Soft Robotics Research, Challenges, and Innovation Potential, Through Showcases

<div>The development of soft materials for various smart applications, such as in</div><div>soft robotic devices derived from elastomers, naturally occurring rubbers and polymers,</div><div>can be traced back to 1940s. Contrary to conventional robotics, soft robotic research</div><div>was primarily dedicated to reproducing and mimicking the elasticity and compliance of</div><div>biological substrates. In the last few decades, however, subsequent progress in soft</div><div>robotic devices and manipulators has been witnessed within the subcategories of</div><div>material & processing, sensor and actuators. In this chapter, a comprehensive</div><div>introduction to soft robotics engineering is highlighted exclusively from the perspective</div><div>of materials development. Soft material prototypes are eventually exploited towards</div><div>designing biomimetic prosthetic devices with sensing capabilities and soft actuating</div><div>bodies. We present an elaborate discussion on soft materials engineering, including</div><div>elastomers, polymer composites containing fillers and associated processing</div><div>methodologies. Despite high flexibility and inherent biocompatibility, conventional</div><div>soft elastomer-based (silicone and polyurethane) matrices often suffer from either poor</div><div>mechanical strength or reduced mechanical output at higher temperatures. This review</div><div>briefly summarizes and critically discusses the different types of compliant materials,</div><div>i.e. elastomers, fluids, gels and others, which are currently being employed in soft</div><div>robotics. This chapter also highlights new opportunities and paves some potential</div><div>avenues to address several challenges, particularly the ones concerning and soft robotic</div><div>materials. Moreover, utilization of soft rubber for sensors and other applications could</div><div>ameliorate the versatility of soft material applications. Therefore, challenges and future</div><div>direction in soft robotics research have to be explicitly integrated within the subdomain</div><div>of materials development and processing and, therefore, addressed in-depth in this</div><div>chapter, with an emphasis on autonomous soft robotic arms capable of stimuliresponsive</div><div>operations.</div>

Emerging classes of soft robotics with flexible actuation, intelligent sensibility, and biomimetic functionality are driving significant advances in academic researches and commercial applications. Such new generation robotics relies heavily on important breakthroughs in soft matter engineering and flexible actuation systems. Unlike conventional rigid robots, soft robots generally have unique sporting styles and manufacturing strategies. Recently, a series of newly developed soft materials—gallium‐based liquid metals (LMs) are found to display dramatical roles in making soft machines and robotics with their unusual properties in soft robotic actuation and self‐driven field. With superior merits of both high stretchability and electroconductivity over conventional soft materials, LM is increasingly innovated as flexible sensors and actuators in constructing intelligent soft robots. Typical advances in LM enabled soft machines and robotics are summarized and interpreted, herein, with special focus on the driving principles of the LM objects, the preparation methods of LM flexible electronics for making soft robots, and the applications of LM in biomedicines and intelligent robots, together with the consideration of key challenges facing LM‐based soft robotics and its future prospects.

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

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 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.

<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>

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 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.

Artificial Intelligence (AI) has rapidly become a widespread design aid through the recent proliferation of generative AI tools. In this work we use generative AI to explore soft robotics designs, specifically Soft Biomorphism, an aesthetic design paradigm emphasizing the inherent biomorphic qualities of soft robots to leverage them as affordances for interactions with humans. The work comprises two experiments aimed at uncovering how generative AI can articulate and expand the design space of soft biomorphic robotics using text-to-image (TTI) and image-to-image (ITI) generation techniques. Through TTI generation, Experiment 1 uncovered alternative interpretations of soft biomorphism, emphasizing the novel incorporation of, e.g., fur, which adds a new dimension to the material aesthetics of soft robotics. In Experiment 2, TTI and ITI generation were combined and a category of hybrid techno-organic robot designs discovered, which combined rigid and pliable materials. The work thus demonstrates in practice the specific ways in which AI image generation can contribute towards expanding the design space of soft robotics.

