Design Of Robotic Gripper Research Articles

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.

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.

Reticular Origami Soft Robotic Gripper for Shape-Adaptive and Bistable Rapid Grasping.

The top-down approach in designing and fabricating origami robots could achieve far more complicated functions with compliant and elegant designs than traditional robots. This study presents the design, fabrication, and testing of a reticular origami soft robotic gripper that could adapt to the shape of the grasping subject and grasp the subject within 80 ms from the trigger instance. A sensing mechanism consisting of the resistive pressure sensor array and flexible elongation sensor is designed to validate further the shape-adaptive grasping capability and model the rough shape and size of the subject. The grasping test on various objects with different shapes, surface textures, sizes, and living animals further validates the excellent grasping capabilities of the gripper. The gripper could be either actively triggered by actuation or passively triggered by a minimum of 0.0014 J disturbance energy. Such features make it particularly suitable for applications such as capturing underwater creatures and illegal drone control.

A user-friendly toolkit to select and design multi-purpose grippers for modular robotic systems

Robotic grasping is a billion-dollar industry with significant growth potential for the next decade. Unsurprisingly, technology providers and integrators are increasingly assisting in optimising production efficiency by implementing industrial robots and the necessary end-effectors into production lines. However, still too often in today’s robotic applications, dedicated grippers are designed to perform single tasks therewith adversely increasing cycle times and robot cell spaces due to constantly switching grippers from a tool magazine. In order to reduce handling costs and to cope with mass customisation, versatile grasping of various objects with multi-purpose grippers is key in evolving to modular assembly systems.Nowadays, thoughtful selection and design of robotic grippers is still mainly restricted to expert knowledge due its ad hoc and multi-objective nature. However, gripper selection and design should be tailored to the properties of the objects to be grasped, and the tasks to be executed. As a result, current solutions often end up being partly appropriate and/or over-dimensioned. The use of software tools to support this complex decision-making process is needed but limited so far.This paper presents a user-friendly toolkit that assists in selecting appropriate robotic grippers for grasping pre-specified ranges of objects. For this, a web API has been established for accessing the available gripper database and selection methodology. Additionally, this toolkit assists in designing multi-functional jaws for impactive grippers, and lay-outing individually positioned astrictive grippers. The underlying optimiser will try to find the most ideal grasping points on each object in accordance with quality metrics and constraints, and this for each of the selected grippers. To demonstrate the functionality of the presented toolkit, two case studies on impactive and astrictive gripper designs have been worked out involving 3D mechatronic and 2D sheet metal parts.

Design and Simulation of Robot Gripper and Conveyor System for Workstations

Abstract: Intelligent manufacturing is going to boom in the upcoming years. The characteristic features of Industry 4.0 are fully automated production facilities where all processes are controlled in real-time and take into account the changing external conditions. The core of the Industry 4.0 is made up of digitalization and robotics, in particular, the use of collaborative robots and MES. MES (Manufacturing Execution System) is a specialized system designed to solve the problems of synchronization, coordination, analysis, and optimization of production. It involves the use of conveyor belts for rapid transportation of products. Industries where conveyor belt systems are excelling include automotive, computer, good, packaging, print finishing, bottling and canning, chemical, pharmaceutical, aerospace and food processing The safety conditions in fundamental industrial jobs are dangerous and harmful combined with ineffective manual work as a result of rising need for mass manufacturing. In industries, automation is the use of technology to jobs that were previously handled by humans. It is a potent instrument that can aid companies in enhancing quality, production, and efficiency. A typical type of mechanical handling equipment that transports things from one place to another is a conveyor system. These systems consist of conveyor belts that are designed to work seamlessly with robotic arms and other automated equipment. Increased automation and mass production rate reduces the lead time and increases the profit of a firm. The deployed robotic arms are immensely capable of handling various tasks with increased flexibility. Using a robotic arm with an end effector as a gripper to hold/pick and place the material from the conveyor belt improves the product flow process. The project comes with the NodeMCU firmware and an integrated Wi-Fi chip, the ESP8266 12E. To connect with the controller via a mobile phone, a web page would be constructed using HTML and coded using the Arduino IDE's C language. At either end of the conveyor belt, 2 robotic arms with 2 degrees of freedom (D.O.F.) would be placed. Using one arm to place the product on the belt and a second to pick it up at the other end. Also calculating the analytical data of the system that is involved in reallife industrial processes along with the simulation of entire system for the UR10 industrial robot, using the RoboDK simulation software. Through the usage of this system, productivity is boosted with improved safety of the working environment, become more effective, be more flexible, and have higher levels of job satisfaction

