Soft Robotic Gripper Research Articles

Bioinspired Soft Robotic Fingers with Sequential Motion Based on Tendon-Driven Mechanisms.

The conformability is yet a challenge for most soft robotic grippers due to the continuous motion and deformation of these machines under external force. Herein, inspired by the movement mechanism of human fingers, we propose a novel tendon-driven soft robotic finger with a preprogrammed bending configuration and a human finger like sequential motion that can be obtained by matching the stiffness gradient of the finger joints with three-dimensional (3D) printing technology. The contents of this article are organized as follows. First, the effect of the anisotropy caused by 3D printing filling direction on the mechanical property is investigated by tensile test. Then, kinematic, stiffness, and fingertip trajectory models are established to analyze the influence of the cross-section thickness and width on the bending and bearing capacity of the finger joint. Furthermore, several experiments are conducted on a self-built experimental platform to evaluate the advantages of sequential motion induced by stiffness gradients. Results reveal that soft robotic fingers with sequential motion show excellent conformability on the object surfaces with various curvatures and outperform nonsequential motion fingers with larger envelop range. Without changing motion trajectories of the fingertip, the deformability of the finger can be tuned by adjusting only the stiffness of the joint. Besides, a two-finger gripper is developed, which presents the capability of grasping objects with different shapes and weights in practical applications. The sequential motion mechanism proposed in this study shows promising potential in soft grippers and robotic design.

A Deep Learning Method for Vision Based Force Prediction of a Soft Fin Ray Gripper Using Simulation Data.

Soft robotic grippers are increasingly desired in applications that involve grasping of complex and deformable objects. However, their flexible nature and non-linear dynamics makes the modelling and control difficult. Numerical techniques such as Finite Element Analysis (FEA) present an accurate way of modelling complex deformations. However, FEA approaches are computationally expensive and consequently challenging to employ for real-time control tasks. Existing analytical techniques simplify the modelling by approximating the deformed gripper geometry. Although this approach is less computationally demanding, it is limited in design scope and can lead to larger estimation errors. In this paper, we present a learning based framework that is able to predict contact forces as well as stress distribution from soft Fin Ray Effect (FRE) finger images in real-time. These images are used to learn internal representations for deformations using a deep neural encoder, which are further decoded to contact forces and stress maps using separate branches. The entire network is jointly learned in an end-to-end fashion. In order to address the challenge of having sufficient labelled data for training, we employ FEA to generate simulated images to supervise our framework. This leads to an accurate prediction, faster inference and availability of large and diverse data for better generalisability. Furthermore, our approach is able to predict a detailed stress distribution that can guide grasp planning, which would be particularly useful for delicate objects. Our proposed approach is validated by comparing the predicted contact forces to the computed ground-truth forces from FEA as well as real force sensor. We rigorously evaluate the performance of our approach under variations in contact point, object material, object shape, viewing angle, and level of occlusion.

A Novel Soft Robotic Hand Design With Human-Inspired Soft Palm: Achieving a Great Diversity of Grasps

Soft robotic hands and grippers are increasingly attracting attention as robotic end effectors. Compared with their rigid counterparts, they are safer for human-robot and environment-robot interactions, easier to control, and more compliant, and they cost and weigh less. Design studies of soft robotic hands have focused mostly on the soft fingers and bending actuators. However, the palm is also an essential part in grasping. In this work, we propose a novel design for an inexpensive soft humanoid hand with pneumatic soft fingers and a soft palm. The configuration of the soft palm is based on a modular design that can be applied to actuate different kinds of existing soft fingers. The splaying of the fingers, bending of the whole palm, and abduction and adduction of the thumb are implemented by the palm. Moreover, we present a new design of soft finger, called the hybrid-bending soft finger (HBSF), that can both bend in the grasping axis and deflect in the side-to-side axis, achieving human-like motion (Figure 1). The functions of the HBSF and soft palm are evaluated both in simulation, using the Simulation Open Framework Architecture (SOFA) framework, and experimentally. Six finger designs with 1-11 longitudinal segments are analyzed. The versatility of the soft hand is evaluated and demonstrated experimentally by its grasping of objects according to Feix's taxonomy. The results demonstrate a great diversity of grasps, with 31 of the 33 grasp types of the taxonomy performed successfully with the proposed design, showing promise for grasping a large variety of objects with different shapes and weights.

