Soft Gripper Research Articles

Soft electroadhesive grippers with variable stiffness and deflection motion capabilities

Soft gripper robots provide superior safety, adaptability, and compliance compared to rigid robots. However, soft grippers must address inadequate stiffness and interference resistance. Soft pneumatic electroadhesion (EA) grippers with variable stiffness are potential options for addressing these difficulties. In this paper, we present a soft bionic gripper (SOBG) that resembles human finger movements, such as bending and deflection, employing pneumatic actuation, and whose stiffness is effectively decoupled from its position through a layer jamming-induced variable stiffness structure. By applying electroadhesive forces, the SOBG can perform complex motion tasks that would typically require a wrist joint, making them simpler to perform than with conventional flexible grippers. In addition, the SOBG can perform one-finger object manipulation to grasp flat, concave, and convex objects. To show the potential for more complex robotic applications, we evaluated each function independently by presenting a demonstration of cap-screwing, a material handling system, and an anti-interference research. The SOBG concept and solution proposed in this study may pave the way for the easy integration of EA into soft robotic systems and promote the wider use of EA technology.

The Enhanced Adaptive Grasping of a Soft Robotic Gripper Using Rigid Supports

Soft pneumatic grippers can grasp soft or irregularly shaped objects, indicating potential applications in industry, agriculture, and healthcare. However, soft grippers rarely carry heavy and dense objects due to the intrinsic low modulus of soft materials in nature. This paper designed a soft robotic gripper with rigid supports to enhance lifting force by 150 ± 20% in comparison with that of the same gripper without supports, which successfully lifted a metallic wrench (672 g). The soft gripper also achieves excellent adaptivity for irregularly shaped objects. The design, fabrication, and performance of soft grippers with rigid supports are discussed in this paper.

Picking food by robot hand with tree-frog like pad in various wet conditions

Achieving stability with less squeeze in picking up wet-soft objects is still challenging for robots. To accomplish this challenge, preventing slippage between robotic grippers and an object is crucial. We used micropatterned pads on robotic grippers to enhance wet adhesion when picking up food items. This paper examines the role of micropattern interfaces in preventing slippage by experimental evaluations, in which soft robotic grippers picked up and released food samples such as tofu, quail egg, coffee jelly, konjac, and jelly under various wet conditions. A micropatterned pad, inspired by the toe pad of a tree-frog, comprises a large number of squared cells that are separated by channels. Normal pads without any micropattern were also made for comparison. Experimental results showed the micropatterned pad required less squeeze force than that of the normal pads, resulting in less deformation of a grasped object such as a piece of tofu. The potential of the micropatterned pad to prevent slippage between a robotic gripper and a fragile deformable object in various wet conditions without a complicated control method was demonstrated, thereby promising wider robotic applications in the food, service, and medical industries.

Hydraulic-driven adaptable morphing active-cooling elastomer with bioinspired bicontinuous phases

The active-cooling elastomer concept, originating from vascular thermoregulation for soft biological tissue, is expected to develop an effective heat dissipation method for human skin, flexible electronics, and soft robots due to the desired interface mechanical compliance. However, its low thermal conduction and poor adaptation limit its cooling effects. Inspired by the bone structure, this work reports a simple yet versatile method of fabricating arbitrary-geometry liquid metal skeleton-based elastomer with bicontinuous Gyroid-shaped phases, exhibiting high thermal conductivity (up to 27.1 W/mK) and stretchability (strain limit >600%). Enlightened by the vasodilation principle for blood flow regulation, we also establish a hydraulic-driven conformal morphing strategy for better thermoregulation by modulating the hydraulic pressure of channels to adapt the complicated shape with large surface roughness (even a concave body). The liquid metal active-cooling elastomer, integrated with the flexible thermoelectric device, is demonstrated with various applications in the soft gripper, thermal-energy harvesting, and head thermoregulation.

Review: Application of 3D Printing Technology in Soft Robots.