A nested optimization approach for robot gripper multi-objective optimization problem

Robot Gripper Design Optimization Problem is a well-known multi-objective optimization problem explored by various approaches in previous studies over the last few decades. The continuous parameter z plays an important role in determining a robot gripper's minimum and maximum gripping forces. However, previous studies have utilized a simple loop approach in which the value of z is iteratively increased by a constant value from 0 to 50 to determine the gripping forces of a solution. This approach limits the solution space by treating the continuous z parameter as a discrete variable. To overcome this limitation, we propose a nested optimization approach in which a single-objective optimizer is embedded within the fitness function of the main optimizer to determine the optimal value of z. The proposed approach is evaluated using a single-optimizer Simulated Annealing (SA) nested within multi-objective optimizers such as Multi-Objective Gray Wolf Optimizer (MOGWO), Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Artificial Algae Algorithm (MOAAA), and Non-Dominated Sorting Genetic Algorithm (NSGA-II). Experimental results demonstrate that our approach significantly outperforms previous studies and achieves the best-known performance. The findings of the study indicate that this method can present a novel framework for researchers in tackling design optimization challenges, encouraging a reevaluation and possible improvement of conventional techniques.

Intelligent Soft Robotic Grippers for Agricultural and Food Product Handling: A Brief Review with a Focus on Design and Control

Advances in material sciences, control algorithms, and manufacturing techniques have facilitated rapid progress in soft grippers, propelling their adoption in various fields. In this review article, a comprehensive overview of the design and control aspects of intelligent soft robotic grippers tailored specifically for agricultural product handling is provided. Soft grippers have emerged as a promising solution for handling delicate and fragile objects. In this article, the recent progress in various gripper design, including fluidic and mechanical grippers, is elucidated and the role of advanced control approaches in enabling intelligent functions, such as object classification and grasping condition evaluation, is explored. Moreover, the challenges and opportunities pertaining to implementation of soft grippers in the agricultural industry are thoroughly discussed. While most demonstrations of soft grippers and their control strategies remain at the experimental stage, in this article, it is aimed to provide insights into the potential applications of soft grippers in agricultural product handling, thereby inspiring future research in this field.

Innovative development of a soft robotic gripper: mathematical modeling and grasping capability analysis

Recently, most soft grippers rely on complex mechanisms or complicated structures, leading to challenging fabrication and mathematical modeling. This study presents an innovative soft-fingered gripper design with an asymmetric tube that has a variable cross-section, providing a simple yet effective solution for gripping tasks. The proposed mathematical modeling and solutions were successfully developed and experimentally validated to analyze the effects of the robot arm’s motion on the gripping capability of the gripper. This innovation represents an improvement over existing designs and has the potential to enhance the design and efficient performance of soft robotic grippers.

An overview of robotic grippers

The development of robotic grippers is driven by the need to execute particular manual tasks or meet specific objectives in handling operations. Grippers with specific functions vary from being small, accurate, and highly controllable, such as the surgical tool effectors of the Da Vinci robot (designed to be used as noninvasive grippers controlled by a human operator during keyhole surgeries), to larger, highly controllable grippers like the Shadow Dexterous Hand (designed to recreate the hand motions of a human). Additionally, there are less finely controllable grippers, such as the iRobot-Harvard-Yale (iHY) Hand or iRobot-Harvard-Yale (IIT)-Pisa SoftHand, which, instead, leverage natural motions during grasping via designs inspired by observed biomechanical systems. As robotic systems become more autonomous and widely used, it is becoming increasingly important to consider the design, form, and function of robotic grippers.