The design of a force feedback soft gripper for tomato harvesting

In smart farming, both artificial intelligence and robotic systems are applied in order to improve efficiency. In agriculture, for jobs such as seeding, monitoring, and harvesting, robots are widely used. When using robots to harvest fruit and vegetables, it is essential not to apply excessive force, as it may damage the harvest. In this paper, a soft robotic three-fingered gripper is presented. It was designed and analysed using the finite element method. Each finger is made of silicone rubber. The shape of the finger is designed so that it is capable of handling spherical shaped objects, such as tomatoes or oranges. When holding a tomato, the fingers apply the contact force. The fingers are actuated pneumatically and the force applied is also controlled by a micro controller. The pressure inside the air chamber of the finger is in the range of 0- 95 kPa. Force sensors are attached to the end of each finger to provide force feedback. Then, the holding force is adjusted and applied to the surface of the tomato. The gripper can successfully grasp tomatoes with a force less than the bio-yield of the tomatoes 2.57 N.

Failure Handling of Robotic Pick and Place Tasks With Multimodal Cues Under Partial Object Occlusion.

The success of a robotic pick and place task depends on the success of the entire procedure: from the grasp planning phase, to the grasp establishment phase, then the lifting and moving phase, and finally the releasing and placing phase. Being able to detect and recover from grasping failures throughout the entire process is therefore a critical requirement for both the robotic manipulator and the gripper, especially when considering the almost inevitable object occlusion by the gripper itself during the robotic pick and place task. With the rapid rising of soft grippers, which rely heavily on their under-actuated body and compliant, open-loop control, less information is available from the gripper for effective overall system control. Tackling on the effectiveness of robotic grasping, this work proposes a hybrid policy by combining visual cues and proprioception of our gripper for the effective failure detection and recovery in grasping, especially using a proprioceptive self-developed soft robotic gripper that is capable of contact sensing. We solved failure handling of robotic pick and place tasks and proposed (1) more accurate pose estimation of a known object by considering the edge-based cost besides the image-based cost; (2) robust object tracking techniques that work even when the object is partially occluded in the system and achieve mean overlap precision up to 80%; (3) contact and contact loss detection between the object and the gripper by analyzing internal pressure signals of our gripper; (4) robust failure handling with the combination of visual cues under partial occlusion and proprioceptive cues from our soft gripper to effectively detect and recover from different accidental grasping failures. The proposed system was experimentally validated with the proprioceptive soft robotic gripper mounted on a collaborative robotic manipulator, and a consumer-grade RGB camera, showing that combining visual cues and proprioception from our soft actuator robotic gripper was effective in improving the detection and recovery from the major grasping failures in different stages for the compliant and robust grasping.

Soft robotic fabric gripper with gecko adhesion and variable stiffness

Fluid-driven soft grippers possess conformable grasping characteristics that differ from their rigid counterparts. Despite advances, their inherent low-stiffness due to constituent materials causes them to be inferior in many high-load applications. Existing fabrication methods of soft grippers that mostly rely on molding silicone elastomers, despite being simple, are not easily scalable. This article presents the design of a soft robotic fabric gripper that can be fabricated by a facile and highly scalable process of apparel engineering. The proposed robotic gripper features a multi-fingered design that comprises hydraulic-driven, sheet-shaped fabric bending actuators. Its performance is enhanced by incorporating a bio-inspired gecko adhesive and a thermo-responsive variable stiffness filament. Experimental studies demonstrate that adding the variable stiffness filament and gecko adhesive improves the holding force of the gripper up to 655 % and 507 % in the gripping and pull-out configurations, respectively. The variable stiffness filament features a relatively good cooling speed of only 31 s by ambient cooling. A simple analytical model was also developed to characterize the deformation of the fabric bending actuators. To monitor the gripper bending motion, a new soft fabric sensor comprising a conductive composite of liquid metal and carbon particles was developed. The sensor was configured in a sheet-like shape and can be easily integrated into the gripper, which has been usually absent for other fabric grippers. The materials employed by this gripper design are commercially available for a reasonable budget, enabling the gripper to be both cost-effective and have potential applications where both gentle grasping and high load capacity are required.

