Closed structure soft robotic gripper

Soft robotic grippers possess high structural compliance and adaptability, allowing them to grasp objects with unknown and irregular shapes and sizes. To enable more dexterous manipulation, soft sensors that are similar in mechanical properties to common elastomer materials are desired to be integrated into soft grippers. In this paper, we develop ionic hydrogel-based strain and tactile sensors and integrate these sensors into a three-finger soft gripper for learning-based object recognition and force-controlled grasping. Such hydrogel-based sensors have excellent conductivity, high stretchability and toughness, good ambient stability, and unique antifreezing property; they can be readily attached to a soft gripper at desired locations for strain and tactile sensing. By using a deep-learning model, the sensory soft gripper is demonstrated to be capable of grasping and recognizing objects at both room and freezing temperatures, and achieving close to 100% recognition accuracy for ten typical objects. Moreover, the capacitive tactile feedback of the gripper is utilized to develop a closed-loop force controller and realize force-controlled grasping of fragile or highly deformable objects. A new slip detection and compensation strategy is also proposed and validated for the sensory gripper for adjusting the grasping force in real time upon detecting slippage. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The multimodal sensation of a soft robotic gripper could enrich its grasping functionalities and improve its manipulation performance. This research integrates novel antifreezing ionic hydrogel-based strain and tactile sensors into a three-finger soft robotic gripper for learning-based object recognition and force-controlled grasping. Constructed from a highly stretchable, ambient-stable, and antifreezing ionic hydrogel, the strain and tactile sensors can be readily integrated at the desired locations on the soft gripper, and can reliably operate at both ambient and freezing temperatures with excellent mechanical and electrical properties. Based on the feedback of the strain and tactile sensors, a deep learning model is employed to enable high-accuracy object recognition while grasping, which can be useful for manipulation in vision-free environments. Closed-loop force control and slip compensation strategies are also demonstrated for reliably grasping fragile/deformable objects and handling slip events during the manipulation of heavy objects. The sensory soft gripper and the associated object recognition and force control methods could find practical applications in a variety of robotic manipulation tasks.

Soft compliant gripping is essential in delicate surgical manipulation for minimizing the risk of tissue grip damage caused by high stress concentrations at the point of contact. It can be achieved by complementing traditional rigid grippers with soft robotic pneumatic gripper devices. This manuscript describes a rod-based approach that combined both 3D-printing and a modified soft lithography technique to fabricate the soft pneumatic gripper. In brief, the pneumatic featureless mold with chamber component is 3D-printed and the rods were used to create the pneumatic channels that connect to the chamber. This protocol eliminates the risk of channels occluding during the sealing process and the need for external air source or related control circuit. The soft gripper consists of a chamber filled with air, and one or more gripper arms with a pneumatic channel in each arm connected to the chamber. The pneumatic channel is positioned close to the outer wall to create different stiffness in the gripper arm. Upon compression of the chamber which generates pressure on the pneumatic channel, the gripper arm will bend inward to form a close grip posture because the outer wall area is more compliant. The soft gripper can be inserted into a 3D-printed handling tool with two different control modes for chamber compression: manual gripper mode with a movable piston, and robotic gripper mode with a linear actuator. The double-arm gripper with two actuatable arms was able to pick up objects of sizes up to 2 mm and yet generate lower compressive forces as compared to elastomer-coated and non-coated rigid grippers. The feasibility of having other designs, such as single-arm or hook gripper, was also demonstrated, which further highlighted the customizability of the soft gripper device, and it's potential to be used in delicate surgical manipulation to reduce the risk of tissue grip damage.

Inspired by nature's flexible and adaptable organisms, soft robotics are motorless robots made from highly compliant materials to work in confined environments and manipulate delicate objects. However, soft robots often suffer from early failure because of unexpected damage. At the same time, it is challenging to manufacture the geometrically complex structures of soft robots. This study introduces resins based on deep eutectic solvents (DES) to fabricate a pneumatically driven soft gripper using digital light processing (DLP). The resins consist of choline chloride (ChCl) as a hydrogen bond acceptor, glycerol (Gly), and acrylamide (AAm) as hydrogen bond donors. By utilizing the intense hydrogen bonding within DES, the resin can be rapidly cured by photopolymerization to form tough ionogels without chemical crosslinkers. The DES ionogels exhibit remarkable toughness and self‐healing performance compared to common hydrogels. Furthermore, the ionogels show not only efficient energy‐dissipating behavior but also achieve rapid self‐recovery. Finally, the DLP‐printed soft gripper from the DES‐based resin performs successful actuation and healing of macroscopic damages. This work presents a simple strategy to 3D print a soft robotic gripper with high toughness and self‐healing capability.

