Soft Robotic Gripper Research Articles

Bioinspired design and validation of a soft robotic end-effector with integrated SMA-driven suction capabilities.

The exploration of adaptive robotic systems capable of performing complex tasks in unstructured environments, such as underwater salvage operations, presents a significant challenge. Traditional rigid grippers often struggle with adaptability, whereas bioinspired soft grippers offer enhanced flexibility and adaptability to varied object shapes. In this study, we present a novel bioinspired soft robotic gripper integrated with a shape memory alloy (SMA) actuated suction cup, inspired by the versatile grasping strategies of octopus arms and suckers. Our design leverages a tendon-driven composite arm, enabling precise bending and adaptive grasping, combined with SMA technology to create a compact, efficient suction mechanism. We develop comprehensive kinematic and static models to predict the interaction between arm bending deflection and suction force, thereby optimizing the gripper's performance. Experimental validation demonstrates the efficacy of our integrated design, highlighting its potential for advanced manipulation tasks in challenging environments. This work provides a new perspective on the integration of bioinspired design principles with smart materials, paving the way for future innovations in adaptive robotic systems.&#xD.

A novel approach to enhancing smart stiffness of soft robotic gripper fingers for wider grasping capability

AbstractThis paper presents a proposed design of soft gripper fingers with adjustable stiffness that could be employed in the applications requiring adaptable and stable grasping. The main idea is to combine the under-actuated cable driven mechanism of a soft gripper finger with particle and layer jamming mechanisms to create a new grasping function with variable stiffness for different manipulation requirements. The movement of the soft gripper finger is produced by a cable-driven mechanism. However, particle and layer jamming chambers were embodied as a variable stiffness mechanism for the variable stiffness function. A single soft gripper finger module was developed and tested with particle and layer jamming chamber attached to it. The stiffness and response time of the soft gripper finger were measured in three distinct configurations: single finger module, particle jamming chamber attached to the finger, and layer jamming chamber attached to the finger. The comparison reveals that combining a soft finger with particle jamming increased performance by 20% compared to using the soft finger alone, while combining it with layer jamming led to an 80% increase. Additionally, layer jamming combined with a soft finger showed a 28% increase compared to particle jamming combined with a soft finger. Furthermore, simulation of the soft finger was conducted to estimate the deflection of the soft gripper finger under various applied forces. Moreover, proposed closed loop smart stiffness mechanism for the soft gripper was modeled and simulated by evaluating both soft and hard objects and simulation results were obtained for different cases. The findings indicated that the stiffness of the soft gripper finger can be adjusted for different grasping requirements.

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.

A magneto-elastica reinforced elastomer makes soft robotic grippers

Soft robotics represents a burgeoning and interdisciplinary domain characterized by diverse research avenues encompassing actuation strategies, materials science, fabrication techniques, morphing mechanisms, and control methods. This work introduces a novel approach to soft actuators and robotic grippers by intricately exploring the magneto-elastica of spherical magnet chains. First, a kind of magneto-elastica reinforced elastomer is achieved by embedding millimetre-sized spherical neodymium-iron-boron (NdFeB) magnets into the polymer elastomer. By modulating the coupling effect of the magnetic and elastic contributions, the bending deformation of the magneto-elastica reinforced elastomer can be initialized. Subsequently, we analytically derive the coupled stiffness formula to integrate the discrete-scale magneto-elastica with the continuum elasticity using the transformed section method. For comparison, we further employ the multi-physics coupling simulation method to reveal the complex magnetics-mechanics interactions within the composite beam. As a proof of concept, we demonstrate two distinct configurations of rope-motor-driven robotic grippers respectively: three-finger soft robotic gripper and five-finger soft anthropomorphic hand. Pick-and-place experiments indicate that they have the good applicability to grasp objects with various sizes, surfaces and forms, including delicate, deformable, and irregularly shaped objects. In conclusion, the proposed method offers compelling advantages, including structural simplicity and modularity, facile manufacturability utilizing commonplace materials and 3D printing, in-situ active magneto-elastica-driven resilience and selectable actuation modalities via either magnetic or mechanical stimuli. We anticipate that the exploratory research can potentially manifest further contributions to soft actuators and soft robots.

