Soft Robotics Research Articles

Polyacrylamide/starch hydrogels doped with layered double hydroxides towards strain sensing applications.

Electrically conductive hydrogels have attracted enormous attention due to the rapid development of flexible electronics. In this paper, the application of layered double hydroxides (LDHs) in the field of conductive hydrogel for strain sensing is firstly explored. LDHs are introduced to the polyacrylamide (PAM)/starch (St) semi-interpenetrating network (SIPN) to fabricate conductive PAM/St/LDHs (PSL) hydrogels for strain sensing applications. The results show that LDHs incorporated into PAM/St SIPN as inorganic nano fillers not only improve the mechanical strength, but also innovatively endow the hydrogel with electrical conductivity properties. The PSL hydrogels exhibit excellent mechanical properties (with an elongation strain of 1750% and a fracture strength of 0.22MPa) and decent sensing properties (with a gauge factor of 2.73). As a proof of concept, an 8*8 sensing array based on PSL hydrogels is designed to realize the visualization of pressure sensing, demonstrating the broad prospect of PSL hydrogels in applications of human-computer interaction, flexible wearable and soft robotics.

Biomimetic Structural Hydrogels Reinforced by Gradient Twisted Plywood Architectures.

Naturally structural hydrogels such as crustacean exoskeletons possess a remarkable combination of seemingly contradictory properties: high strength, modulus, and toughness coupled with exceptional fatigue resistance, owing to their hierarchical structures across multiple length scales. However, replicating these unique mechanical properties in synthetic hydrogels remains a significant challenge. This work presents a synergistic approach for constructing hierarchical structural hydrogels by employing cholesteric liquid crystal self-assembly followed by nanocrystalline engineering. The resulting hydrogels exhibit a long-range ordered gradient twisted plywood structure with high crystallinity to mimic the design of crustacean exoskeletons. Consequently, the structural hydrogels achieve an unprecedented combination of ultrahigh strength (46±3MPa), modulus (496±25MPa), and toughness (170±14MJ m-3), together with recorded high fatigue threshold (32.5kJ m-2) and superior impact resistance (48±2kJ m-1). Additionally, through controlling geometry and compositional gradients of the hierarchical structures, a programmable shape morphing process allows for the fabrication of complex 3D hydrogels. This study not only offers valuable insights into advanced design strategies applicable to a broad range of promising hierarchical materials, but also pave the ways for load-bearing applications in tissue engineering, wearable devices, and soft robotics.

Combining Simple Deformations to Elicit Complex Motions and Directed Swimming of Smart Organic Crystals with Controllable Thickness.

The lack of control over the crystal growth in a systematic way currently stands as an unsurmountable impediment to the preparation of dynamic crystals as soft robots; in effect, the mechanical effects of molecular crystals have become a subject of scattered reports that pertain only to specific crystal sizes and actuation conditions, often without the ability to establish or confirm systematic trends. One of the factors that prevents the verification of such performance is the unavailability of strategies for effectively controlling crystal size and aspect ratio, where crystals of serendipitous size are harvested from crystallization solution. Here we devised a water-assisted precipitation method to prepare crystals of chemical variants of 9-anthracene derivative (chemical substitution of the 9th carbon) crystals with various thicknesses that respond to ultraviolet light with simple mechanical effects, including bending, splintering, and rotation. By capitalizing on the robust mechanical flexibility and deformability of crystals, we demonstrate systematic variations in crystal deformation that are further elevated in complexity to construct crystal-based robots capable of motions reminiscent of controllable sailing and humanoid movements. The results illustrate an approach to eliminate a critical obstacletowards complete control over the motility of dynamic molecular crystals as microrobots in nonaerial environments.

Electrothermally actuated network metamaterials with reconfigurable bending deformation modes

Reconfigurable metamaterials with specific deformation modes show great promise in applications such as multifunctional antenna, stretchable electronic device, and reconfigurable soft robot, due to their ability to achieve multiple operational states within a single system. Previous researches on active metamaterials with bending deformation responses revealed two main issues: (1) achieving reconfigurable deformation within the same metamaterial is challenging due to the reliance on uniform external field actuation; and (2) there is a lack of in-depth studies on the microstructure-property relationships for the bending deformation responses of network metamaterials due to the lack of theoretical analysis. To address these issues, this study presents a mechanical design strategy for an electrothermally actuated network metamaterials to realize reconfigurable bending deformation. A theoretical model describing the electrothermally actuated bending deformation responses is developed through a three-level analysis, which offers a comprehensive understanding of the parameter-property relationships and accurately describes the bending deformation behaviors. The validity of these mechanical models is confirmed through finite element analyses (FEAs) and experimental results. These mechanical models provide analytical solutions for crucial mechanical quantities, including the electrothermally actuated bending angles and effective strains for bending deformation responses. The bending behaviors of the reconfigurable metamaterials under electrothermal actuation can be adjusted by the key nondimensional geometric parameters and the actuation strategies. Additionally, experimental results and FE calculations demonstrate that multiple bending responses can be realized within a single metamaterial by different actuation strategies. This study offers comprehensive guideline from theoretical predictions, FE calculations, and experimental demonstrations for future researched of reconfigurable metamaterials to realize required deformation behaviors.