With the purpose of making soft robotic structures with embedded sensors, additive manufacturing techniques like fused deposition modeling (FDM) are popular. Thermoplastic polyurethane (TPU) filaments, with and without conductive fillers, are now commercially available. However, conventional FDM still has some limitations because of the marginal compatibility with soft materials. Material selection criteria for the available material options for FDM have not been established. In this study, an open-source soft robotic gripper design has been used to evaluate the FDM printing of TPU structures with integrated strain sensing elements in order to provide some guidelines for the material selection when an elastomer and a soft piezoresistive sensor are combined. Such soft grippers, with integrated strain sensing elements, were successfully printed using a multi-material FDM 3D printer. Characterization of the integrated piezoresistive sensor function, using dynamic tensile testing, revealed that the sensors exhibited good linearity up to 30% strain, which was sufficient for the deformation range of the selected gripper structure. Grippers produced using four different TPU materials were used to investigate the effect of the Shore hardness of the TPU on the piezoresistive sensor properties. The results indicated that the in situ printed strain sensing elements on the soft gripper were able to detect the deformation of the structure when the tentacles of the gripper were open or closed. The sensor signal could differentiate between the picking of small or big objects and when an obstacle prevented the tentacles from opening. Interestingly, the sensors embedded in the tentacles exhibited good reproducibility and linearity, and the sensitivity of the sensor response changed with the Shore hardness of the gripper. Correlation between TPU Shore hardness, used for the gripper body and sensitivity of the integrated in situ strain sensing elements, showed that material selection affects the sensor signal significantly.

Tactile sensing is desirable for skillful object handling in soft robotics applications. Real-time measurement and identification of dynamic shear forces are crucial for slip detection and object interaction. This study proposes a soft sensor with a closed-loop feedback system for dynamic shear force detection to address object slippage in a soft robotic gripper. The sensor is made of a ferroelectric polymer with nanocarbon materials because of the resulting improved crystallinity and good sensitivity. The sensor shows high performance and high-speed response for detecting dynamic shear forces when fragile objects (e.g., vegetables) slip from the soft gripper. The artificial cutaneous sensor shows high sensitivity for grasping such objects with the gripper. Furthermore, the feedback system provides a control system for operations and avoids the need for training for various tasks, thus demonstrating the potential of the proposed system for novel soft robotics applications such as biomimetic electronic skin.

Soft robots have advantages such as low weight and compact size compared to rigid robots. Variable stiffness is one of the key methods of improving the performance of a soft robot. The soft gripper grasps objects of various shapes or sizes based on the variable stiffness. In a previous study, we validated the variable stiffness mechanism where flexible and rigid segments were connected alternately in series. This paper presents a soft variable stiffness gripper that can be used to control the stiffness by pulling tendons. The soft gripper has three variable stiffness structures acting as fingers and the stiffness can be controlled using two motors by winding tendons. To understand the tendency of the stiffness variation and determine the design parameters, a compliant mechanism was developed using a pseudo-rigid-body model (PRBM). The experimental results show that the difference between the lowest and highest stiffness values of the fabricated variable stiffness gripper was 5.6 times the original. Similarly, the difference in the gripping weight was 19 times. Using the experimental results, the variable stiffness gripper can be designed and manufactured based on the required stiffness and used to grip various types of objects.

Soft robotics is an expanding area of research which exploits compliance and adaptability of soft structures to design highly adaptive robots for contact interactions. The ability to 3D-print materials with softer, more elastic materials properties is a recent development and a key enabling technology for the rapid development of soft robots. Among the possible applications, soft cooperative grippers and manipulators have demonstrated a high potential impact. However, retrieving data about mechanical properties of such novel soft materials and information about the parameters to select in the 3D-printers is often not straightforward. The aim of this chapter is to systematically investigate the mechanical properties of 3D-printed specimens from one of the most used soft filament, the Ninjaflex (Lulzbot, USA). In particular, we focus on the characterization of bending stiffness with reference to the selection of different infill density and printing patterns. We also report on how the collected data can be used in the design phase to obtain a desired behaviour of a soft gripper and on repeatability of the 3D-printing process. Finally, we tested with a finger of a cooperative gripper how the different approaches to obtain a given stiffness value affect the flexion/extension trajectory. This work is intended to be a useful tool for researchers who use rapid-prototyping techniques to develop soft grippers and it also serves as a possible guideline to the characterization of the mechanical properties of novel soft filaments.

