Design Of Gripper Research Articles

Corrigendum to: Current Status of Patent Research on Picking End Effector

An error appeared in the citation of Tables 2 and 3 in the manuscript titled “Current Status of Patent Research on Picking End Effector”, 2024; 18(6): e190523217074 [1]. <p> Original: <p> Li [69] developed a self-triggering apple picking and collecting machine, which consists of five parts: a selftriggering gripper, a telescopic adjustment device, a rotary turntable, a collecting device, and a floor car. The gripper part innovatively adopts a selftriggering gripper design. The invention solves the problem of low apple-picking efficiency and causes less damage to apples. <p> Wang et al. [78] invented an end effector suitable for gripping spherical fruits. The fingers of the bionic human form an effective gripping space through the upper gripping finger, the lower gripping finger, and the auxiliary gripping finger, providing reliable gripping performance. This leads to the avoidance of mechanical damage to the fruit. This device has been found to improve fruit sorting efficiency and reduce labor, but its applicability is not wide enough. <p> Corrected: <p> Li [69] developed a self-triggering apple picking and collecting machine, which consists of five parts: a selftriggering gripper, a telescopic adjustment device, a rotary turntable, a collecting device, and a floor car. The gripper part innovatively adopts a selftriggering gripper design. The invention solves the problem of low apple-picking efficiency and causes less damage to apples (Table 2). <p> Wang et al. [78] invented an end effector suitable for gripping spherical fruits. The fingers of the bionic human form an effective gripping space through the upper gripping finger, the lower gripping finger, and the auxiliary gripping finger, providing reliable gripping performance. This leads to the avoidance of mechanical damage to the fruit. This device has been found to improve fruit sorting efficiency and reduce labor, but its applicability is not wide enough (Table 3). <p> The original article can be found online at https://www.eurekaselect.com/article/131925

Enhanced interlocking in granular jamming grippers through hard and soft particle mixtures

We investigate the influence of particle stiffness on the grasping performance of granular grippers, a class of soft robotic effectors that utilize granular jamming for object manipulation. Through experimental analyses and X-ray imaging, we show that grippers with soft particles exhibit improved wrapping of the object after jamming, in contrast to grippers with rigid particles. This results in significantly increased holding force through the interlocking. The addition of a small proportion of rigid particles into a predominantly soft particle mixture maintains the improved wrapping but also significantly increases the maximum holding force. These results suggest a tunable approach to optimizing the design of granular grippers for improved performance in soft robotics applications.Graphic abstract

Novel Soft Gripper With Adjustable Performance Based on Blade Flexures

Abstract The design of soft grippers is challenging as the target objects to be handled involve a wide variety of sizes, shapes, and softness. Most grippers reported present drawbacks, e.g., complex control strategies for stiffness and force variation, inadequate adaptability to target shape variability, and complicated adaptation to manipulators, which in turn limit their implementation in applications such as harvesting. This paper presents a novel overlay soft gripper based on an assembling mechanism and passive soft structures, enabling the modification of grip size, orientation, and gripping force. This gripper can be assembled over a commercial robot-gripper taking the closing motion of this as the input to perform its closing motion. Input and output parameters are related by means of the displacement transmission mechanism which converts the closing motion of the robot-gripper into the closing motion of the overlay soft gripper. The gripping force can be modified through the change of stiffness, via adjustment of effective length using contact elements, of internal blade flexures. The displacement transmission mechanism works in two modes: as rigid-body mechanism without the contact elements and as rigid-body/flexible mechanism with the contact elements. The relationships between input and output parameters are obtained analytically for the case of rigid-body mechanism, and through finite element analysis simulations for the case of rigid-body/flexible mechanism. Relationships between input and output parameters are approximated with polynomial surfaces. Finally, physical prototypes are manufactured and assembled on their respective robot-grippers to qualitatively demonstrate their performance.

A transforming flexahedron origami inspired gripper actuated by shape memory alloy

Abstract The transforming flexahedron origami (TFO) is a unique origami that combines the functions of fingers and palm, with a symmetrical distribution of four-finger structures and good shape adaptability. In this paper, inspired by the TFO configuration, a new type of reconfigurable gripper is designed. The kinematics and mechanics of the gripper are analyzed theoretically, and paper folding is used as the skeleton, thin film as the skin, shape memory alloy spring as the actuator, and active cooling is used to enhance the actuator efficiency. Different kinds of objects are used to test the gripping performance of the gripper, including shape adaptive ability and gripping robustness test, and the test results show that the gripper is with good gripping performance. The reconfigurable capability of the TFO configuration allows the hand gripper to have two gripping modes, adapting to the adaptive gripping of thin rod-like objects and curved objects, which is difficult to achieve with a single gripping mode. The TFO gripper provides a new way of thinking for the design of flexible and origami grippers in the future.

