Artificial Cutaneous Sensing of Object Slippage using Soft Robotics with Closed-Loop Feedback Process.

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.

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.

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.

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.

Biologically inspired or mimetic approaches have been traditionally widely adopted in robotic creative design. Generally, the most considered source for biorobotics and soft robotics are animals. Rarely, plants have been considered as a model of inspiration for innovative engineering solutions owing to their inconspicuous motion principles. In this work, a new type of plant-inspired soft robotic gripper was created by reconstructing and simulating the leaf structure and head formation mechanisms of cabbage. Firstly, according to the research reports of biological morphology and curling development mechanism of cabbage leaves, we determined that the flexible leaf with special hierarchical veins is the biological structure basis of curling formation. In the light of this observation, we designed a cabbage-inspired leaf bionic structure using paper substrate as a leaf body and using polylactic acid (PLA) polymer as leaf veins. This bionic design schema of cabbage leaves first has the advantage of ease of manufacture using 3D printing technology. In addition, due to the shape memory effect of PLA, we can control the reciprocal deformation of bionic leaves by controlling the temperature field. Undoubtedly, when two or more pieces of such bionic leaves are assembled together, they can be considered as a soft robotic gripper. Furthermore, we thoroughly studied the embodiment design of this bionic design concept as well as its 3D printing process. Finally, we built a prototype of this cabbage-inspired soft robotic gripper and successfully performed small objects (e.g., spitballs and cylinders) grasping experiments. Obviously, this research achievement first successfully integrated the bionic structure of plant deformation, direct digital manufacturing, and the control of shape memory materials. Secondly, this work realized the unified design of the structure, function, driving, and fabrication of robotics, and further expanded the family of the bionic robot, especially plant-like or plant-inspired robotic solutions.

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.

This work describes a new kind of soft gripper. Inspiration on nature is usual in Soft Robotics device creation. In this case, the inspiration came from a sea lamprey in order to create a closed structure soft robotic gripper. Usually the grippers have one or more soft actuators that act like fingers. Since it is difficult to know whether the fingers are really grasping an object, it is being proposed a closed structure to deal with this problem. While the proposed gripper involves the object as whole, a homogeneous force is applied on it. In addition to the details of the closed structure actuator manufacture description, two procedure tests are presented: an analysis of load versus pressure function and of the force versus pressure characteristics. At the end, some new research topics to the proposed soft gripper are discussed.

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.

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.

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.

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

Robotic object exploration and identification methods to date have attempted to mimic human Exploratory Procedures (EPs) using complex, rigid robotic hands with multifaceted sensory suites. For applications where the target objects may have different or unknown cross-sectional shapes and sizes (e.g. beam members in truss structures), rigid grippers are not a good option as they are unable to adapt to the target objects. This may make it very difficult to recognise the shape and size of a beam member and the approaching angles which would result in a secure grasp. To best meet the requirements of adaptability and compliancy, a soft robotic gripper with simple exteroceptive force sensors has been designed. This paper experimentally verifies the gripper design by assessing its performance in grasping and adapting to a variety of target beam members in a truss structure. The sensor arrangement is also assessed by verifying that sufficient data is extracted during a grasp to recognise the approaching angle of the gripper. Firstly, the gripper is used to grasp each beam member from various angles of approach and readings from the force sensors are collected. Secondly, the collected sensor data is used to train and then test a range of commonly used classifiers for classification of the angle of approach. Thirdly, the classification results are analysed. Through this process, it is found that the gripper is proficient in grasping the variety of target beam members. Despite the uncertainty in the gripper pose, the sensor data collected from the soft gripper during a grasp is sufficient for classification of the angles of approach.

Soft robotic grippers have an advantage in grasping objects with various shapes as compared to conventional rigid grippers. Soft grippers have been researched to hold objects which are fragile. Recently, researchers have utilized the jamming effect using particles, which makes stiffness varied to hold the objects tightly. In this paper, we propose a lip-inspired soft robotic gripper. This gripper is motivated by animals&#x0027; oral structure, especially from lips. Lips have various functions: holding, re-grasping, sucking in, and spitting objects. This gripper especially focuses on the functions of holding and re-grasping. The lip-pouch is fabricated including granular particles inside for passive particle jamming. This paper describes how the gripper is motivated by lips with explanations of the anatomy and functions of the lip. An explanation of the passive particle jamming effect follows. We validated the capability of the lip-pouch of the gripper with various objects through experiments. Moreover, we demonstrated re-grasping objects with this gripper.