A Note on Redesign Material Substitution and Topology Optimization in a Lightweight Robotic Gripper

The gripper is required because it is the portion of the robot that makes direct contact with the object being grasped. It should weigh as little as possible without compromising functionality or its performance. This study aims to reconsider the construction of a lightweight robotic gripper by modifying the gripper's materials and topology. Using the finite element (FE) method, several types of gripper materials were evaluated for static stress. On the basis of the results of the FE analysis, the optimal material candidate was chosen using the weighted objective method. Using the Fusion 360 software, the topology of the selected material was then optimized in an effort to achieve the 40% weight reduction’s objective. In addition, the suggested optimized geometry is then fine-tuned so that it can be manufactured as efficiently as possible. The final step in the validation of the robotic gripper's design was stress static analysis. The revised gripper design has a mass of 0.08 kg, a reduction of 94% from the original mass, and a safety factor of 3.67%, which satisfies the desired level of performance for the robotic gripper. Utilizing different materials and optimizing the gripper's topology can significantly reduce the overall mass of a robotic gripper.

Elephant trunks use an adaptable prehensile grip

Elephants have long been observed to grip objects with their trunk, but little is known about how they adjust their strategy for different weights. In this study, we challenge a female African elephant at Zoo Atlanta to lift 20–60 kg barbell weights with only its trunk. We measure the trunk’s shape and wrinkle geometry from a frozen elephant trunk at the Smithsonian. We observe several strategies employed to accommodate heavier weights, including accelerating less, orienting the trunk vertically, and wrapping the barbell with a greater trunk length. Mathematical models show that increasing barbell weights are associated with constant trunk tensile force and an increasing barbell-wrapping surface area due to the trunk’s wrinkles. Our findings may inspire the design of more adaptable soft robotic grippers that can improve grip using surface morphology such as wrinkles.

Task-Oriented Methodology Combining Human Manual Gestures and Robotic Grasp Stability Analyses: Application to the Specification of Dexterous Robotic Grippers

Abstract The objective of this article is to present a comprehensive task analysis methodology that can provide guidelines for the design of dexterous robotic grippers that are versatile enough to perform various tasks, yet simple to manufacture. This methodology combines a human-centered gestures analysis and an object-centered grasp stability analysis. The former relies on a careful examination of a human operator’s hands gestures while performing a specific process, providing designers with tools that help specify the number of fingers, the number of degrees-of-freedom, and the placement of tactile sensors. The latter exploits a grasp quality metric to compute the efforts required to handle the involved objects, providing guidelines for the specification of the actuation system. Using observations of operators at work as a source of inspiration allows guarantying the ability to perform the considered tasks (with guaranteed stability, thanks to the grasp analysis), contrary to technologically driven optimization methodologies, which often sacrifice manipulation capabilities for the sake of simplicity. Yet, our task-oriented approach allows focusing on certain tasks, hence simpler solutions than bio-mimetic designs that try to fully mimic the human hand. In other words, the methodology introduced in this article intends to help specify multi-fingered architectures able to maintain a high degree of dexterity with a reduced kinematic complexity, favoring the best possible compromise between grasp capabilities and design complexity. This approach is exemplified by defining technical specifications for the design of a multi-fingered robotic gripper intended to perform the tasks involved in a sterility testing process.