A soft gripper inspired by the glowing sucker octopus (Stauroteuthis syrtensis)’ highly evolved grasping capability enabled by the umbrella‐shaped dorsal and ventral membrane between each arm is presented here, comprising of a 3D‐printed linkage mechanism used to actuate a modular mold silicone‐casting soft suction disc to deform. The soft gripper grasp can lift objects using the suction generated by the pump in the soft disc. Moreover, the protruded funnel‐shaped end of the deformed suctorial mouth can adapt to smooth and rough surfaces. Furthermore, when the gripper contacts the submerged target objects in a turbid environment, local suctorial mouth arrays on the suction disc are locked, causing the variable flow inside them, which can be detected as a tactile perception signal to the target objects instead of visual perception. Aided by the 3D‐printed linkage mechanism, the soft gripper can grasp objects of different shapes and dimensions, including flat objects, objects beyond the grasping range, irregular objects, scattered objects, and a moving turtle. The results report the soft gripper's versatility and demonstrate the vast application potentials of self‐adaptive grasping and sensing in various environments, including but are not limited to underwater, which is always a key challenge of grasping technology.

Developing non-visual sensing and intelligent recognition technologies is crucial for enhancing the manipulation performance of robots in dim or obstructed environments. Although precise object recognition has been extensively researched for rigid manipulators, the adoption of these techniques in soft robotic systems has been limited by the high modulus or low sensitivity of existing sensors (gauge factor/Young’s modulus<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt;$</tex-math> </inline-formula>10 kPa<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{-1}$</tex-math> </inline-formula>). Meanwhile, these systems necessitate advanced artificial intelligence(AI) algorithms to effectively process multiple sensing data. In this study, we utilize newly developed soft sensors and AI algorithm to establish a biomimetic perceptual soft gripper system capable of sensing and generating category object information during the grasping task. A strain/pressure bimodal sensor, mimicking the exceptional softness (Young’s modulus<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt;$</tex-math> </inline-formula>10 kPa) and high sensitivity (gauge factor<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula>2000) of human skin, has been developed and seamlessly integrated into a three-finger soft robotic gripper. A Swin Transformer network was developed to learn rules from bimodal data acquired from sensors and generate category information of the grasping objects. The perceptual gripper system exhibited superior recognition accuracy compared to previously reported systems, achieving an impressive 94.9% accuracy in categorizing 18 objects of varying shapes and sizes. We believe this advancement unlocks soft machines’ potential for automated applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i>—Non-visual sensing and recognition techniques for soft robotic grippers are valuable for operation in dimly lit or obstructed environments. Traditional rigid sensor have limitations when applied to soft robotic systems due to their high modulus. This research developed novel ultra-soft and high sensitive strain/pressure bimodal sensors integrated into a three-fingered soft robotic gripper. Meanwhile, an AI algorithm applicable to the soft gripper system was developed to process the sensing data and generate the corresponding object category information when grasping the object. With the aid of the developed soft sensor and AI algorithm, the perceptual soft gripper can achieve high-precision object recognition, giving it great potential for automating tasks such as item picking, assembly, and handling in energy-efficient warehouses and logistics centers. The developed soft sensor and AI algorithm co-pilot object recognition strategy can contribute to the advancement of soft machine automation capability technology.

Soft robotic grippers are able to carry out many tasks that traditional rigid-bodied grippers cannot perform but often have many limitations in terms of control and feedback. In this study, a Fin Ray effect inspired soft robotic gripper is proposed with its whole body directly 3D printed using soft material without the need of assembly. As a result, the soft gripper has a light weight, simple structure, is enabled with high compliance and conformability, and is able to grasp objects with arbitrary geometry. A force sensor is embedded in the inner side of the gripper, which allows the contact force required to grip the object to be measured in order to guarantee successful grasping and to provide the most suitable gripping force. In addition, it enables control and data monitoring of the gripper’s operating state at all times. Characterization and grasping demonstration of the gripper are given in the Experiment section. Results show that the gripper can be used in a wide range of scenarios and applications, such as the service robot and food industry.

Soft pneumatic grippers, structures created from soft material like silicone rubber and powered by air pressure, can grip and transfer fragile items safely and stably. These grippers are widely applicable under medical situations, especially during surgery. Other applications include automated laboratory operations, underwater gripping, food delivery, extraterrestrial exploration efforts, etc. However, traditional soft grippers face serious difficulties with gripping objects of different volumes and weights. This study proposed a novel layer-jamming mechanism of tuning the stiffness of soft grippers to increase its weight capacity. The study also proposed a novel bellowed-pipe structure for soft grippers to incorporate dual-mode gripping. The process of gripping was automated through the introduction of a camera and recognition algorithms capable of computing the size and depth of the detected object. Experiments conducted in room conditions, using the variable stiffness mechanism, the gripper could grip objects 120% heavier than without using the mechanism, indicating a significant advancement from traditional soft grippers. Further research could be done with making the gripper smaller and applicable under surgical conditions.