Magnetically switchable soft suction grippers

Grasping is one of the key tasks for robots. Gripping fragile and complex three-dimensional (3D) objects without applying excessive contact forces has been a challenge for traditional rigid robot grippers. To solve this challenge, soft robotic grippers have been recently proposed for applying small forces and for conforming to complex 3D object shapes passively and easily. However, rigid grippers are still able to exert larger forces, necessary for picking heavy objects. Therefore, in this study, we propose a magnetically switchable soft suction gripper (diameter: 20 mm) to be able to apply both small and large forces. The suction gripper is in its soft state during approach and attachment while it is switched to its rigid state during picking. Such stiffness switching is enabled by filling the soft suction cup with a magnetorheological fluid (MR fluid), which is switched between low-viscosity (soft) and high-viscosity (rigid) states using a strong magnetic field. We characterized the gripper by measuring the force required to pull the gripper from a smooth glass surface. The force was up to 90% larger when the magnetic field was applied (7.1 N vs. 3.8 N). We also demonstrated picking of curved, rough, and wet 3D objects, and thin and delicate films. The proposed stiffness-switchable gripper can also carry heavy objects and still be delicate while handling fragile objects, which is very beneficial for future potential industrial part pick-and-place applications.

A Gas-Ribbon-Hybrid Actuated Soft Finger with Active Variable Stiffness.

A new hybrid actuated soft finger with active variable stiffness is proposed for the first time by integrating gas-driven and ribbon-driven mechanisms. By carefully coordinating the two mechanisms, the bending deformation and the stiffness modulation processes of the soft finger can be uncoupled, providing it with both high flexibility and good variable stiffness. Although the soft finger, made entirely from flexible materials, works under a low and safe gas pressure of below 35 kPa, the maximum bending angle reaches ∼210°, and a single soft finger can withstand a weight of 1.25 kg. For any bending angle, with the help of the ribbon-driven mechanism, the stiffness of the soft finger can increase by three to six times. In addition, theoretical models are established for the evaluation of the bending-deformation characteristic and the output force of the soft finger, which are verified by experiments. A dual-finger soft robotic gripper is assembled by utilizing two soft fingers, which can easily and stably grab various objects with different sizes, shapes, and weights. Both the theoretical and experimental results indicate that the proposed gas-ribbon-hybrid actuated mechanism can effectively enhance the variable stiffness property of a soft finger while retaining its good compliance with the surroundings. This work might provide future insights for the development of compact and cost-effective soft end effectors with active variable stiffness.

Hybrid System Analysis and Control of a Soft Robotic Gripper with Embedded Proprioceptive Sensing for Enhanced Gripping Performance

Soft Grippers In article number 2000061, Yong-Lae Park and co-workers present a method of controlling a soft robotic gripper for grasping unidentified objects using a hybrid system analysis approach, which enables the system to detect the size of the object and to determine the optimal position of the gripper for successful grasping based on the proprioceptive feedback from an embedded soft strain sensor.