Optimizing the grasping behavior of a soft robot's gripper

AbstractIn this study, smart composite materials based on 4D printing technology were used to determine the grasping ability of four‐claw gripper of soft robot. First, fused deposition modeling (FDM) is used to prepare polylactic acid (PLA) strips on the surface of a rectangular bamboo substrate to create a single‐claw gripper for a smart composite soft robot, while using stimulus (heat) to deform and recover its original shape. Here, the authors first evaluate the deformation and recovery angle of the single‐claw gripper of the smart composite soft robot, as well as various processing parameters such as heating temperature and time. This study then used FDM to print PLA ribbons on cross‐shaped bamboo sheets to design the four‐claw gripper of soft robot and its gripper technology was actuation type. Then, the four‐claw gripper of soft robot was used to grab the table tennis ball to test its grasping ability. Finally, four processing parameters (width of claw, pitch of PLA ribbon, heating temperature, and heating time) were applied to determine the grasping ability of the four‐claw gripper of soft robot. The optimal processing parameters for the grasping ability of the four‐claw gripper of soft robot were width of claw of 20.2 mm, pitch of PLA ribbon of 1 mm, heating temperature of 200°C, and the heating time of 15 s. The authors also applied the operation window of two processing parameters to discuss the success of the grasping ability of the four‐claw gripper of soft robot. The innovation of this study is the use of PLA/bamboo composite materials as the four‐claw gripper of soft robot and these materials are cheap, easy to obtain, and can be recycled naturally. This study uses a simple thermal stimulus to enable the four‐claw gripper of soft robot to easily grasp irregular objects.Highlights The innovative composite material of polylactic acid and bamboo fabricated by 3D printing. Width of claw and heating temperature are the key factors on grasping ability. Operating window of grasping ability appears on four‐claw gripper of soft robot.

Flexible Triboelectric Sensor based on Catalyst‐Diffusion Self‐Encapsulated Conductive Liquid‐Metal‐Silicone Ink for Somatosensory Soft Robotic System

AbstractThe combination of fluidity and metallic conductivity has attracted considerable attention to liquid metal (LM), but its development remains challenging due to enormous surface tension. Here, vinyl‐terminated silicone oil and platinum catalyst are added to LM to reduce its surface tension, which develops a special type of liquid‐metal‐silicone (LMS) ink with a catalyst diffusion effect. Combined with an embedded three‐dimentional (3D) printing method, the LMS ink is printed on the support matrix, and the catalyst diffuses outward along the print path to cure the silicone around it, directly constructing self‐encapsulated conductive composites with excellent conductivity and self‐encapsulated flexible tactile sensors based on triboelectric nanogenerator (TENG). The sensor exhibits excellent sensitivity (0.308 V kPa−1), high linearity (≈0.99), and good durability (over 10 000 cycles). Furthermore, when used in flexible wearable electronics, the sensor demonstrates a good performance with an accuracy of ≈96% in classifying different human postures using a convolutional neural network. Finally, through embedded 3D printing with LMS ink and silicone ink, a somatosensory soft robotic gripper with complex cavity structures is designed and manufactured in one step, achieving the all‐in‐one integration of sensors and actuators. This study shows great application potential in flexible electronics and soft robotic systems.

Spring-reinforced pneumatic actuator and soft robotic applications

Abstract Compared to rigid-structure robots, soft robots possess higher degrees of freedom and stronger environmental adaptability, which has aroused increasing attention in the robotic field. Among them, soft pneumatic robots have excellent performances in various practical applications. However, the nonlinearity and instability of pressure response of soft actuators caused by lateral expansion come to a great challenge. To address this problem, we proposed to embed a spring constraint layer around each single air chamber. Following the design concept, we obtained single-cavity and multi-DoF pneumatic actuators and evaluated their elongation and bending characteristics. Experimental results demonstrated that our proposed actuators have more linear pressure response as well as higher consistency. Eventually, through robotic applications, including soft robotic hand and gripper our proposed actuators could facilitate flexible manipulation and elaborate performance.

A soft robotic system imitating the multimodal sensory mechanism of human fingers for intelligent grasping and recognition

The sensory function is crucial for achieving precise feedback control and environmental interaction in soft robots. Therefore, the multimodal sensory capabilities that mimic human fingers have always been a research hotspot. This study proposes a design method for a soft robotic gripper that can emulate the multimodal perception mechanism of human fingers and achieve high-precision recognition of grasped objects using machine learning algorithms. In addition, a finger texture structure inspired by human fingerprints is designed using a negative pressure-induced buckling method to enhance the soft gripper's ability to perceive minute surface textures. Inspired by slow adapting (SA) receptors and fast adapting (FA) receptors of human fingers, we have innovatively designed a capacitive curvature sensor (CS) and a triboelectric texture sensor (TS) to detect the size, material, and texture of objects. Furthermore, a non-contact medium identification sensor (MIS), having a sensitivity of more than 1300 times higher compared to the traditional MISs, is fabricated using the principle of electromagnetic resonance. It is shown that by employing a deep learning-based multi-channel feature fusion network, the soft grasping system with multimodal sensing has achieved a recognition accuracy of 98.43 % for different objects. This work provides an innovative approach for the application of soft robots in various fields, such as logistics sorting and food safety.