Soft robots, inspired by living organisms in nature, are primarily made of soft materials, and can be used to perform delicate tasks due to their high flexibility, such as grasping and locomotion. However, it is a challenge to efficiently manufacture soft robots with complex functions. In recent years, 3D printing technology has greatly improved the efficiency and flexibility of manufacturing soft robots. Unlike traditional subtractive manufacturing technologies, 3D printing, as an additive manufacturing method, can directly produce parts of high quality and complex geometry for soft robots without manual errors or costly post-processing. In this review, we investigate the basic concepts and working principles of current 3D printing technologies, including stereolithography, selective laser sintering, material extrusion, and material jetting. The advantages and disadvantages of fabricating soft robots are discussed. Various 3D printing materials for soft robots are introduced, including elastomers, shape memory polymers, hydrogels, composites, and other materials. Their functions and limitations in soft robots are illustrated. The existing 3D-printed soft robots, including soft grippers, soft locomotion robots, and wearable soft robots, are demonstrated. Their application in industrial, manufacturing, service, and assistive medical fields is discussed. We summarize the challenges of 3D printing at the technical level, material level, and application level. The prospects of 3D printing technology in the field of soft robots are explored.

Direct ink writing of polymer matrix composite with carbon for driving a flexible thermoelectric actuator of shape memory polymer

Carbon nanomaterials and polymer composites that possess excellent electrical and mechanical properties for soft robots have been investigated over decades in multidisciplinary research fields. As considering the incorporation of carbon into polymer composite, several major concerns include the electrical conductivity, elastic module, and manufacturing flexibility that can enable them to satisfy various functions such as actuation and sensing. In this paper, we report a comprehensive study on how to design and fabricate a soft gripper based on carbon polymer matrix composite and multilayer structure through multistep processes of 3D printing on paper. Using commercial conductive additives of graphene and carbon nanotube with polydimethylsiloxane (PDMS), a polymer matrix composite (PMC) was prepared for the ink of direct ink writing (DIW) with percolation conductivity. Additionally, a shape memory polymer (SMP) was utilized for fused deposition modeling (FDM) of a thermal actuator. Incorporating the DIW of PMC with the FDM of SMP on paper, a soft gripper with four fingers was designed and fabricated to show remarkable thermoelectrical actuation for grasping a 50 mm-diameter 10 g-weight spherical ball. In the future, it would provide an alternative approach for the fields of science and engineering, such as nanomaterials and nanotechnology, space applications, sustainable soft robots, and flexible autonomous systems.

Robust Grasping of a Variable Stiffness Soft Gripper in High-Speed Motion Based on Reinforcement Learning.

Industrial robots are widely deployed to perform pick-and-place tasks at high speeds to minimize manufacturing time and boost productivity. When dealing with delicate or fragile goods, soft robotic grippers are better end effectors than rigid grippers due to their softness and safe interaction. However, high-speed motion causes the soft robotic gripper to vibrate, leading to damage of the objects or failed grasping. Soft grippers with variable stiffness are considered to be effective in suppressing vibrations by adding damping devices, but it is quite challenging to compromise between stiffness and compliance. In this article, a controller based on deep reinforcement learning is proposed to control the stiffness of the soft robotic gripper, which can accurately suppress the vibration with only a minor influence on its compliance and softness. The proposed controller is a real-time vibration control strategy, which estimates the output of the controller based on the current operating environment. To demonstrate the effectiveness of the proposed controller, experiments were done with a UR5 robotic arm. For different situations, experimental results show that the proposed controller responds quickly and reduces the amplitude of the oscillation substantially.

Bionic soft robotic gripper with feedback control for adaptive grasping and capturing applications

Bionic soft robotic gripper with feedback control for adaptive grasping and capturing applications

Design and Fabrication of Flexible Composite Tactile Sensors for Robotic Soft Grippers

Design and Fabrication of Flexible Composite Tactile Sensors for Robotic Soft Grippers

Joint angle prediction for a cable-driven gripper with variable joint stiffness through numerical modeling and machine learning

Soft grippers in automation, particularly those with variable joint stiffness, offer promising possibilities for precise manipulation tasks. However, accurately predicting finger joint bending angles in this field poses significant challenges due to the soft and complex nature of the grippers, making modeling and angle prediction difficult. This paper presents the development of a predictive model for precisely controlling bending angles in multi-material printed soft grippers with variable stiffness, which are pivotal for delicate manipulation tasks in automation. In particular, we explore a cable-driven gripper design made of thermoplastic polyurethane and conductive polylactic acid materials, featuring integrated resistive joints for stiffness modulation through controlled Joule heating. A data-driven modeling approach, combining numerical modeling of the gripper and machine learning techniques, was employed for the development of the predictive model. We performed static structural simulations using ANSYS Workbench to measure bending angles under various conditions for developing datasets for model training. In this work, we evaluated several machine learning models such as linear regression, decision tree, and K-nearest neighbor regression models to predict the correlation between temperature, pull distance, and bending angle. The K-nearest neighbor regression model demonstrated the highest accuracy, with a mean absolute error of approximately 11%. These findings underline the importance of precise angle prediction models in enhancing the functionality and reliability of soft grippers, paving the way for their broader application in automation and robotics.

