Liquid Metal Based Soft Robotics: Materials, Designs, and Applications

A soft robot is a kind of robot designed to simulate mollusks. It has the characteristics of degrees of freedom, strong adaptability, and high flexibility and safety. The main purpose of this paper is to study the intelligent education assistance of soft robots and then combine the application of improved genetic algorithm and the Internet of Things technology in soft robots to improve its performance and effect. Therefore, this paper designs the optimal guidance strategy through the NSGA genetic algorithm and then combines the improved genetic algorithm and the application of the Internet of Things technology in the flexible actuator and FEA actuator of the soft robot. In order to optimize the performance of the IoT-assisted intelligent education software robot based on the improved genetic algorithm, the genetic algorithm simulation test experiment, the rolling motion simulation experiment of the bionic software robot, and the inflating and exhausting experiment of the base section of the software robot are designed and analyzed. Through the analysis of the data obtained from the experiment, this paper finally designs a set of controlled experiments to verify its teaching ability. The experimental results show that the students’ evaluation of the IoT intelligent education software robot education method based on the improved genetic algorithm designed in this paper is 16.31 points higher than that of the traditional education method. Compared with the traditional teaching, the scores of the students after the IOT intelligent education software robot teaching based on the improved genetic algorithm is 11.16 points higher.

Soft robots made from soft materials can safely interact with humans and environments compared with the traditional rigid robots. However, the inherent compliance of the soft robots makes them difficult to realize the jumping motion. Although many soft robots can jump through the design of bistable structure. However, the existing soft robots need rigid spine to be embedded in the soft body. Therefore, it is still hard for the jumping of a soft robot with pure soft body. This paper offers the design method for the pure soft bistable robot with jumping ability. The critical buckling force and total elastic potential energy of the robot can be increased by designing the soft body with different thickness. We use the twisted artificial muscle (TAM) to realize the jumping motion of the soft bistable robot. The proposed robot can jump at a height nearly two times its body height. We envision that the design method will promote the application of the soft robot with pure soft body to realize more types of motion strategy.

Conventional robots are constructed with rigid parts and materials, restricting their ability to deform freely and adapt to the external environment. Performing tasks such as handling delicate materials and navigating narrow pathways is challenging for these typical robots made of stiff materials. However, there is a growing demand for such functions in various industries. Recent advances in soft robotics have garnered considerable attention as solutions to these challenges. Soft robotic systems in various forms present a systematic solution for performing unique tasks. The goal of soft robotics research is to develop advanced technologies, incorporating innovative robotic mechanisms based on soft materials and structures to enhance adaptability. Soft robots find broad applications in diverse fields, including industry, healthcare, and field exploration. Soft component technology plays a crucial role in configuring soft robots. Soft actuators introduce new types of motion and operation mechanisms, while soft sensors measure the deformation of the actuators with a tiny stiffness change. These advancements in soft actuators and sensors encourage researchers to explore new robotic designs and applications. Additionally, the component technology enables the integration of soft and typical robots into hybrid B Youngsu Cha

<p>Soft robots have become important members of the robot community with many potential applications owing to their unique flexibility and security embedded at the material level. An increasing number of researchers are interested in their designing, manufacturing, modeling, and control. However, the dynamic simulation of soft robots is difficult owing to their infinite degrees of freedom and nonlinear characteristics that are associated with soft materials and flexible geometric structures. In this study, a novel multi-flexible body dynamic modeling and simulation technique is introduced for soft robots. Various actuators for soft robots are modeled in a virtual environment, including soft cable-driven, spring actuation, and pneumatic driving. A pneumatic driving simulation was demonstrated by the bending modules with different materials. A cable-driven soft robot arm prototype and a cylindrical soft module actuated by shape memory alley springs inspired by an octopus were manufactured and used to validate the simulation model, and the experimental results demonstrated adequate accuracy. The proposed technique can be widely applied for the modeling and dynamic simulation of other soft robots, including hybrid actuated robots and rigid-flexible coupling robots. This study also provides a fundamental framework for simulating soft mobile robots and soft manipulators in contact with the environment.</p> <p> </p>