Bending Dielectric Elastomer Actuators

Dielectric Elastomer Actuators (DEAs) are a type of electroactive polymers that transform electric energy into mechanical work and are capable of large strains and high energy densities. DEAs are often described as “artificial muscles” because they exhibit strains, energy density, and responsiveness similar to natural human muscles. Their applications as soft robotic grippers, artificial arms, underwater robots, and aerial robots have previously been demonstrated. Single-layer DEAs have a simple design, with a dielectric layer sandwiched between compliant electrodes. However, by alternating positive and negative electrodes, multilayer DEAs can exhibit larger forces and strains than single-layer DEAs. DEAs are actuated by applying voltage and forming an electric field, generating a Maxwell stress between the electrodes. The process of fabricating a multilayer DEA consists of spin coating and curing layers of elastomer, stamping carbon nanotubes (CNT), and making connections to the electrodes. By alternating positive and negative electrodes, multilayer DEAs are constructed, which can exhibit larger forces and strains than single-layer DEAs. When electrically activated, DEAs expand laterally; however, by fabricating a bimorph DEA actuator, we demonstrate bending DEAs. A bimorph DEA consists of a multilayer DEA layer attached to a thicker inactive elastomer layer. When the bimorph is actuated, only the DEA layer expands, causing the actuator to bend. We study the relationship between the bending and stiffness of the inactive layer. By changing the shape of the DEA to a triangle or wing-like shape, we also emulate the high-frequency flapping of birds. Work begun on characterizing the voltage-controlled deflection of bimorph DEAs and understanding the mechanics of the bending of a simple beam in terms of forces at Harvard University will be continued on the Navajo Reservation at Navajo Technical University.

Multi-Degree-of-Freedom Force Sensor Incorporated into Soft Robotic Gripper for Improved Grasping Stability.

In recent years, soft robotic grippers have emerged as a promising solution for versatile and safe manipulation of objects in various fields. However, precise force control is critical, especially when handling delicate or fragile objects, to avoid excessive grip force application or to prevent object slippage. Herein, we propose a novel three-degree-of-freedom force sensor incorporated within a soft robotic gripper to realize stable grasping with force feedback. The proposed optical sensor employs lightweight and compact optical fibers, thereby allowing for cost-effective fabrication, and a robust sensing system that is immune to electromagnetic fields. By innervating the soft gripper with optical fibers, a durable system is achieved with the fibers functioning as a strengthening layer, thereby eliminating the need for embedding an external stiffening structure for efficient bending actuation. The innovative contact-based light loss sensing mechanism allows for a robust and stable sensing mechanism with low drift (<0.1% over 9000 cycles) that can be applied to soft pneumatic bending grippers. We used the developed sensor-incorporated soft gripper to grasp various objects, including magnetic materials, and achieved slip detection along with grip force feedback without any signal interference. Overall, this study proposes a robust measuring multi-degree-of-freedom force sensor that can be incorporated into grippers for improved grasping stability.

An effective nonlinear dynamic formulation to analyze grasping capability of soft pneumatic robotic gripper

ABSTRACT Soft pneumatic robotic grippers have found extensive applications across various engineering domains, which prompts active research due to their splendid compliance, high flexibility, and safe human-robot interaction over conventional stiff counterparts. Previously simplified rod-based models principally focused on clarifying overall large deformation and bending postures of soft grippers from static or quasi-static perspectives, whereas it is challenging to elaborate grasping characteristics of soft grippers without considering contact interaction and nonlinear large deformation behaviors. To address this, based on absolute nodal coordinate formulation (ANCF), comprehensively allowing for structural complexity, geometric, material and boundary nonlinearities, and incorporating Coulomb’ friction law with a multiple-point contact method, we put forward an effective nonlinear dynamic modeling approach for exploring grasping capability of soft gripper. Moreover, we solved the established dynamic equations using Generalized-α scheme, and conducted thorough numerical simulation analysis on a three-jaw soft pneumatic gripper (SPG) in terms of grasping configurations, displacements and contact forces. The proposed dynamic approach can accurately both describe complicated deformed configurations along with stress distribution and provide a feasible solution to simulate grasping targets, whose effectiveness and precision were analyzed theoretically and verified experimentally, which may shed new light on devising and optimizing other multifunctional SPGs.