In recent years, many researchers have aimed to construct robotic soft grippers that can handle fragile or unusually shaped objects without causing damage. This study proposes a smart textile-composite actuator and its application to a soft robotic gripper. An active fiber and an inactive fiber are combined together using knitting techniques to manufacture a textile actuator. The active fiber is a shape memory alloy (SMA) that is wire-wrapped with conventional fibers, and the inactive fiber is a knitting yarn. A knitted textile structure is flexible, with an excellent structure retention ability and high compliance, which is suitable for developing soft grippers. A driving source of the actuator is the SMA wire, which deforms under heating due to the shape memory effect. Through experiments, the course-to-wale ratio, the number of bundling SMA wires, and the driving current value needed to achieve the maximum deformation of the actuator were investigated. Three actuators were stitched together to make up each finger of the gripper, and layer placement research was completed to find the fingers’ suitable bending angle for object grasping. Finally, the gripping performance was evaluated through a test of grasping various object shapes, which demonstrated that the gripper could successfully lift flat/spherical/uniquely shaped objects.

One of the avenues for the development of functional gradient additive manufacturing is the creation of four-dimensional (4D) printed structures for soft robotic gripping, achieved by combining fused deposition modeling 3D printing with soft hydrogel actuators. This work proposes a conceptual approach to creating an energy-independent soft robotic gripper, consisting of a modified 3D printed holder substrate made from thermoplastic polyurethane (TPU) and an actuator based on a gelatin hydrogel, allowing programmed hygroscopic deformation without using complex mechanical constructions. The use of a 20% gelatin-based hydrogel imparts soft robotic biomimetic functionality to the structure and is responsible for the intelligent stimulus-responsive mechanical functionality of the printed object by responding to swelling processes in liquid environments. The targeted surface functionalization of thermoplastic polyurethane in an argon–oxygen environment for 90 s, at a power of 100 W and a pressure of 26.7 Pa, facilitates changes in its microrelief, thus improving the adhesion and stability of the swollen gelatin on its surface. In this work, the morphological (scanning electron microscopy and atomic force microscopy), colloidal, chemical (FTIR), physicochemical (swelling), biological (cell biocompatibility), and performance evaluations of the TPU/gelatin gripper were studied. The realized concept of creating 4D printed biocompatible comb structures for macroscopic underwater soft robotic gripping can provide noninvasive local gripping, transport small objects, and release bioactive substances upon swelling in water. The resulting product can therefore be used as a self-powered biomimetic actuator, an encapsulation system, or soft robotics.

PurposeSoft grippers have safer and more adaptable human–machine and environment–machine interactions than rigid grippers. However, most soft grippers with single gripping postures have a limited gripping range. Therefore, this paper aims to design a soft gripper with variable gripping posture to enhance the gripping adaptability.Design/methodology/approachThis paper proposes a novel soft gripper consisting of a conversion mechanism and four spring-reinforced soft pneumatic actuators (SSPAs) as soft fingers. By adjusting the conversion mechanism, four gripping postures can be achieved to grip objects of different shapes, sizes and weights. Furthermore, a quasi-static model is established to predict the bending deformation of the finger. Finally, the bending angle of the finger is measured to validate the accuracy of the quasi-static model. The gripping force and gripping adaptability are tested to explore the gripping performance of the gripper.FindingsThrough experiments, the results have shown that the quasi-static model can accurately predict the deformation of the finger; the gripper has the most significant gripping force under the parallel posture, and the gripping adaptability of the gripper is highly enhanced by converting the four gripping postures.Originality/valueBy increasing the gripping posture, a novel soft gripper with enhanced gripping adaptability is proposed to enlarge the gripping range of the soft gripper with a single posture. Furthermore, a quasi-static model is established to analyze the deformation of SSPA.

With the development of advance materials, soft robotics is being widely applied in various industrial manufacturing chains. One of the prominent applications of soft robotics is soft pneumatic actuator (SPA) gripper which is an essential part in the food industry due to their advantages like high compliance and human-friendly interaction. This study presents a systematic approach to analyze a SPA gripper used in the food industry. The behaviors of SPA gripper are analyzed by using image processing technology. Firstly, the structural geometry of soft gripper model controlled by pneumatic actuators is suggested. Moreover, the geometrical parameters like chamber thickness of the soft gripper is optimized by the combination of finite element method (FEM) modeling and experiment. The working characteristics of the chosen concept based on the chamber parameter of this soft SPA gripper are analyzed.