A Potentiometric Electronic Skin for Thermosensation and Mechanosensation

AbstractElectronic skins (e‐skins) that mimic the thermosensation and mechanosensation functionalities of natural skin are highly desired for the emerging fields of prosthetics and robotics. Advances in the materials and architecture of e‐skins have been made; nevertheless, sensing mechanism innovations are rarely explored. Here, inspired by the skin sensory behaviors, a single potentiometric sensing scheme for both thermosensation and mechanosensation functionalities are presented. Through careful materials selection, component optimization, and structure configuration, the coupling effect between thermosensation and mechanosensation can be significantly minimized. Such a potentiometric sensing scheme enables one to create a new class of energy‐efficient e‐skin with distinctive characteristics that are highly analogous to those of natural human skin. The e‐skin reported here features ultralow power consumption (at nanowatt level), greatly simplified operation (only voltage output), ultrahigh sensitivity (non‐contact sensing capability), all‐solution‐processing fabrication, and, more importantly, good capability for simultaneous monitoring/mapping of both thermal and mechanical stimulations. In addition to proposing a new sensory mechanism, integration of the dual‐functional e‐skin with a soft robotic gripper for object manipulation is demonstrated. The presented concise yet efficient sensing scheme for both thermosensation and mechanosensation opens up previously unexplored avenues for the future design of skin prosthetics, humanoid robotics, and wearable electronics.

Solar-Driven Soft Robots.

Stimuli‐responsive materials have been lately employed in soft robotics enabling new classes of robots that can emulate biological systems. The untethered operation of soft materials with high power light, magnetic field, and electric field has been previously demonstrated. While electric and magnetic fields can be stimulants for untethered actuation, their rapid decay as a function of distance limits their efficacy for long‐range operations. In contrast, light—in the form of sunlight or collimated from an artificial source (e.g., laser, Xenon lamps)—does not decay rapidly, making it suitable for long‐range excitation of untethered soft robots. In this work, an approach to harnessing sunlight for the untethered operation of soft robots is presented. By employing a selective solar absorber film and a low‐boiling point (34 °C) fluid, light‐operated soft robotic grippers are demonstrated, grasping and lifting objects almost 25 times the mass of the fluid in a controllable fashion. The method addresses one of the salient challenges in the field of untethered soft robotics. It precludes the use of bulky peripheral components (e.g., compressors, valves, or pressurized gas tank) and enables the untethered long‐range operation of soft robots.

Additively Manufactured Custom Soft Gripper with Embedded Soft Force Sensors for an Industrial Robot

Soft robotic grippers are required for power grasping of objects without inducing damage. Additive manufacturing can be used to produce custom-made grippers for industrial robots, in which soft joints and links are additively manufactured. In this study, a monoblock soft robotic gripper having three geometrically gradient fingers with soft sensors was designed and additively manufactured for the power grasping of spherical objects. The monoblock structure design reduces the number of components to be assembled for the soft gripper, and the gripper is designed with a single cavity to enable bending by the application of pneumatic pressure, which is required for the desired actuation. Finite element analysis (FEA) using a hyperelastic material model was performed to simulate the actuation. A material extrusion process using a thermoplastic polyurethane (TPU) was used to manufacture the designed gripper. Soft sensors were produced by a screen printing process that uses a flexible material and ionic liquids. The grasping capability of the manufactured gripper was experimentally evaluated by changing the pneumatic pressure (0–0.7 MPa) of the cavity. Experimental results show that the proposed monoblock gripper with integrated soft sensors successfully performed real-time grasp detection for power grasping.

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

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.

Versatile Soft Robot Gripper Enabled by Stiffness and Adhesion Tuning via Thermoplastic Composite.

Within the field of robotics, stiffness tuning technologies have potential for a variety of applications-perhaps most notably for robotic grasping. Many stiffness tuning grippers have been developed that can grasp fragile or irregularly shaped objects without causing damage and while still accommodating large loads. In addition to limiting gripper deformation when lifting an object, increasing gripper stiffness after contact formation improves load sharing at the interface and enhances adhesion. In this study, we present a novel stiffness and adhesion tuning gripper, enabled by the thermally induced phase change of a thermoplastic composite material embedded within a silicone contact pad. The gripper operates by bringing the pad into contact with an object while in its heated, soft state, and then allowing the pad to cool and stiffen to form a strong adhesive bond before lifting the object. Pull-off tests conducted using the gripper show that transitioning from a soft to stiff state during grasping enables up to 6 × increase in adhesion strength. Additionally, a finite element model is developed to simulate the behavior of the gripper. Finally, pick-and-place demonstrations are performed, which highlight the gripper's ability to delicately grasp objects of various shapes, sizes, and weights.