Adaptive pneumatic soft gripper with embedded flexible bending sensor

Purpose The purpose of this paper is to introduce a variable distance pneumatic gripper with embedded flexible sensors, which can effectively grasp fragile and flexible objects. Design/methodology/approach Based on the motion principle of the three-jaw chuck and the pneumatic “fast pneumatic network” (FPN), a variable distance pneumatic holder embedded with a flexible sensor is designed. A structural design plan and preparation process of a soft driver is proposed, using carbon nanotubes as filler in a polyurethane (PU) sponge. A flexible bending sensor based on carbon nanotube materials was produced. A static model of the soft driver cavity was established, and a bending simulation was performed. Based on the designed variable distance soft pneumatic gripper, a real-time monitoring and control system was developed. Combined with the developed pneumatic control system, gripping experiments on objects of different shapes and easily deformable and fragile objects were conducted. Findings In this paper, a variable-distance pneumatic gripper embedded with a flexible sensor was designed, and a control system for real-time monitoring and multi-terminal input was developed. Combined with the developed pneumatic control system, a measure was carried out to measure the relationship between the bending angle, output force and air pressure of the soft driver. Flexible bending sensor performance test. The gripper diameter and gripping weight were tested, and the maximum gripping diameter was determined to be 182 mm, the maximum gripping weight was approximately 900 g and the average measurement error of the bending sensor was 5.91%. Objects of different shapes and easily deformable and fragile objects were tested. Originality/value Based on the motion principle of the three-jaw chuck and the pneumatic FPN, a variable distance pneumatic gripper with embedded flexible sensors is proposed by using the method of layered and step-by-step preparation. The authors studied the gripper structure design, simulation analysis, prototype preparation, control system construction and experimental testing. The results show that the designed flexible pneumatic gripper with variable distance can grasp common objects.

Effect of ionic liquids on structure and electromechanical properties of plasticized polyvinyl chloride (PVC) gels

Polyvinyl chloride (PVC) gels, as electroactive polymers, have various applications in the fields of soft actuators and sensors. The relationship between the structure and electromechanical properties of PVC gels, however, has not been fully understood so far. In this work, the effect of different alkyl chain length of 1-allyl- imidazolium cations of tetrafluoroborate (BF4)-based ionic liquid (IL) on structure, mechanical and dielectric properties, bending actuation, sensing and electroadhesion properties of plasticized polyvinyl chloride/dibutyl adipate (PVC/DBA) gel were investigated. The interaction between DBA and ILs in PVC/DBA/IL gels leads to the disruption of interactions between inter-DBA molecules and subsequent formation of DBA-rich phases with DBA/IL complexes. The number of DBA-rich phases in PVC/DBA/IL gels increases as the alkyl chain length of the cation in IL increases, due to an enhanced interaction between DBA and ILs with an increase in the alkyl chain length of the cation in IL. The Young's modulus of the PVC/DBA gel was slightly reduced, while the dielectric constant significantly increased when IL was added. Additionally, the bending actuation properties and capacitance pressure sensing sensitivity of the PVC/DBA/IL gel increased with an increase in the alkyl chain length of the cation of IL. However, there was an opposite trend observed for electroadhesion peel strength. Among the studied PVC/DBA/IL gels, the bending displacement, pressure sensing sensitivity, and electroadhesion peel strength of PVC/DBA/IL gel with 3.2 wt% of 1-allyl-3-butylimidazolium tetrafluoroborate ([ABIM]BF4) increased by 2.6 times, 7.5 times, and 1.7 times compared to PVC/DBA gel, respectively. These results provide new insights into understanding the relationship between structure and electromechanical properties of PVC gels for their application in actuation, sensing, and electroadhesion integrated soft grippers.

