Robotic Gripper Research Articles

Research on Additive Technique Parameter Optimization for Robotic Gripper Construction

Designing an industrial robot gripper suitable for today’s industry is a challenging task due to the rapid evolution of products. Industrial robots are involved in machining, the transfer of parts, control and assembly, and the number of tasks performed by robots are increasing. Robots need to have the capability to adapt to new jobs consisting of new parts and new trajectories, and in most cases the preferred end effectors are grippers. In turn, grippers need to be flexible enough in order to cope with these changes. For this research, the authors propose a new gripper design which is capable of handling a large variety of parts with different sizes and shapes. In this research, an electrically actuated four-jaw gripper, with the capability of parallel movement of its jaws, is presented that also has the capability to fold the clamping jaws two by two and become a two-jaw gripper. Since the design is most suitable for additive manufacturing techniques, different additive techniques are analyzed for the manufacturing of the gripper. In the second part of the paper, different setups of the 3D printers are considered, such as infill percentage, raster angle and layer height. The main material on focus is a PET with grinded carbon-fiber reinforcement, but different materials are used for a better comparison of the rigidity of the system. This comparison is also presented in this article. The analysis of the material and 3D printing parameters are tested with Standard D638-14 probes used in a traction testing machine. After performing the traction test, the results are compared with FEA analysis. An optimal solution based on the experimental tests is proposed for the manufacture of the proposed gripper design.

Geometric mechanics of ordered and disordered kirigami

The presence of incomplete cuts in a thin planar sheet can dramatically alter its mechanical and geometrical response to loading, as the cuts allow the sheet to deform strongly in the third dimension, most beautifully demonstrated in kirigami art-forms. We use numerical experiments to characterize the geometric mechanics of kirigamized sheets as a function of the number, size and orientation of cuts. We show that the geometry of mechanically loaded sheets can be approximated as a composition of simple developable units: flats, cylinders, cones and compressed Elasticae. This geometric construction yields scaling laws for the mechanical response of the sheet in both the weak and strongly deformed limit. In the ultimately stretched limit, this further leads to a theorem on the nature and form of geodesics in an arbitrary kirigami pattern, consistent with observations and simulations. Finally, we show that by varying the shape and size of the geodesic in a kirigamized sheet, we can control the deployment trajectory of the sheet, and thence its functional properties as an exemplar of a tunable structure that can serve as a robotic gripper, a soft light window or the basis for a physically unclonable device. Overall our study of disordered kirigami sets the stage for controlling the shape and shielding the stresses in thin sheets using cuts.

Additive manufacturing of inductive force sensor with NiZn-ferrite magnetic core for robotic gripper

Additive manufacturing of inductive force sensor with NiZn-ferrite magnetic core for robotic gripper

Additive manufacturing of flexible 3D surface electrodes for electrostatic adhesion control and smart robotic gripping

Mechanically flexible surface structures with embedded conductive electrodes are attractive in contact-based devices, such as those used in reversible dry/adhesion and tactile sensing. Geometrical shapes of the surface structures strongly determine the contact behavior and therefore the resulting adhesion and sensing functionalities; however, available features are often restricted by fabrication techniques. Here, we additively manufacture elastomeric structure arrays with diverse angles, shapes, and sizes; this is followed by integration of conductive nanowire electrodes. The fabricated flexible three-dimensional (3D) surface electrodes are mechanically compliant and electrically conductive, providing multifunctional ability to sense touch and to switch adhesion via a combined effect of shear- and electro adhesives. We designed soft, anisotropic flexible structures to mimic the gecko’s reversible adhesion, which is governed by van der Waals forces; we integrated nanowires to further manipulate the localized electric field among the adjacent flexible 3D surface electrodes to provide additional means to digitally tune the electrostatic attraction at the contact interface. In addition, the composite surface can sense the contact force via capacitive sensing. Using our flexible 3D surface electrodes, we demonstrate a complete soft gripper that can grasp diverse convex objects, including metal, ceramic, and plastic products, as well as fresh fruits, and that exhibits 72% greater electroadhesive gripping force when voltage is applied.