Actuating Shape Memory Polymer for Thermoresponsive Soft Robotic Gripper and Programmable Materials.

For soft robotics and programmable metamaterials, novel approaches are required enabling the design of highly integrated thermoresponsive actuating systems. In the concept presented here, the necessary functional component was obtained by polymer syntheses. First, poly(1,10-decylene adipate) diol (PDA) with a number average molecular weight Mn of 3290 g·mol−1 was synthesized from 1,10-decanediol and adipic acid. Afterward, the PDA was brought to reaction with 4,4′-diphenylmethane diisocyanate and 1,4-butanediol. The resulting polyester urethane (PEU) was processed to the filament, and samples were additively manufactured by fused-filament fabrication. After thermomechanical treatment, the PEU reliably actuated under stress-free conditions by expanding on cooling and shrinking on heating with a maximum thermoreversible strain of 16.1%. Actuation stabilized at 12.2%, as verified in a measurement comprising 100 heating-cooling cycles. By adding an actuator element to a gripper system, a hen’s egg could be picked up, safely transported and deposited. Finally, one actuator element each was built into two types of unit cells for programmable materials, thus enabling the design of temperature-dependent behavior. The approaches are expected to open up new opportunities, e.g., in the fields of soft robotics and shape morphing.

An untethered soft robotic gripper with high payload-to-weight ratio

Most of the soft grippers with high payload cannot carry cumbersome control systems and power sources themselves, and have to be tethered to external hardware through tubes and wires. To tackle this problem, we proposed an untethered soft pneumatic gripper with a high payload by developing a soft gripping structure with a high gripping force to pressure ratio. The untethered soft gripper integrates silicone elastomer structure, pneumatic artificial muscle (PAM), air pump, battery, controller, solenoid valve, and other components, with a total weight of 1.5 kg. The entire power sources of the untethered soft gripper are supplied by a battery, allowing it to perform at least 300 opening and closing movements. The lightweight and high load capacity of the untethered soft gripper enable its payload to weight ratio to exceed 26. Further, the untethered soft gripper can be installed on an unmanned aerial vehicle to expand its workspace. Also, a tightening force model for winding PAMs is presented in this paper, which can be used to evaluate the pressing force of the untethered soft gripper on the surface of an object.

Theoretical modelling of soft robotic gripper with bioinspired fibrillar adhesives

Soft robotic grippers have gained much attention in recent years owing to their advantages of easy fabrication, flexibility, and adaptability. Inspired by the adhesion capability of gecko’s toe and tree frogs in nature, this study presents a soft robotic gripper made of fiber-reinforced actuator and bioinspired fibrillar adhesives for grasping circular objects with enhanced grasping capability. Theoretical models of the designed gripper are established for characterizing bending angle, blocking force, friction force, and weight-carrying capability. The contribution of this article is the theoretical derivation of an explicit approximation formula for calculating bending angle and grasping force of designed soft gripper under different inflation pressures. Correctness and robustness of the derived formula for bending angle approximation was verified by established three-dimensional finite element model of the designed actuator. With the bioinspired fibrillar adhesives, the friction force between gripper and grasped objects is transformed to load-controlled and area-dependent friction force, which is much greater than that of the traditional design. A two-fingered soft gripper with the newly designed fiber-reinforced actuator is analyzed to verify the derived mathematical model. These models will be beneficial for robotic design/fabrication/control.

A Single-Mode, Self-Adapting, and Self-Powered Mechanoreceptor Based on a Potentiometric-Triboelectric Hybridized Sensing Mechanism for Resolving Complex Stimuli.