Diversity‐Based Topology Optimization of Soft Robotic Grippers

Soft grippers are ideal for grasping delicate, deformable objects with complex geometries. Universal soft grippers have proven effective for grasping common objects, however complex objects or environments require bespoke gripper designs. Multi‐material printing presents a vast design‐space which, when coupled with an expressive computational design algorithm, can produce numerous, novel, high‐performance soft grippers. Finding high‐performing designs in challenging design spaces requires tools that combine rapid iteration, simulation accuracy, and fine‐grained optimization across a range of gripper designs to maximize performance, no current tools meet all these criteria. Herein, a diversity‐based soft gripper design framework combining generative design and topology optimization (TO) are presented. Compositional pattern‐producing networks (CPPNs) seed a diverse set of initial material distributions for the fine‐grained TO. Focusing on vacuum‐driven multi‐material soft grippers, several grasping modes (e.g. pinching, scooping) emerging without explicit prompting are demonstrated. Extensive automated experimentation with printed multi‐material grippers confirms optimized candidates exceed the grasp strength of comparable commercial designs. Grip strength, durability, and robustness is evaluated across 15,170 grasps. The combination of fine‐grained generative design, diversity‐based design processes, high‐fidelity simulation, and automated experimental evaluation represents a new paradigm for bespoke soft gripper design which is generalizable across numerous design domains, tasks, and environments.

Advanced Technologies and Applications of Robotic Soft Grippers

AbstractThe robotic soft gripper is a significant and expanding area of study. It is primarily a unique gadget that interacts with the external environment in a certain actuating mode by utilizing the properties of flexible materials and biological shape design. Robotic grippers are increasingly needed in the fields of medical operations, auxiliary maintenance, land rescue, underwater grasping, and space manipulation due to the increasing demand for automation. However, conventional rigid gripper is not the ideal option or optimal design for particular activities requiring a high degree of adaptation and interaction. The soft gripper can interact with its surroundings safely and flexibly because it is comprised of flexible materials. Currently, certain technical system has been developed through research on flexible grippers, but it is still in its infancy. This paper reviews the materials and manufacturing technology, actuating technology, and sensing and control technology of flexible mechanical grippers. Finally, several main application areas of soft grippers are discussed from a technical point of view, as well as possible challenges for further development.

A retrofit sensing strategy for soft fluidic robots

Soft robots are intrinsically capable of adapting to different environments by changing their shape in response to interaction forces. However, sensory feedback is still required for higher level decisions. Most sensing technologies integrate separate sensing elements in soft actuators, which presents a considerable challenge for both the fabrication and robustness of soft robots. Here we present a versatile sensing strategy that can be retrofitted to existing soft fluidic devices without the need for design changes. We achieve this by measuring the fluidic input that is required to activate a soft actuator during interaction with the environment, and relating this input to its deformed state. We demonstrate the versatility of our strategy by tactile sensing of the size, shape, surface roughness and stiffness of objects. We furthermore retrofit sensing to a range of existing pneumatic soft actuators and grippers. Finally, we show the robustness of our fluidic sensing strategy in closed-loop control of a soft gripper for sorting, fruit picking and ripeness detection. We conclude that as long as the interaction of the actuator with the environment results in a shape change of the interval volume, soft fluidic actuators require no embedded sensors and design modifications to implement useful sensing.

Leveraging pleat folds and soft compliant elements in inflatable fabric beams.

Inflatable fabric beams (IFBs) integrating pleat folds can generate complex motion by modifying the pleat characteristics (e.g., dimensions, orientations). However, the capability of the IFB to return to the folded configuration relies upon the elasticity of the fabrics, requiring additional pressure inputs or complementary mechanisms. Using soft compliant elements (SCEs) assembled onto pleat folds is an appealing approach to improving the IFB elasticity and providing a range of spatial configurations when pressurized. This study introduces an actuator comprising an IFB with pleat folds and SCEs. By methodologically assembling the SCEs onto the pleat folds, we constrain the IFB unfolding to achieve out-of-plane motion at 5 kPa. Besides, the proposed actuator can generate angular displacement by regulating the input pressure ( 5 kPa). A matrix-based representation and model are proposed to analyze the actuator motion. We experimentally study the actuator's angular displacement by modifying SCE shapes, fold dimensions, and assembly distances of SCEs. Moreover, we analyze the effects of incorporating two SCEs onto a pleat fold. Our results show that the actuator motion can be tuned by integrating SCEs with different stiffness and varying the pleat fold dimensions. In addition, we demonstrate that the integration of two SCEs onto the pleat fold permits the actuator to return to its folded configuration when depressurized. In order to demonstrate the versatility of the proposed actuator, we devise and conduct experiments showcasing the implementation of a planar serial manipulator and a soft gripper with two grasping modalities.

Haptic Localization with a Soft Whisker from Moment Readings at the Base.