Design and Implementation of the Soft Robot's End-Effecter

Design and Implementation of the Soft Robot's End-Effecter

A Morphological Transfer-Based Multi-Fidelity Evolutionary Algorithm for Soft Robot Design

A Morphological Transfer-Based Multi-Fidelity Evolutionary Algorithm for Soft Robot Design

Modeling Elastic Cable-Surface Friction for Soft Robots

Modeling Elastic Cable-Surface Friction for Soft Robots

Superflexible Carbon Nanofibers for Multidimensional Complex Deformation Sensing in Soft Robots.

Soft robots can make complex motions or deformations due to their infinite freedom, which poses great challenges for monitoring their motion and position. While previous investigations of flexible sensing either focused on stretchable or compression deformations in one or two directions, the complex multidimensional deformations that occur on the surfaces of soft robots have been frequently overlooked. In this work, inspired by spider silk, superflexible carbon nanofibers with a bundled structure were biomimetically designed and fabricated using electrospinning technology and carbonization treatment. The fabricated fibers can be microscopically folded at 180° and can sustain multidimensional shrinkage deformation without microstructural damage during 200,000 times of repeated folding. In addition, the fibers process ultrasmall bending resistance that is two orders of magnitude lower than that of A4 paper and commercial conductive fibers, demonstrating excellent flexibility that is ideal for fabricating sensors in soft robots. Combining the study of origami techniques and mechanical simulations, the bending resistance of the fibers was found to have a step change in response to different deformation angles and radii. As a demonstration, a sensor based on this flexible carbon nanofiber successfully monitors the irregular shrinkage deformation of soft parts, showing great potential in applications of grasping, recognition, and perception. This work sheds light on the design of ultraflexible conductive carbon materials and provides an avenue for the extreme shape-morphing monitoring of soft robots.

Enhancing versatility and adaptability in soft robotics: Design and analysis of an adaptive gripping port

Abstract Currently, soft robots show the considerable promise for various applications. However, soft actuators tend to get damaged more easily compared to rigid mechanical components. Its the common practice of replacing soft actuators typically requires manual intervention and a longer duration. In this paper, we design an adaptive gripping port (AGP) for soft robotics interactions, used for fast switching and connectivity. Through a blend of theoretical modeling, fabrication, and experimental analysis, its gripping, releasing, and sealing capabilities are demonstrated. We present four types of the AGP with different internal bladder structures, demonstrating maximal enhancements in grip strength and sealing performance of up to 50\% and 40\%, respectively. Experimental shows the AGP's adaptability to connectors of various sizes, accommodating diameters from 0 mm to 15.76 mm under negative pressure driving. The relationship between gripping force, maximum sealing pressure, connector diameter, and driving pressure is studied. We also exhibit collaborations with the circular gripper and soft grippers with the AGP to verify possibility of its interaction.

Multilayer Bionic Tunable Strain Sensor with Mutually Non‐Interfering Conductive Networks for Machine Learning‐Assisted Gesture Recognition

AbstractFlexible strain sensors capable of acquiring tiny mechanical signals and attaching to various irregular surfaces are becoming prevalent in physiological measurement, soft robotics, and human‐machine interaction. However, the advancement of flexible strain sensors is substantially impeded by the intrinsic compromise between sensitivity and the range of detectable signals. Inspired by the multilayer structure of nacre in nature, a carbon nanotubes (CNTs)/graphene (GR)/graphene (GR)/thermoplastic polyurethane (TPU) mat (CGGTM) is prepared by electrospinning and high‐pressure spraying technology. By designing a multilayer structure with mutually non‐interfering conductive networks, the resulting CGGTM possesses a low detection limit (0.05% strain), high sensitivity (gauge factor, GF > 152537), large detection range (up to 364% strain), fast response/recovery time (80 ms/100 ms), and excellent cyclic durability (up to 1000 cycles). Furthermore, the CGGTM also exhibits satisfactory triboelectric performances when assembled into a triboelectric nanogenerator (TENG, 3 × 3 cm2), including high triboelectric output (open‐circuit voltage Voc = 135.4 V, short‐circuit current Isc = 1.25 µA) and power density (88 mW m−2), enabling reliable power supply, self‐powered sensing, and pulse monitoring capability. Finally, CGGTM is successfully applied to the collection of biological signals and multi‐gesture motion recognition assisted by machine learning algorithms, which holds promise for intelligent interaction with gestures in the future.