Characterization, generative design, and fabrication of a carbon fiber-reinforced industrial robot gripper via additive manufacturing

Robot grippers are crucial components across various industrial applications, requiring special design and production for obtaining the optimal performance. Conventional plastic injection moulding techniques fall short in achieving the specificity needed for these grippers. To address this challenge, current paper focuses on developing a robot gripper using carbon fiber-reinforced polyamide with a next-generation composite filament and employing the innovative Generative Design technique. In the work, we began by characterizing and optimizing the composite material specifications. Then, the tensile strength and fracture mechanics of standard samples based on printing parameters, applying Taguchi experimental design for optimization were evaluated. Analysis of Variance (ANOVA) was used for factor analysis to fine-tune the process. Using the Generative Design technique, we determined optimal geometries, which were then fabricated through Fused Deposition Modeling (FDM). As a result, the optimization efforts led to significant improvements i.e., tensile strength increased from 103.2 to 116 MPa, and the elasticity modulus from 8386 to 8990 MPa. In practical industrial applications, we achieved a reduction in material weight from 14 to 4 g, lowered production costs from $5.16 to $1.50, and cut production time from 58 to 28 min. This study presents a validated method for developing industrial products with reduced material usage and costs, promoting sustainable production practices.

Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design

This paper presents a comprehensive study on the design optimization of a robotic gripper, focusing on both the gripper modeling and the optimization of its parallel mechanism structure. This study integrates the Black-winged Kite Algorithm (BKA), Finite Element Analysis (FEA), Backpropagation Neural Network (BPNN), and response surface optimization techniques. The Good Point Set (GPS), nonlinear convergence factor, and adaptive t-distribution method improve BKA, which enhances exploration and exploitation performance, convergence speed, and solution quality. Subsequently, the parallel mechanism structure is designed to minimize the total mass, total deformation, and maximum equivalent stress. The central composite design (CCD) method was used to design the FEA experiment and establish the BKA-BPNN regression prediction model. The RMSE of this model’s training set and test set are 0.001615 and 0.0029328. A response surface optimization model is constructed to determine the best design solution. The optimized design achieves a 33.12% reduction in maximum equivalent stress, a 1.47% decrease in total mass, and a 0.16% reduction in maximum total deformation. This study provides valuable insights into the design optimization process for robotic grippers, showcasing the effectiveness of the proposed methodologies in enhancing performance while reducing mass and improving structural integrity.

Design of a dual-finger 3D-printed gripper with a lowcost ToF-based force sensor

Design of a dual-finger 3D-printed gripper with a lowcost ToF-based force sensor

Customizable 6 degrees of freedom grasping dataset and an interactive training method for graph convolutional network

The field of robotic grasping has seen significant progress with the development of deep learning and the creation of large-scale datasets like the Cornell Grasping Dataset (Jiang et al., 2011) and DexNet (Mahler et al., 2016). However, challenges persist due to the reliance on manually annotated datasets, limited by data scarcity, high costs, biases, and a lack of diversity in gripper types and three-dimensional information, hampering their effectiveness in real-world applications. To confront these issues, an innovative method was introduced for generating robotic grasping datasets in a simulated environment, eliminating the need for manual annotations. The method utilizes a highly realistic movement of the gripper, offering extensive customization options for a variety of gripper types. It also introduces detailed evaluation metrics specifically designed to assess different gripper designs, ensuring accurate and meaningful analysis of grasping efficacy. Further, it excels in simulating a wide range of industrial scenarios, significantly enhancing the dataset's diversity and applicability in real-world applications. In addition, an end-to-end grasping prediction network is introduced, which leverages advanced graph convolution techniques to predict optimal grasping points and orientations from point cloud. It also serves as an effective baseline for the proposed grasping dataset. Lastly, the authors propose a novel interactive training method for deep learning models driven by data generation, featuring real-time interaction between the model and the data generator with a rule-based strategy that optimizes the training workflow based on feedback. Experimental results demonstrate that the interactive training method enables models to achieve superior outcomes in a shorter timeframe compared to those trained using traditional methods.