Vertebrate animals benefit from a combination of rigidity for structural support and softness for adaptation. Similarly, integrating rigidity and softness can enhance the versatility of soft robotics. However, the challenges associated with creating durable bonding interfaces between soft and rigid materials have limited the development of hybrid robots. Existing solutions require specialized machinery, such as polyjet 3D printers, which are not commonly available. In response to these challenges, we have developed a 3D printing technique that can be used with almost all commercially available FDM printers. This technique leverages the common issue of underextrusion to create a strong bond between soft and rigid materials. Underextrusion generates a porous structure, similar to fibrous connective tissues, that provides a robust interface with the rigid part through layer fusion, while the porosity enables interlocking with the soft material. Our experiments demonstrated that this method outperforms conventional adhesives commonly used in soft robotics, achieving nearly 200% of the bonding strength in both lap shear and peeling tests. Additionally, we investigated how different porosity levels affect bonding strength. We tested the technique under pressure scenarios critical to soft and hybrid robots and achieved three times more pressure than the current adhesion solution. Finally, we fabricated various hybrid robots using this technique to demonstrate the wide range of capabilities this approach and hybridity can bring to soft robotics.

Soft robotics is a rapidly evolving research field that focuses on developing robots with bioinspired actuation/sensing mechanisms and highly flexible soft materials, some of which are similar to those found in living organisms. The hydrogel has the characteristics of excellent biocompatibility, softness, and elasticity, which makes it an ideal candidate material for the preparation of soft robots. Here we utilized a self-healing approach to develop a catalytically driven soft robot, which was constructed by dynamic imine bonds between modular hydrogels. One of the modules was a hydrogel formed by dynamic aldimine cross-linking of chitosan and glutaraldehyde, and the other module was a hydrogel embedded with catalase. The soft hydrogel robot moved because of catalytic reactions between the robot and environment [hydrogen peroxide (H2O2) fuel], giving rise to a fluidic release that supports propulsion, as inspired by the jet-propulsive mechanism in swimming dragonfly larvae. The speed of the soft robot can be mediated by adjusting the concentration of H2O2 and enable/disable movement based on the folding and unfolding of enzymes. In addition, the hydrogel formed by replacing glutaraldehyde with dialdehyde-functionalized PEG2000 had excellent elastic properties, and the soft robot based on PEG2000 had a higher movement speed than that based on glutaraldehyde under the same H2O2 concentration. Moreover, the addition of iron oxide nanoparticles can realize the magnetic guidance of the soft robot and the combination of different modules can realize different motion modes. The highly configurable self-healing catalytic soft robot holds great potential for a variety of interesting applications, including swimming robots, robot-assisted water treatment, and drug release.

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.

Since its beginnings in the 1960s, soft robotics has been a steadily growing field that has enjoyed recent growth with the advent of rapid prototyping and the provision of new flexible materials. These two innovations have enabled the development of fully flexible and untethered soft robotic systems. The integration of novel sensors enabled by new manufacturing processes and materials shows promise for enabling the production of soft systems with ‘embodied intelligence’. Here, four experts present their perspectives for the future of the field of soft robotics based on these past innovations. Their focus is on finding answers to the questions of: how to manufacture soft robots, and on how soft robots can sense, move, and think. We highlight industrial production techniques, which are unused to date for manufacturing soft robots. They discuss how novel tactile sensors for soft robots could be created to enable better interaction of the soft robot with the environment. In conclusion this article highlights how embodied intelligence in soft robots could be used to make soft robots think and to make systems that can compute, autonomously, from sensory inputs.

Integration of multiple functions into a simple unit can simplify the device design and facilitate the miniaturization and portability of soft robots. Actuating and sensing functions are the basis of soft robots to interact with the environment. In this work, a modified melt‐spinning method is used to produce liquid crystal elastomer (LCE) fibers with a hollow structure and fill the core with liquid metal (LM) to construct LCE–LM coaxial fibers with integrated sensing and actuation functions. The inner LM generates Joule heat upon an electrical trigger to actuate the outer LCE shell. When LCE–LM coaxial fibers are actuated or loaded with objects, the deformation of LCE shell forces the inner LM to reform, allowing to sense the motion of the fibers or the mass of objects, by measuring the feedback voltage or resistance change of the inner LM. Benefitting from the material selection and structural design, the LCE–LM coaxial fibers with integrated actuating and sensing capability exhibit large reversible contraction (40%) and reliable durability (5000 cycles for actuation and motion sensing). Finally, a soft three‐arm Delta robot is fabricated to complete object identification, sorting tasks, and in situ monitoring the motion process.