Human skin is equipped with slow adapting (SA) and fast adapting (FA) capabilities simultaneously. To mimic such functionalities, elaborately designed devices have been explored by integrating multiple sensing elements or adopting multimode sensing principles. However, the complicated fabrication, signal mismatch of different modules, complex operation, and high power-consumption hinder their widespread applications. Here, a new type of single-mode and self-powered mechanoreceptor that can mimic both SA and FA via seamless fusion of complementary while compatible potentiometric and triboelectric sensing principles is reported. The resultant potentiometric-triboelectric hybridized mechanoreceptor exhibits distinctive features that are hard to achieve via currently existing methods, including single-mode output (only voltage signal), greatly simplified operation (single-measurement setup), ultralow power-consumption (<1 nW), self-adaptive response behavior, and good capability for resolving complex stimuli. Diverse mechanical characteristics, including magnitude, duration, frequency, applying and releasing speed, can be well interpreted with this single-mode and self-powered mechanoreceptor. Its promising application for monitoring object manipulations with a soft robotic gripper is explored. Furthermore, the versatility of the mechanoreceptor for resolving complex stimuli in diverse daily scenarios is demonstrated. This work presents a new design that will significantly simplify the fabrication/operation and meanwhile boost the functionality/energy-efficiency of future electronic devices and smart systems.

Employing Pneumatic, Telescopic Actuators for the Development of Soft and Hybrid Robotic Grippers.

Traditionally, the robotic end-effectors that are employed in unstructured and dynamic environments are rigid and their operation requires sophisticated sensing elements and complicated control algorithms in order to handle and manipulate delicate and fragile objects. Over the last decade, considerable research effort has been put into the development of adaptive, under-actuated, soft robots that facilitate robust interactions with dynamic environments. In this paper, we present soft, retractable, pneumatically actuated, telescopic actuators that facilitate the efficient execution of stable grasps involving a plethora of everyday life objects. The efficiency of the proposed actuators is validated by employing them in two different soft and hybrid robotic grippers. The hybrid gripper uses three rigid fingers to accomplish the execution of all the tasks required by a traditional robotic gripper, while three inflatable, telescopic fingers provide soft interaction with objects. This synergistic combination of soft and rigid structures allows the gripper to cage/trap and firmly hold heavy and irregular objects. The second, simplistic and highly affordable robotic gripper employs just the telescopic actuators, exhibiting an adaptive behavior during the execution of stable grasps of fragile and delicate objects. The experiments demonstrate that both grippers can successfully and stably grasp a wide range of objects, being able to exert significantly high contact forces.

A Novel Simple, Adaptive, and Versatile Soft-Robotic Compliant Two-Finger Gripper With an Inherently Gentle Touch

AbstractIn soft robotics, there is still a great need for a universal but simple gripper that realizes a high level of adaptability as well as a gentle touch to a wide variety of unknown objects of different size, shape, stiffness, and weight without the use of sensors or vision. Various, mostly complex grippers already exist based on certain actuation concepts. However, each solution has specific limitations, especially regarding gripping different soft and delicate objects. Therefore, this paper introduces a new approach to design a simple, adaptive, and versatile soft robotic two-finger gripper that is based on compliant mechanisms. More specifically, an inherently gentle touch is realized by utilizing an optimally synthesized mechanism with distributed compliance in combination with a conventional linear actuator. It is shown by finite elements method (FEM) simulations that the gripper realizes a high force and motion transmission at the same time. Furthermore, it is demonstrated by tests with a gripper prototype that reliable, safe, and fast grasping as well as manipulation are possible for a wide variety of objects. It is shown that beside regular and stiff objects also very challenging objects can be easily gripped, e.g., small, irregular, soft, and squeezable objects like fruits, berries, and vegetables. Moreover, it is confirmed that the developed compliant two-finger gripper can be used beneficially without sensors and control for differently sized and shaped objects with a comparable weight.