Porous Magnetic Soft Grippers for Fast and Gentle Grasping of Delicate Living Objects.

Magnetic soft grippers have attracted intensive interest due to their untethered controllability, rapid response, and biological safety. However, manipulating living objects requires a simultaneous increase in shape adaptability and gripping force, which are typically mutually exclusive. Increasing the magnetic particle content enhances the magnetic strength but also increases the elastic modulus, leading to low adaptability and high impact force. Here, a porous magnetic soft gripper (PMSG) is developed by integrating a porous structure into a magnetic silicone elastomer. The design of porous hard magnetic composite is characterized by high magnetization, low modulus, and rough surface. It offers the PMSG good compliance, high gripping force, and low impact force at fast gripping. The PMSG is capable of performing a variety of tasks, including the fast and gentle grasping of delicate living objects. The study provides insight into the design of novel magnetic grippers and may offer a promising outlook for biomedical or scientific applications in the manipulation of delicate organisms.

Enhancing compliant gripper performance: Exploiting electro-adhesion to increase lifting force over grasping force

On the landscape of solutions to deal with delicate objects, the development and use of soft grippers is a topic of increasing interest, with a large number of prototypes proposed by the research community employing non-linear soft materials and based on diverse actuation means. However, increasing compliance usually leads to the reduction of lifting capacity. As a recent promising approach, shear forces exerted by a soft gripper can be enhanced by exploiting the electro-adhesion (EA) effect. Following this research trend, this paper proposes a new gripper that combines a compliant finger structure, with geometry taken from the FESTO FinRay but made of a softer material (a urethane rubber), and custom EA pads that are placed on the fingers at the interface with the grasped object. Following hyper-elastic model identification of the considered material and preliminary functional verification of gripper design via finite element simulations, the gripper is then manufactured and tested by means of a specific setup, replicating the grasping and lifting of cylindrical objects with different diameters. The results clearly show that the new gripper makes it possible to generate holding forces similar to those of the FESTO FinRay, but with significantly lower pressures on the grasped object (77 % less). Besides enabling the handling of more fragile items, the drastic increase in gripper compliance also results in lower mechanical actuation force (namely, 71 % less of gripping energy) required to generate the same holding force, with a consequent reduction of operation costs and sustainability of its application.

Design and analysis of a four-crease origami gripper (FCOG)

In this paper, a Four-Crease Origami Gripper (FCOG) is designed. The kinematic model of the FCOG is derived. The correctness of the theoretical model is verified through experimental tests. To describe the performance of the FCOG, two parameters are defined, that is, capacity ratio and load ratio. The capacity ratio describes the configuration characteristics of the FCOG, which has been validated through experiments and analysis. The load ratio describes the load-bearing performance. The experiments show that the FCOG can meet the requirements of strength and load performance. Finally, the future potential applications of the FCOG are discussed.

Design of a robotic gripper for casting sorting robots with rigid–flexible coupling structures

AbstractIn order to solve the problem of the insufficient adaptability of the current small- and medium-sized casting sorting robot gripper, we have designed a casting sorting robot bionic gripper with rigid–flexible coupling structures based on the robot topology theory. The second-order Yeoh model was used to statically model the clamping belt in the gripper to derive the relationship between the external input air pressure and the bending angle of the driving layer, and the feasibility of multiangle bending of the driving layer was verified by finite element analysis. The maximum gripping diameter of the gripper is 140 mm, and in order to test the adaptive gripping ability of the gripper, a prototype of the casting sorting robot gripper is prepared, and the pneumatic control system and human–machine interface of the gripper are designed. After several experimental analyses, the designed casting sorting robot gripper is characterized by strong adaptability and high robustness, with a maximum load capacity of 930 g and a maximum wrap angle of 296°, which can complete the gripping operation within 1 s, and the comprehensive gripping success rate reaches 96.4%. The casting sorting robot gripper designed in the paper can provide a reference for the design and optimization of various types of shaped workpiece gripping manipulators.