CONCEPTUAL DESIGN AND BIO-INSPIRED CONTROL OF A NEW SURGICAL SOFT ROBOTIC GRIPPER

This paper describes the design and development of a novel soft robotic gripper for minimally invasive surgery (MIS) intended to remove foreign bodies from the patient's body by imitating human esophageal swallowing motions. The robotic gripper operates as follows: after locating and contacting the foreign body, the last segment of the gripper is expanding or contracting to match the size and catch the targeted object, then pushes forward, or bends it with its legs and starts to remove the object by a rhythmic peristaltic (periodically repeated) motion. This mode of the gripper’s operation allows removing bodies of different sizes from the human body without damaging the surrounding tissues, especially the blood vessels. Both the segments and legs of the gripper are made of dielectric elastomer actuators (DEAs) capable of large deformations under the influence of an external electric field (EEF). A central pattern generator (CPG)- based controller and a Rowat-Selverston type oscillator are selected to control both discrete and rhythmic motions based on electrical voltage – equivalent elastic strain relations of the orthotropic silicone elastomer obtained by the finite element analysis (FEA). The robotic gripper is modelled and studied by the ANSYS Workbench and Matlab/Simulink software. The performed computer modeling shows that due to the simple modular structure and CPG controller, the proposed robotic gripper is much faster, more adaptive and shows better response compared with its pneumatic actuated analogues.

Finite Element Analysis of a Novel Robotic Gripper:Grabo

Robotic grippers, with several design variations in its jaws, are gaining popularity in commercial market worldwide in recent past. In fact, these customized robotic grippers are widely used for diverse end-applications in various arenas. We have aimed to optimize the mechanical design of such customized robotic grippers having two fingers (jaws), as those grippers are the most prevalent variants in industrial robots. In this study, a niche contigutive robotic gripper for direct adhesion contactis developed, to meet the technical trials of force-closure of grasp. The prime focus of our research is reduction of the tareweight of the prototype gripper keeping its strength unaltered under the threshold of grasping maximum payload. Through Finite Element Analysis and optimization thereof, material retention percentage is defined for the prototype gripper so as to improve its overall envelope.

Robotic Arm Gripper Using Force Sensor for Crop Picking Mechanism

A dynamic gripper with qualities that resemble the human hand as closely as possible is sought after in the field of robotics. The idea of a robotic arm has been used in various cutting-edge technology fields, including agriculture, to assist people or farmers in carrying out regular tasks, such as gathering fruit, etc. The robot arm's end effector is one of the essential parts of the robot that we can configure based on their tasks, such as a spraying adaptor for fertilization function or a gripper for the picking mechanism. Since fruits have a delicate and fragile surfaces, it is vital to have a gripper with a smooth contact surface that can apply the right amount of force to pick the fruits without causing any bruising that can degrade the crop's quality. Hence, this paper proposes a robotic arm gripper design for the crop-picking mechanism using a force sensor as the main component of the Arduino Uno embedded system. The reliability result for the chili obtained is around 95% showing that this design is promising for designing an adaptive robotic arm gripper.

A novel multi objective constraints based industrial gripper design with optimized stiffness for object grasping

Soft gripper design is a rising area of research due of its great possibilities in automation. One difficult problem in robot design is the ability to grasp a broader variety of items with variable stiffness, forms, and sizes in a single gripper. An ideal soft robotic gripper design with variable stiffness was designed in this research as a grasping model. Its distinctiveness is found in the methods utilized for modelling actuators and in the shifting stiffness characteristics of silicon soft gripper. When modelling the actuator in this case, multi-objective functions like gripping displacement and force transmission ratio are taken into account, and the actuator functions are controlled by the MDF (multiple degrees of freedom). The precise stiffness needed to grasp the item is then chosen using an adaptive optimization method. This enhanced weight-based horse herd (IHH) optimization method carries out the stiffness adjustment based on actuation pressure. Additionally, the suggested soft robotic gripper with variable stiffness employs the adaptive level set (ALS) technique to build the gripping force model. Additionally, several validations are offered in relation to the outcomes for item gripping by the suggested soft gripper. This shown that the results of the created soft gripper excelled those of other methods. The developed ABBIRB 1410 robot gripper type may enhance the work cycle in industrial applications and performs object grabbing with dependability and speed. The experimental validations show that the developed gripper model provides an enhanced object grasp with a range of curvatures, delivering a maximum pulling force of 121.07 kPa at 50 kpa, 119.15 kPa for patterned pulling, and 45.05 kPa for non-patterned pulling. The designed gripper type has a curve with a minimum size of 1.1 mm and a maximum size of 218 mm. Additionally, the soft gripper for industrial applications is examined with variously sized and weighted items. The suggested gripper model achieved an RMSE performance of 2.9 and a Pearson correlation of 0.993.