3D‐Printed Mechano‐Optic Force Sensor for Soft Robotic Gripper Enabled by Programmable Structural Metamaterials

Rapid deployment of automation in today's world has opened up exciting possibilities in the realm of design and fabrication of soft robotic grippers endowed with sensing capabilities. Herein, a novel design and rapid fabrication by 3D printing of a mechano‐optic force sensor with a large dynamic range, sensitivity, and linear response, enabled by metamaterials‐based structures, is presented. A simple approach for programming the metamaterial's behavior based on mathematical modeling of the sensor under dynamic loading is proposed. Machine learning models are utilized to predict the complete force–deformation profile, encompassing the linear range, the onset of nonlinear behavior, and the slope of profiles in both bending and compression‐dominated regions. The design supports seamless integration of the sensor into soft grippers, enabling 3D printing of the soft gripper with an embedded sensor in a single step, thus overcoming the tedious and complex and multiple fabrication steps commonly applied in conventional processes. The sensor boasts a fine resolution of 0.015 N, a measurement range up to 16 N, linearity (adj. R2–0.991), and delivers consistent performance beyond 100 000 cycles. The sensitivity and range of the embedded mechano‐optic force sensor can be easily programmed by both the metamaterial structure and the material's properties.

A Review of Somatic Design for Soft Robotic Grippers: From Parts Integration to Functional Synergy

Somatic design is crucial for soft grippers to emulate the embodied intelligence of their biological counterparts, due to the blurry boundaries among material, structure, and function. There are five critical parts in somatic design, including morphology, material, fabrication, actuation, and variable stiffness. The strong nonlinear coupling among these factors often leads to mutual influence and functional compromise after integration. Herein, methods and strategies harnessed in these parts for performance improvement of soft grippers are systematically reviewed, particularly clarifying how to exert the coupling enhancement effects of the somatic parts for functional synergy. Finally, the remaining challenges and the future development directions of soft grippers for organism‐like intelligence and wide‐range applications are discussed.

Versatile 3D-printed fin-ray effect soft robotic fingers: lightweight optimization and performance analysis

Fin ray soft robotic fingers are inspired by the structure and movement of fish fins, enabling flexible and adaptive grasping capabilities. Addressing the challenges of resource efficiency in terms of reduced energy consumption and material expense, this work focuses on further optimizing inherently low-energy fin-ray fingers towards lightweight design. Soft grippers are used frequently in dynamically changing environments and have become inevitable in handling tasks for delicate objects. However, these grippers generally show limited performance and payload-carrying capacity in high-force application scenarios. To address these limitations, topology optimization technique is used here to obtain both gripping capabilities and high factor of safety (FOS) of fingers. The performance of various structures of fin-ray and optimized fingers are analyzed: rectangular, trapezoidal, straight struts, and inclined struts for angles + 45°, − 45°. The topologically optimized structure has 15.2% less mass compared to considered fin-ray finger’s average mass. The deflection coefficient (Cd) is calculated to select the best structure of the fingers based on grasping scenario, and its value should be minimum. The straight strut finger with thickness of t = 2 mm shows best wrapping capabilities compared to all fingers with Cd = 0.1574. The topologically optimized finger’ Cd = 0.1896 at volume fraction of 0.1. Even though the Cd is slightly higher, its FOS is 1.71 times higher. An experimental setup is developed to validate the simulation results with the help of a UR3e robotic arm and an AXIA80 force sensor. The grasping demonstration of soft robotic gripper is performed on various objects: coffee cup and wooden block.