This manuscript explains the employment of flexible actuators to act as a soft robot and transporting agent to assist medical X-ray examinations. Although soft robots from silicone material can be transparence and a human compliance used as medical assistive devices, soft robots have some problems: they tend to be sluggish, have long and imprecise gait trajectories, and need their control parameters to be adjusted for motion diversion. A soft robot with omnidirectional locomotion has been created, one that has a combination of pneumatic rubber legs that form a soft robot platform and an associated hardware setup. Tests have confirmed its omnidirectional locomotion ability; it has a maximum speed of 6.90 mm/s in forward locomotion and a maximum payload of 70 g. These features indicate that the robot can be used as a medical assistive device for fluoroscopy examinations.

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).

Studying droplet dynamics is crucial for microfluidics, materials science and soft robotics. Electric and magnetic fields enable precise droplet handling for drug delivery and diagnostics, but high-energy inputs trigger droplet instabilities and explosive breakup. Here these materials instabilities are leveraged for high energy transduction in liquid metal droplets with high surface tension and conductivity. Liquid metal shapeshifting (LMSS) is introduced, a method for fluidic power generation that uses Lorentz forces and surface tension. Demonstrated in a low voltage (mean≤0.1V), soft, bidirectional pump, it features a simple design with two electrodes, one liquid metal drop, and a small magnet, which can be driven directly from an AAA battery, outperforming previous pumps. Demonstrating versatility, this shapeshifting approach is applied to create an active wearable photoprotective skin, soft robotic actuators, and adaptive color-changing units for clothing, establishing a range of material-focused applications. The liquid metal shapeshifting system represents a transformative material platform for compact, integrated fluidic power supplies and its potential spans applications across lab-on-a-chip devices, micro/macro robotics, adaptive materials, and wearable technologies, positioning it as a foundational technology for the next generation of multifunctional, fluidic-driven soft machines.

Soft robotics, intended as the use of soft materials in robotics, is a young yet promising and growing research field. The need for soft robots emerged in robotics, for facing unstructured environments, and in artificial intelligence, too, for implementing the embodied intelligence, or morphological computation, paradigm, which attributes a stronger role to the bodyware and its interaction with the environment. Using soft materials for building robots poses new technological challenges: the technologies for actuating soft materials, for embedding sensors into soft robot parts, for controlling soft robots are among the main ones. Though still in its early stages of development, soft robotics is finding its way in a variety of applications, where safe contact is a main issue, in the biomedical field, as well as in exploration tasks and in the manufacturing industry. Literature in soft robotics is increasingly rich, though scattered in many disciplines. The soft robotics community is growing worldwide and initiatives are being taken, at international level, for consolidating this community and strengthening its potential for disruptive innovation.

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.

Endoscopic procedures are minimally invasive and are commonly used in surgeries and various diagnoses. Endoscopes allow vision in very delicate and constricted areas of the human body. Although the endoscope is a very beneficial tool, it still has its disadvantages. Today’s metallic endoscopes are rigid and tend to be hard to manipulate on the fine scales necessary to work within the human body while minimizing harm. Endoscopes made of metal can easily puncture an internal organ if not directed properly [1]. Soft robotics is a new and unique system for designing and creating a new generation of medical devices [2]. With soft robotics, silicone-based molds can be controlled and driven using nothing more than tubing and air pressure (Fig. 1) [3]. Employing the principles of soft robotics, we are attempting to create a new kind of endoscope made from a silicon-based material. Ideally, a silicone-based endoscope would be cheap to produce and require almost no training to operate, while still maintaining the same benefits of metallic endoscopes. Endoscopes made from soft robotic actuators will have better dexterity due to the wall thickness allowing a full range of motion and flexion. In this study, it was hypothesized that additional controllability in the soft robotic endoscope design could be achieved by controlling the combination of wall thicknesses of the silicone molds during the fabrication process.