A novel design of a passive variable stiffness soft robotic gripper

Inspired by the twisting and hanging phenomenon of vines, this paper proposes and designs a passive variable stiffness soft robotic gripper to grasp an object in a simple and robust manner using the principle of jamming. This method has the characteristics of high reliability and good stability, which can achieve soft grasping and rigid load-bearing of the object. Firstly, according to two key issues, the design model of the gripper is proposed, the principle of the proposed gripper is analyzed, and the relationship between the stiffness of the gripper and the stiffness of the object is revealed. Secondly, the model of the robotic gripper is created using a conventional motor drive method, and the grasping process and deformation causes of the gripper are analyzed by using the principle of instability effect and large deformation principle. Finally, the experimental prototype is developed and the feasibility of the design principle and the grasping deformation process of the gripper are verified by gripping experiments.

Single-Fingered Reconfigurable Robotic Gripper With a Folding Mechanism for Narrow Working Spaces

This letter proposes a novel single-fingered reconfigurable robotic gripper for grasping objects in narrow working spaces. The finger of the developed gripper realizes two configurations, namely, the insertion and grasping modes, using only a single motor. In the insertion mode, the finger assumes a thin shape such that it can insert its tip into a narrow space. The grasping mode of the finger is activated through a folding mechanism. Mode switching can be achieved in two ways: switching the mode actively by a motor, or combining passive rotation of the fingertip through contact with the support surface and active motorized construction of the claw. The latter approach is effective when it is unclear how much finger insertion is required for a specific task. The structure provides a simple control scheme. The performance of the proposed robotic gripper design and control methodology was experimentally evaluated. The minimum width of the insertion space required to grasp an object is 4 mm (1 mm, when using a strategy).

Tunable force sensor based on carbon nanotube fiber for fine mechanical and acoustic technologies

Design of new smart prosthetics or robotic grippers gives a major impetus to low-cost manufacturing and rapid prototyping of force sensing devices. In this paper, we examine piezoresistive force sensors based on carbon nanotube fibers fabricated by a novel wet pulling technique. The developed sensor is characterized by an adjustable force range coupled with high sensitivity to enable the detection of a wide range of forces and displacements limited by the experimental setup only. We have demonstrated the applicability of the developed unit in tactile sensing, displacement sensing, and nanophone vibration monitoring system and evaluated its force sensing characteristics, i.e. displacement/force input and resistance/mechanical response. In the experiments it measures 0–115 N force range within 2.5 mm displacement. Moreover, the sensor demonstrates good linearity, low hysteresis, and stability when tested over 10 000 cycles. The developed sensor suits multiple applications in the field of soft and transparent sensors, nanophones, actuators, and other robotics devices for both regular and extreme environments, e.g. deep underwater and radioactive environment.

Efficient tactile encoding of object slippage

When grasping objects, we rely on our sense of touch to adjust our grip and react against external perturbations. Less than 200 ms after an unexpected event, the sensorimotor system is able to process tactile information to deduce the frictional strength of the contact and to react accordingly. Given that roughly 1,300 afferents innervate the fingertips, it is unclear how the nervous system can process such a large influx of data in a sufficiently short time span. In this study, we measured the deformation of the skin during the initial stages of incipient sliding for a wide range of frictional conditions. We show that the dominant patterns of deformation are sufficient to estimate the distance between the frictional force and the frictional strength of the contact. From these stereotypical patterns, a classifier can predict if an object is about to slide during the initial stages of incipient slip. The prediction is robust to the actual value of the interfacial friction, showing sensory invariance. These results suggest the existence of a possible compact set of bases that we call Eigenstrains. These Eigenstrains are a potential mechanism to rapidly decode the margin from full slip from the tactile information contained in the deformation of the skin. Our findings suggest that only 6 of these Eigenstrains are necessary to classify whether the object is firmly stuck to the fingers or is close to slipping away. These findings give clues about the tactile regulation of grasp and the insights are directly applicable to the design of robotic grippers and prosthetics that rapidly react to external perturbations.