Experimental and Numerical Testing of Jaw Gripper Design Using The Mass Reduction Method of Onyx-Carbon Fiber Material at PT. Matahari Megah

Experimental and Numerical Testing of Jaw Gripper Design Using The Mass Reduction Method of Onyx-Carbon Fiber Material at PT. Matahari Megah

Adaptive wrapping with active elastic band-based gripper for stable in-hand manipulation

Achieving both compliant grasping and robust in-hand manipulation for a robotic hand is a challenging task, especially for fragile or irregularly shaped objects. Active surface grippers usually rely on non-stretchable belts and compliant finger mechanisms for adaptive manipulation and grasping, requiring complex tensioning mechanisms that increase mechanical complexity. This paper presents a novel design of an active elastic band-based gripper, that can achieve translation and rotation of grasped objects with passive adaptive wrapping and active conveyor motion. The design utilizes pre-tensioned elastic bands that function simultaneously as an adaptive grasping mechanism and an active surface, conforming to the shape of the object shape and providing evenly distributed gripping forces. The principles of grasping and manipulation, as well as the fabrication process of the gripper are presented in this work, and the kinematics and workspace of the gripper with cylindrical objects are analyzed. Based on experiments, the contact stiffness model of the elastic band finger with cylindrical objects was established. To evaluate the performance of the gripper, we carried out experiments on manipulation objects with different shapes and sizes, such as boiled eggs, and fruits. Experimental results illustrated the adaptable grasping and stable in-hand manipulation ability of the gripper.

Adaptive grey wolf optimizer based on transfer function inertia weight of second-order high-pass filter

This paper proposes an Adaptive Grey Wolf Optimizer based on Transfer function Inertia Weight of Second-order High-pass Filter (SAGWO), aiming to overcome the limitations of Grey Wolf Optimizer (GWO) in terms of convergence speed and solution accuracy. SAGWO enhances the algorithm's dynamic adaptability and greatly speeds up convergence by incorporating the transfer function of the second-order high-pass filter into the modifications of inertia and leader wolves' weight. Furthermore, SAGWO incorporates an adaptive random search component, which significantly enhances the search range and the ability to explore globally. The performance of SAGWO is assessed and compared against three prominent Swarm Intelligence Algorithms (ALO, WOA, and EHO), along with GWO and GWO optimized by PSO. This evaluation is conducted through trials on the CEC2017 standard test set. The experimental results indicate that SAGWO outperforms in terms of both convergence speed and solution correctness. Moreover, SAGWO is utilized to address practical engineering problems such as pressure vessel design and robot gripper design, showcasing its remarkable effectiveness once more. This research enhances the theoretical development of Swarm Intelligence Algorithms and demonstrates the significant utility of SAGWO in practical applications, establishing it as a powerful instrument for scientific investigation and engineering optimization.

Sensor-Enhanced Smart Gripper Development for Automated Meat Processing.

Grasping and object manipulation have been considered key domains of Cyber-Physical Systems (CPS) since the beginning of automation, as they are the most common interactions between systems, or a system and its environment. As the demand for automation is spreading to increasingly complex fields of industry, smart tools with sensors and internal decision-making become necessities. CPS, such as robots and smart autonomous machinery, have been introduced in the meat industry in recent decades; however, the natural diversity of animals, potential anatomical disorders and soft, slippery animal tissues require the use of a wide range of sensors, software and intelligent tools. This paper presents the development of a smart robotic gripper for deployment in the meat industry. A comprehensive review of the available robotic grippers employed in the sector is presented along with the relevant recent research projects. Based on the identified needs, a new mechatronic design and early development process of the smart gripper is described. The integrated force sensing method based on strain measurement and magnetic encoders is described, including the adjacent laboratory and on-site tests. Furthermore, a combined slip detection system is presented, which relies on an optical flow-based image processing algorithm using the video feed of a built-in endoscopic camera. Basic user tests and application assessments are presented.

Design of Adaptive Grippers for Fruit-Picking Robots Considering Contact Behavior

Adaptability to unstructured objects and the avoidance of target damage are critical challenges for flexible grippers in fruit-picking robots. Most existing flexible grippers have many problems in terms of control complexity, stability and cost. This paper proposes a flexible finger design method that considers contact behavior. The new approach incorporates topological design of contact targets and introduces contact stress constraints to directly obtain a flexible finger structure with low contact stress and good adaptability. The study explores the effects of design parameters, including virtual spring stiffness, volume fraction, design domain size, and discretization, on the outcomes of the flexible finger topology optimization. Two flexible finger structures were selected for comparative analysis. The experimental results verified the effectiveness of the design method and the maximum contact stress was reduced by about 70%. An adaptive two-finger gripper was developed. This design allows the gripper to achieve damage-free grasping without additional sensors and control systems. The adaptive and contact performances of the grippers with different driving modes were analyzed. Practical grasping tests were also performed, including evaluation of adaptive performance, stability, and maximum grasping weight. The results indicate that gripper 2 with flexible finger 2 excelled in contact stress and adaptive wrapping, making it well-suited for grasping unstructured and fragile objects. This paper provides valuable insights for the design and application of flexible grippers for picking robots, offering a promising solution to enhance adaptability while minimizing target damage.

