatility in robot design, we present robotic skins that can wrap around arbitrary soft bodies to induce the desired motions and deformations. Robotic skins integrate actuation and sensing into a single conformable material and may be leveraged to create a multitude of controllable soft robots with different functions or gaits to accommodate the demands of different environments. We show that attaching the same robotic skin to a soft body in different ways, or to different soft bodies, leads to distinct motions. Further, we show that combining multiple robotic skins enables complex motions and functions. We demonstrate the versatility of this soft robot design approach in a wide range of applications-including manipulation tasks, locomotion, and wearables-using the same two-dimensional (2D) robotic skins reconfigured on the surface of various 3D soft, inanimate objects.