Robots have been around us for quite some time now. Robots are machines capable of performing tasks on their own with high accuracy and precision and minimal supervision to reduce human effort. Our daily lives are touched by different forms of robots around us, be it a direct interaction in form of robotic helpers at the airport or indirectly in the case of industrial arm robots assembling the electronics and machines. Traditionally, there are several component systems to robotics and one of the prominent one is actuation, which is made from hard and rigid components in these conventional robots. However, this is responsible for their intermittent motion. A new field of research, soft robotics, makes of soft materials for their fabrication and draws inspiration from natural organisms and how they produce fluidic and complex motions. There are different ways by which these soft materials can be actuated and include techniques like applying pneumatic and hydraulic pressure, applying heat, making use of a chemical process like combustion, applying varying magnetic fields and applying electric fields. Among these, the class of actuators employing soft materials deformed by applying the electric field, known as Dielectric elastomer actuators, are preferred owing to their advantages of simple fabrication, large actuation strain and control by electrical stimulus. The architecture of these actuators resembles a variable capacitor with electrodes on either sides of the elastomer, and their performance depends on material properties like dielectric constant and mechanical stiffness. Generally, researchers have tried to modify either of these properties with addition of solid conductive or ceramic fillers, or chemical modification of the elastomer, and this has led to undesirable changes on the other material properties. Drawing inspiration from biological materials like skin, which is a soft material system filled with fluids, a novel composite is synthesized utilizing a solid polymer matrix and a high dielectric constant liquid filler, an ionic liquid. An increase in dielectric constant is observed accompanied with significant reduction in mechanical stiffness. The novel composites show significant improvement upon the actuation performance of the commonly used elastomeric system at considerably low electric fields. Owing to the judicious choice of material systems, the composites also show high stability and transparency. With emergence of new generation technologies such as display touchscreens, touchpads, virtual reality and augmented reality, human machine interaction and haptics has become ever more important. Soft actuators and dielectric elastomer actuators (in particular), owing to their inherent softness, have convincing advantage over the conventional rigid actuators. Additionally, state-of-the-art technologies for haptics primarily seek to simulate the perception of texture change with sensory manipulation. Furthermore, dielectric elastomer actuators in their current form suffer from visible obstructiveness and non-integrable architecture. A new device architecture for dielectric elastomer actuators, capable of producing on-demand surface texture change and local topographic features, is demonstrated. Owing to its unique architecture, a highly transparent, flexible and integrable configuration of the device is showed. Out-of-plane deformations and force feedback from the devices are shown to be well above the perceptual threshold of sensors, known as mechanoreceptors, present in our fingertips. Another attribute of the biological appendages in natural organisms is the ability to reversibly modulate its mechanical properties upon demand, which still eludes the field of soft robotics. One of the best examples of this is found in human body, where muscles modulate their stiffness in accordance to the load distribution and movement. Most of the current approaches mange to only demonstrate reversible stiffness and involves combination of a passive reversible stiffness component with the existing actuator. Hence, a novel low-temperature phase change material is identified for enabling reversible rigidity in the soft actuators owing to its low melting point, significant gap between melting and solidification, superior thermal stability, high mechanical strength and high dielectric constant. Thermal control is chosen the modulation of mechanical properties, owing to its lightweight design and scalability. The fabricated reversible rigidity composites show impressive variations in stretchability, bendability and hardness. Owing to its ability to melt and re-solidify, the composites also demonstrate a healing behaviour with the ability to regain complete structural integrity and mechanical strength. These novel ultra-soft, high-k liquid filler-based polymer composites, reversible rigidity composites and transparent and integrable device architectures for field-driven soft actuators could pave the way for next generation applications such as shape morphing, adaptive surfaces and transparent haptic devices.

Agriculture and horticulture depend heavily on human labor to perform tasks that are often dirty, hazardous, and highly repetitive. One reason for the lack of automation of these tasks is the absence of suitable robotic handling equipment. Rigid robotic manipulators are typically incapable of performing dexterous manipulation tasks such as harvesting apples as they lack the ability to adapt to objects of various shapes and sizes. Such robotic manipulators need a large number of sensors and actuators to overcome these challenges, making them overly complex and not very robust. Therefore, the development of robotic manipulators for dexterous manipulation tasks has begun to focus on morphological computation, in which at least some aspects of the control are outsourced to the body of the robot. Taking inspiration from grasping mechanisms in natural systems, the field of soft robotics attempts to address this problem by constructing robots from soft materials. Although soft robotics may be the key to realizing automation of dexterous manipulation tasks, the current commercially available soft robotic grippers are only capable of performing simple pick-and-place tasks with open-loop control. This limited capability is in large part due to a lack of techniques to endow these manipulators with a sense of self-movement and body position, known as proprioception. Proprioception is a simple problem for conventional robots with rigid members and discrete joints, as the body position can be easily reconstructed using the information from encoders in the robots’ joints. However, it is a highly challenging problem for soft robots with virtually infinite degrees of freedom and above all, no suitable off-the-shelf sensors…

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.

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).

A systematic trend analysis of 3D printing techniques used in specific soft robotic elements

Progressive neurodegenerative disease known as Parkinson's disease (PD) is characterized by both motor and nonmotor symptoms that severely reduce the quality of life. Recent developments in soft robotics provide customizable, cozy, and less intrusive assistive devices, which provide promising answers to these problems. To develop an enhanced support system for people with PD, this article explores the potential of next-generation soft robotics, specifically hydrogel materials, integrated with artificial intelligence (AI) and augmented reality (AR) to provide an innovative solution for PD management. The integration of an AI copilot allows for remote monitoring and real-time adjustments, ensuring optimal performance and personalized care. The use of AR enhances human-computer interactions, offering an intuitive and immersive experience for both patients and healthcare providers. By leveraging these advanced technologies, our approach aims to substantially improve motor function, reduce symptoms, and enhance the overall quality of life for PD patients. This review outlines the key components, benefits, and potential impact of this novel approach, highlighting the transformative potential of combining wearable robotics, AI, and AR in the treatment of PD. The potential for creating novel healthcare solutions by combining soft robotics, functional materials, the Internet-of-Things (IoT), and machine learning (ML) is highlighted by this multidisciplinary approach.