Design of a soft gripper with negative-pressure actuated synthetic annulus microwedge adhesive

The automation and assembly lines of factories have faced the need for reliable handling of porous, fragile, and soft objects. Current soft grippers have difficulty robustly grasping flat, flexible surfaces without distorting them. This paper proposes a soft gripper based on annulus microwedge adhesive for grasping flat objects using negative pressure actuation mechanisms, which can achieve controllable adhesives to reliably grasp and easily release smooth flat objects. This is achieved through negative pressure actuation, which provides a centripetal load to the annular microwedge adhesives for strong attachment, by returning to normal pressure, the centripetal load is rapidly removed, allowing for easy detachment from the adhesive surface. Additionally, the relationship between the geometrical parameters of the deformation chamber and the contact area of annulus microwedges is investigated by using finite-element method models and experiments. The results show that the wall thickness is a key parameter in designing the deformation chamber. The results of the payload test have determined the geometrical and pressure parameters of the deformation chamber with the best adhesion performance. Finally, a demonstration shows that the gripper can handle various objects for potential applications, such as in clean environmental industries.

Easy-to-actuate multi-compatible truss structures with prescribed reconfiguration

Multi-stable structures attract great interest because they possess special energy landscapes with domains of attraction around the stable states. Consequently, multi-stable structures have the potential to achieve prescribed reconfiguration with only a few lightweight actuators (such as shape-memory alloy springs), and do not need constant actuation to be locked at a stable state. However, most existing multi-stability designs are based on assembling bi-stable unit cells, which contain multitudes of distractive stable states, diminishing the feasibility of reconfiguration actuation. Another type is by introducing prestress together with kinematic symmetry or nonlinearity to achieve multi-stability, but the resultant structure often suffers the lack of stiffness. To help address these challenges, we firstly introduce the constraints that a truss structure is simultaneously compatible at multiple (more than two) prescribed states. Then, we solve for the design of multi-stable truss structures, named multi-compatible structures in this paper, where redundant stable states are limited. Secondly, we explore minimum energy paths connecting the designed stable states, and compute for a simple and inaccurate pulling actuation guiding the structure to transform along the computed paths. Finally, we fabricated four prototypes to demonstrate that prescribed reconfigurations with easy-actuation have been achieved and applied a quadra-stable structure to the design of a variable stiffness gripper. Altogether, our full-cycle design approach contains multi-stability design, stiffness design, minimum-energy-path finding, and pulling actuation design, which highlights the potential for designing morphing structures with lightweight actuation for practical applications.

Unlocking Versatility: Magnetic-Actuated Deployable Suction Gripper for Complex Surface Handling.

Suction grippers offer a distinct advantage in their ability to handle a wide range of items. However, attaching these grippers to irregular and rough surfaces presents an ongoing challenge. To address this obstacle, this study explores the integration of magnetic intelligence into a soft suction gripper design, enabling fast external magnetic actuation of the attachment process. Additionally, miniaturization options are enhanced by implementing a compliant deploying mechanism. The resulting design is the first-of-its-kind magnetically-actuated deployable suction gripper featuring a thin magnetic membrane (Ø 50 mm) composed of carbonyl iron particles embedded in a silicone matrix. This membrane is supported by a frame made of superelastic nitinol wires that facilitate deployment. During experiments, the proof-of-principle prototype demonstrates successful attachment on a diverse range of curved surfaces in both dry and wet environments. The gripper achieves attachment on curved surfaces with radii of 50-75 mm, exerting a maximum attachment force of 2.89 ± 0.54 N. The current gripper design achieves a folding percentage of 75%, enabling it to fit into a Ø 12.5 mm tube and access hard-to-reach areas while maintaining sufficient surface area for attachment forces. The proposed prototype serves as a foundational steppingstone for further research in the development of reliable and effective magnetically-actuated suction grippers across various configurations. By addressing the limitations of attachment to irregular surfaces and exploring possibilities for miniaturization and precise control, this study opens new avenues for the practical application of suction grippers in diverse industries and scenarios.