This article focuses on haptic localization of very lightweight and delicate objects while applying a contact force >5000 times lower than the weight of the object. A soft whisker integrated with a Force/Moment (F/M) sensor at the base, and a novel reconstruction algorithm have been proposed for this purpose. Initially, the mathematical relationships between the deformations of the whisker and the F/M sensor outputs were used to reconstruct the shape of the whisker and the position of the touched object. The Cosserat rod theory was used under the assumption that only one contact point occurs during the exploration, and friction effects are negligible. A new methodology we called moment only reading (MOR) has been tested, verified, and compared with previous methods that employed Force and Moment Readings (FMR). Experimental investigations revealed that the spatial position estimation error of the MOR method was confined within 13 mm, when the force applied ranged between 0.001 and 0.01 N. Moreover, the comparison with FMR demonstrated that MOR is capable of retrieving the position of objects even when the force readings drop below the force resolution of the sensor. Eventually, the MOR method has been applied to demonstrate the localization and grasping, with a soft gripper, of delicate crops like tomatoes and strawberries.

Experimental assessment and prediction of design parameter influences on a specific vacuum-based granular gripper

Innovative soft robotic grippers, such as granular grippers, enable the automated handling of a wide spectrum of different geometries, increasing the flexibility and robustness of industrial production systems. Granular grippers vary in their design as well as in their configuration, which affects the specific characteristics and capabilities regarding grippable objects. Relevant aspects are the selection of granulates and membranes, as they affect the deformability. This influences the achievable gripping forces, which vary with the gripped objects geometry. On the basis of experimental studies, the modeling of interpolations as well as through experimental validations, the present research investigates the influences of different configurations on the achievable gripping forces for a specific concept of an innovative vacuum-based granular gripper. Specifically, the focus lies on design as well as configuration parameters, which could influence the achievable gripping force. Influencing parameters are determined based on a literature review of similar gripping concepts. Various adjustment possibilities are identified, such as materials of granulates or membranes. The possible configuration options are experimentally analyzed with a one-factor-at-a-time approach. The possibility of modelling the effects of their interrelations on the achievable gripping force is examined with approaches for linear models and compared to interpolations based on Machine Learning. Especially the granulate filling level and the membrane configuration exhibit the largest influences, which were best predicted with the approach based on artificial neural networks. A selection of an optimized gripper configuration for a specified object set as well as possible further developments such as a continuous expandability of the approaches and integrations with simulations are discussed. As a result of these analyses, this research provides methodologies for an optimized selection of a gripper configuration for an improved object-specific achievable gripping force and allows for more efficient handling processes with the examined type of vacuum-based granular gripper.

Optimizing surface roughness in soft pneumatic gripper fabricated via FDM: experimental investigation using Taguchi method

Purpose In this study, experimental studies were carried out using different process parameters of the soft pneumatic gripper (SPG) fabricated by the fused deposition modeling method. In the experimental studies, the surface quality of the gripper was examined by determining four different levels and factors. The experiment was designed to estimate the surface roughness of the SPG.Design/methodology/approach The methodology consists of an experimental phase in which the SPG is fabricated and the surface roughness is measured. Thermoplastic polyurethane (TPU) flex filament material was used in the fabrication of SPG. The control factors used in the Taguchi L16 vertical array experimental design and their level values were determined. Analysis of variance (ANOVA) was performed to observe the effect of printing parameters on the surface quality. Finally, regression analysis was applied to mathematically model the surface roughness values obtained from the experimental measurements.Findings Based on the Taguchi signal-to-noise ratio and ANOVA, layer height is the most influential parameter for surface roughness. The best surface quality value was obtained with a surface roughness value of 18.752 µm using the combination of 100 µm layer height, 2 mm wall thickness, 200 °C nozzle temperature and 120 mm/s printing speed. The developed model predicted the surface roughness of SPG with 95% confidence intervals.Originality/value It is essential to examine the surface quality of parts fabricated in additive manufacturing using different variables. In the literature, surface roughness has been examined using different factors and levels. However, the surface roughness of a soft gripper fabricated with TPU material has not been examined previously. The surface quality of parts fabricated using flexible materials is very important.

Bioinspired Multifunctional Self-Sensing Actuated Gradient Hydrogel for Soft-Hard Robot Remote Interaction

HighlightsThe bioinspired self-sensing actuated gradient hydrogel was developed by a wettability-based method via precipitation of MoO2 nanosheets.Self-sensing actuated gradient hydrogel combined ultrafast thermo-responsive actuation (21° s–1), exceptional photothermal efficiency (3.7 °C s–1) and high sensing properties (GF = 3.94).The first self-sensing remote interaction system based on gradient hydrogel actuators and robotic hands was constructed.