Model-based design of a mechanically intelligent shape-morphing structure

Soft robotics has significant interest within the industrial applications due to its advantages in flexibility and adaptability. Nevertheless, its potential is challenged by low stiffness and limited deformability, particularly in large-scale application scenarios such as underwater and offshore engineering. The integration of smart materials and morphing structures presents a promising avenue for enhancing the capabilities of soft robotic systems, especially in large deformation and variations in stiffness. In this study, we propose a multiple smart materials based mechanically intelligent structure devised through a model-based design framework. Specifically, the intelligent structure incorporates smart hydrogel and shape memory polymer (SMP). Employing the finite element method (FEM), we simulated the complex interactions among smart material to analyze the performance characteristics of the intelligent structure. The results demonstrate that, utilizing smart hydrogel and shape memory polymer (SMP) can effectively attain large deformation and exhibit variable stiffness due to the shape memory effect. Besides, the shape-morphing structures exhibit customized behaviours including bending, curling, and elongation, all while reducing reliance on external power sources. In conclusion, utilizing multiple smart materials within the model-based design framework offers an efficient approach for developing mechanically intelligent structure capable of complex deformations and variable stiffness, thereby providing support for underwater or offshore engineering applications.

Enhanced interlocking in granular jamming grippers through hard and soft particle mixtures

We investigate the influence of particle stiffness on the grasping performance of granular grippers, a class of soft robotic effectors that utilize granular jamming for object manipulation. Through experimental analyses and X-ray imaging, we show that grippers with soft particles exhibit improved wrapping of the object after jamming, in contrast to grippers with rigid particles. This results in significantly increased holding force through the interlocking. The addition of a small proportion of rigid particles into a predominantly soft particle mixture maintains the improved wrapping but also significantly increases the maximum holding force. These results suggest a tunable approach to optimizing the design of granular grippers for improved performance in soft robotics applications.Graphic abstract

A Crawling Robot with Temperature-Controlled Variable Stiffness Feet: Strong Surface Adaptability

Abstract Existing soft robots have not shown the same level of adaptability on diverse terrains when compared to natural animals that possess variable stiffness characteristics. In this paper, utilizing the mutual transformation between the glassy and molten states of rosin materials, temperature-controlled variable stiffness feet (TVSF) are achieved by filling the rosin into an array of soft cavity villi. A single motor is utilized as the vibration-driven actuator of the robot. It is found that the feet stiffness can be changed ranging from 0.6227 N/mm to 0.9611 N/mm, which has a significant impact on the robot’s velocity and follows a nonlinear relationship. A smooth motion surface requires a high-stiffness foot to provide support and stability for the robot, while a rough motion surface necessitates a low-stiffness foot to reduce friction and resistance allowing for quick movement for the driving voltage of 1.6 V. Furthermore, the TVSF enable stable operation on complex surfaces, including a 30°slope, pipes, and pothole surfaces. This research provides a new technological approach to designing and manufacturing variable stiffness robots with enhanced surface adaptability.

Bioinspired Moisture‐Driven Soft Robotic Actuator

Multifunctional materials in nature, utilizing atmospheric water content, offer solutions to challenges in soft‐actuating materials research. The leaf of Arundo donax, an invasive plant, showcases exceptional properties: structural integrity, passive actuation, tunability, load‐bearing capacity, longevity, and an integrated battery‐free fluid generation and distribution mechanism. These functions coexist within a thin (≈130 μm) and lightweight structure with simple compositions. The study investigates material composition, passive fluid transport, water harvesting structures, and mechanical properties, revealing unexpected correlations between surface and internal structures. Additionally, tunable actuation, suitable for origami‐based mechanisms, is explored. Testing an energy‐free, moisture‐triggered soft robotic gripper, its ability to lift over 50 times its weight while maintaining sufficient softness for delicate tasks is demonstrated. This unit‐wise structure‐based actuation offers promise for soft robotics, with favorable fabrication possibilities and motion manipulation.