A finger-inspired pneumatic network actuator based on rigid-flexible coupling structure for soft robotic grippers

A finger-inspired pneumatic network actuator based on rigid-flexible coupling structure for soft robotic grippers

Natural ceramic clay and cucurbituril guest-host reconfiguration for smart light, humidity, solvent and temperature responsiveness

Aiming to endow natural minerals with fast, high-sensitivity, and controllable smart light, humidity, solvent and temperature responsiveness, a novel ceramic clay and cucurbituril (CB) guest-host system (MVCB) inspired by nature was self-assembled and rearranged via an ingenious and simple design successfully. For the first time, the multi-functional MVCB not only displayed a high intelligent multi-stimuli responsiveness as light, humidity, solvent, and temperature switches, but also had multiple selective molecular recognition responses because of different organic vapor with CB and asymmetrical bilayer clay. For instance, due to the factors of guest-host and slow release, the average response speed of self-assembled shuffling the MVCB in methanol was as high as 108°/s compared with bilayer film without CB (20°/s). Besides, the MVCB exhibited controllable swift movement and was bending for grabbing three times its own weight. Moreover, the ultra-wide operating temperature of the MVCB from was ranging −196 to 300 °C under extreme condition. More interestingly, the MVCB presented a unique and compliable self-driven, smart switch, shape memory, environmental awareness and soft robotic gripper owing to the asymmetrical swelling and supramolecular of the CB. This work not only brings a simple, cost-effective, large-scale, and efficient strategy for multi-functionalized natural minerals materials, but also opens potential applications for intelligent response, extremely resistant substances, energy conversion and soft robotics.

Electronic skin with shape sensing and Bending-Insensitive pressure sensing for soft robotic grippers object recognition

Soft robotic grippers rely on accurate contact surface information to perform gripping and sorting tasks safely. Difficulty in obtaining accurate contact surface information for recognizing objects with a single type of sensor hinders the advanced capabilities of soft robotic grippers. To obtain contact surface information more accurately for object recognition, in this paper a shape-pressure dual-mode electronic skin for contact surface detection of soft robotic grippers is proposed. It consists of pressure sensing units and curvature sensing units. The pressure sensing unit is insensitive to curvature through an ingenious structural design. It can detect pressure in the range of 0–––7.5 N with a measurement error of 3.92 %. The curvature sensing unit can detect curvature in the range of 0–––0.5 cm−1 with a sensitivity of 63.66 % / cm-1. The experimental results show that the electronic skin can help the soft robotic gripper obtain information on the contact surface. The electronic skin is expected to accelerate the application of soft robots in advanced tasks such as recognition and classification.

Reticular Origami Soft Robotic Gripper for Shape-Adaptive and Bistable Rapid Grasping.

The top-down approach in designing and fabricating origami robots could achieve far more complicated functions with compliant and elegant designs than traditional robots. This study presents the design, fabrication, and testing of a reticular origami soft robotic gripper that could adapt to the shape of the grasping subject and grasp the subject within 80 ms from the trigger instance. A sensing mechanism consisting of the resistive pressure sensor array and flexible elongation sensor is designed to validate further the shape-adaptive grasping capability and model the rough shape and size of the subject. The grasping test on various objects with different shapes, surface textures, sizes, and living animals further validates the excellent grasping capabilities of the gripper. The gripper could be either actively triggered by actuation or passively triggered by a minimum of 0.0014 J disturbance energy. Such features make it particularly suitable for applications such as capturing underwater creatures and illegal drone control.

Friction Enhancement Through Fingerprint-like Soft Surface Textures in Soft Robotic Grippers for Grasping Abilities

Friction Enhancement Through Fingerprint-like Soft Surface Textures in Soft Robotic Grippers for Grasping Abilities

A Sensorized Soft Robotic Hand with Adhesive Fingertips for Multimode Grasping and Manipulation.

Soft robotic grippers excel at achieving conformal and reliable contact with objects without the need for complex control algorithms. However, they still lack in grasp and manipulation abilities compared with human hands. In this study, we present a sensorized multi-fingered soft gripper with bioinspired adhesive fingertips that can provide both fingertip-based adhesion grasping and finger-based form closure grasping modes. The gripper incorporates mushroom-like microstructures on its adhesive fingertips, enabling robust adhesion through uniform load shearing. A single fingertip exhibits a maximum load capacity of 4.18 N against a flat substrate. The soft fingers have multiple joints, and each joint can be independently actuated through pneumatic control. This enables diverse bending motions and stable grasping of various objects, with a maximum load capacity of 28.29 N for three fingers. In addition, the soft gripper is equipped with a kirigami-patterned stretchable sensor for motion monitoring and control. We demonstrate the effectiveness of our design by successfully grasping and manipulating a diverse range of objects with varying shapes, sizes, and curvatures. Moreover, we present the practical application of our sensorized soft gripper for remotely controlled cooking.