Biocompatible PVAc-g-PLLA Acrylate Polymers for DLP 3D Printing with Tunable Mechanical Properties.

The technological advancement of Additive Manufacturing has enabled the fabrication of various customized artifacts and devices, which has prompted a huge demand for multimaterials that can cater to stringent mechanical, chemical, and other functional property requirements. Photocurable formulations that are widely used for Digital Light Processing (DLP)/Stereolithography (SLA) 3D printing applications are now expected to meet these new challenges of hard and soft or stretchable structural requirements in addition to good resolution in multiple scales. Here we present a biocompatible photocurable resin formulation with tunable mechanical properties that can produce hard or stretchable elastomeric 3D printed materials in a graded manner. Acrylate poly(lactic acid) (PLA) grafted polyvinyl acetate (PVAc) polymer was mixed with hydroxyl ethyl methacrylate (HEMA) and hydroxyl ethyl acrylate (HEA) as reactive diluents (50-70 wt %) in various compositions to form a series of photocurable resin formulations. Depending on the nature of the reactive diluent (HEMA or HEA) and their weight percentage, the mechanical properties of the 3D printed parts could be fine-tuned from hard (Tensile strength 20.6 ± 2 MPa, elongation 2 ± 1%) to soft (Tensile strength 1.1 ± 0.2 MPa, elongation 62 ± 8%) materials. The printed materials displayed remarkable dye absorption (95%), showing stimuli-responsive behavior for dye release (with respect to both pH and enzyme), while also demonstrating high cell viability (>90%) for mouse embryonic (WT-MEF) cells and degradability in PBS solution. These biobased 3D printing resins have the potential for a variety of applications, including tissue engineering, soft robotics, dye absorption, and elastomeric actuators.

Digital Light Process 3D Printing of Magnetically Aligned Liquid Crystalline Elastomer Free-forms.

Liquid crystalline elastomers (LCEs) are anisotropic soft materials capable of large dimensional changes when subjected to a stimulus. The magnitude and directionality of the stimuli-induced thermomechanical response is associated with the alignment of the LCE. Recent reports detail the preparation of LCEs by additive manufacturing (AM) techniques, predominately using direct ink write printing. Another AM technique, digital light process (DLP) 3D printing, has generated significant interest as it affords LCE free-forms with high fidelity and resolution. However, one challenge of printing LCEs using vat polymerization methods such as DLP is enforcing alignment. Here, we document the preparation of aligned, main-chain LCEs via DLP 3D printing using a 100 mT magnetic field. Systematic examination isolates the contribution of magnetic field strength, alignment time, and build layer thickness on the degree of orientation in 3D printed LCEs. Informed by this fundamental understanding, DLP is used to print complex LCE free-forms with through-thickness variation in both spatial orientations. The hierarchical variation in spatial orientation within LCE free-forms is used to produce objects that exhibit mechanical instabilities upon heating. DLP printing of aligned LCEs opens new opportunities to fabricate stimuli-responsive materials in form factors optimized for functional use in soft robotics and energy absorption.

Modeling Backlash-like Hysteresis and Feedforward Control of Tendon-Sheath Mechanism Pair with Arbitrary Shape: Excluding Distal Sensory Input

Abstract The tendon-sheath mechanism (TSM) has garnered attention for its versatile applications in soft robotics, robotic hands, and surgical robots due to its adeptness in transmitting force through tortuous and narrow channels. However, precise control of the TSM is challenging due to its shape-dependent deformation and force generation, highlighting the need to address shape variations in dynamic environments where direct measurement is impractical. This paper introduces a novel methodology leveraging the concept of the equivalent circle in TSM to model its shape under varying conditions. The equivalent circle substitutes an arbitrarily shaped TSM with a circle, whose radius is derived from TSM's deformation and forces measured at the proximal end during calibration. This approach provides a concise and effective framework for understanding and analyzing the distinct characteristics of TSM. Additionally, a control strategy is proposed, utilizing the estimated equivalent circle to adapt to dynamic environments. Experimental results demonstrate promising performance without distal end sensory feedback, with mean percentage errors (MPE) of 0.78% and 2.33% for predicting cumulated curve angle and equivalent circle radius, respectively. Moreover, by utilizing the equivalent circle to calculate and feedforward deadband, effective control is achieved. The root mean square error (RMSE) reduced from 1.31 mm to an average of 0.19 mm in two feedforward experiments, representing an average reduction of 85.5%. These findings shed light on effective control strategies for TSM, offering valuable insights for robotics and surgical applications where TSM shape configurations are uncertain and subject to dynamic change.

Optimizing actual PID control for walking quadruped soft robots using genetic algorithms

The construction of soft robots’s models and controllers remains a significant challenge. In this paper, we propose a new walking control method for the quadruped soft robot named genetic algorithm-optimized PID. First, we construct the control model correlating valve voltage with leg bending based on the geometrical analysis. This modeling approach leverages the characteristics of novel leg structure and bend sensor, thereby streamlining the control model for locomotion of quadruped soft robotic. Moreover, We apply the genetic algorithm to automatically tune parameters and optimize PID controllers, aiming to enhance control performance. The application of the proposed method to the walking control has been uniquely demonstrated on a real 3D-printed quadruped soft robot. Experimental results indicate that the genetic algorithm-optimized PID controller significantly improves trajectory tracking compared to the Ziegler-Nichols tuning method. This optimization increases the robot’s walking speed from 5 mm/s to 8 mm/s, reduces the error rate by 2.4064%, decreases overshoot by 12.55%, and shortens response time by 0.5 s, substantially enhancing the controller’s overall performance. Additionally, compared to particle swarm optimization, the proposed method further improves performance by reducing the error rate by 0.4079%, overshoot by 8.4%, and response time by 1.0 s.

The Future of Robotics: Integrating Biological Components in Biohybrid Robotics for Enhanced Functionality and Adaptability

Biohybrid robotics, an emerging interdisciplinary field, combines biological tissues with synthetic robotic systems to create machines that exhibit enhanced functionality, adaptability, and efficiency. By integrating living cells, muscles, or other biological components with engineered structures, biohybrid robots are designed to mimic natural processes and behaviors, offering the potential for significant advancements in soft robotics, medical devices, and autonomous systems. This paper explores the latest developments in biohybrid robotics, focusing on the design principles, challenges in integrating biological and synthetic components, and the potential applications in fields such as healthcare, environmental monitoring, and bioengineering. By leveraging the inherent advantages of biological tissues—such as self-healing, energy efficiency, and adaptive responsiveness—biohybrid robots could outperform conventional robotic systems in tasks that require flexibility, precision, and interaction with dynamic environments. This research also examines the ethical and technical challenges associated with the field, including the sustainability of biological materials and the long-term stability of these systems. The potential for biohybrid robotics to revolutionize industries by blending biological intelligence with synthetic durability underscores the significance of this rapidly evolving technology. Biohybrid robotics not only holds promise for creating more versatile and efficient machines but also represents a major step toward bridging the gap between biology and engineering. By harnessing the unique properties of biological systems, such as their ability to grow, repair, and adapt to changing environments, biohybrid robots can offer solutions to challenges that traditional robotics struggle to address. For example, in the medical field, these robots could assist in developing more effective prosthetics, bio-inspired implants, and even robotic systems that work inside the body to perform tasks with a level of precision and biocompatibility previously unattainable. This research delves into the potential for future advancements in areas such as environmental sustainability, where biohybrid robots could be used for tasks like pollution detection and waste management. Their biological components would enable them to interact with natural ecosystems in more seamless and non-disruptive ways. However, this integration of living tissues with technology also raises important ethical considerations regarding the use of biological materials and the extent to which we can manipulate living organisms for technological purposes. Addressing these challenges will be critical to the successful development and deployment of biohybrid robotics in real-world applications.

A Star‐Nose‐Inspired Bionic Soft Robot for Nonvisual Spatial Detection and Reconstruction

The star‐nosed mole is recognized as a tactilely sensitive mammal due to its unique nose, which facilitates spatial detection in dark environments through touch using its appendages enveloped in numerous sensory receptors. This article introduces a bionic soft robot inspired by the star‐nosed mole, which combines a pneumatic soft platform with a polydimethylsiloxane–polyethylene terephthalate cylindrical tactile sensor array based on bilayer single‐electrode triboelectric nanogenerators, mimicking the muscle tissue of the mole's nose and the cylindrical appendages surrounded by Eimer's organs. The cylindrical sensor array enables multiangle spatial detection without an external power supply and remains unaffected by external materials. By implementing a constant curvature model for robot motion control, positional information is provided for the contact points between the cylindrical sensor array and the external environment. The robot effectively discriminates the distance and shape of various objects and achieves nonvisual 3D spatial detection and reconstruction in real‐world scenarios. This work presents a novel bionic approach for 3D spatial detection in nonvisual environments.