A Review on Technologies in Robotic Gripper

Robots are replacing human workers in many industries due which increases efficiency, productivity and thus results in high operational quality. An intelligent robot is the one which interact with the environment and can take the decisions itself. Domestic robots can also provide assistance to physically disabled or elder people in their day to day life. Pressing the demands of enhanced productivity has necessitated the deployment of robot to automate tasks (Baizid et al., 2015). A manipulator can be geared with end-effector to complete variety of tasks. Robotic grippers are used in agriculture, food processing industry, packaging of food, palletizing of boxes and many more. Grippers are the device that enables robots to grasp and grip objects. The designing of end effector should be done by considering its specific application in industry. On comparison of gripper with human hand, a robot’s gripper is very bounded in terms of mechanical movement, practical service and general applications. To use the full ability of robotic technology, the gripper must be designed more of a like human hand. This paper attempts to describe the different technologies of Robotic gripper which help the people which would be a Businessman as well as common people or Industrialist. The main target of this paper is to contribute some information on different on robotic grippers, since selection of gripper plays a fundamental part in robot’s productivity and performance.

Printed Directional Bending Sensor with High Sensitivity and Low Hysteresis for Human Motion Detection and Soft Robotic Perception.

We present a high-performance flexible bending strain sensor for directional motion detection of human hands and soft robotic grippers. The sensor was fabricated using a printable porous conductive composite composed of polydimethylsiloxane (PDMS) and carbon black (CB). The utilization of a deep eutectic solvent (DES) in the ink formulation induced a phase segregation between the CB and PDMS and led to a porous structure inside the printed films after being vapored. This simple and spontaneously formed conductive architecture provided superior directional bend-sensing characteristics compared to conventional random composites. The resulting flexible bending sensors displayed high bidirectional sensitivity (gauge factor of 45.6 under compressive bending and 35.2 under tensile bending), negligible hysteresis, good linearity (>0.99), and excellent bending durability (over 10,000 cycles). The multifunctional applications of these sensors, including human motion detection, object-shape monitoring, and robotic perceptions, are demonstrated as a proof-of-concept.

A wing-structured sensor with multiple mechanical stimuli differentiation capabilities toward multifunctional applications

Wearable sensing electronics capable of detecting and differentiating multiple mechanical stimuli are promising devices in the applications of healthcare monitoring, robotics, etc. However, most wearable sensors are developed for detecting only uniaxial mechanical stimuli, which severely hinders their practical applications that usually involve complex mechanical stimuli. Here, a wing-like multifunctional sensor (WMS) consisting of pressure sensing module in the middle and stretching sensing module in both wings is developed with the capability of detecting and differentiating pressure, stretching, convex and concave bending through distinct electrical signals variation trends. The hierarchical in-situ filling porous as pressure sensing layer and micro-wrinkled carbon nanotubes (CNTs)/Ag nanoflakes as stretch sensing layer achieve high sensitivity over a broad range in both pressure (sensitivity of 0.25 kPa−1 at 500 kPa) and strain sensing (Gauge factor of 140 at 150%). The pressure and stretch sensing module can work without interfering with each other, which is realized by structural design of lower Young’s modulus stretch sensing module and higher thickness and compressibility pressure sensing module. Then WMS is demonstrated in accurate detection of human kinesthesia, human-computer interface, identification of objects of various hardnesses and sizes by robotic gripper, and perception of environmental information through a crawling robot.

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.

Insect Tarsus-Inspired Compliant Robotic Gripper With Soft Adhesive Pads for Versatile and Stable Object Grasping

Grasping multiple object types (versatile object grasping) with a single gripper is always a challenging task in robotic manipulation. Different types of grippers, including rigid and soft, have been developed to try to achieve the task. However, each gripper type is still restricted to specific object types. In nature, many insects can be observed to use only one tarsus mechanism to cope with several tasks. They have a very high grasping capability with objects and can adhere to a variety of surface types. Inspired by insect tarsus, this paper proposes a novel underactuated, single cable-driven, compliant gripper design. The structure of the gripper is based on the hornet tarsus morphology with a proportional scale. An additional pulley-like structure is introduced to increase the generated grasping torque. To maintain the ability to automatically rebound back to the original position, a torsion spring is implemented at each joint. In order to stably grasp and hold objects, soft adhesive pads with an asymmetric sawtooth-like surface structure are attached at the tarsus segments. The performance of this insect tarsus-inspired gripper with the soft pads is evaluated by grasping 35 different objects of various sizes, shapes, and weights for comparison with industrial soft and rigid grippers. The proposed gripper shows a 100% success rate in grasping all objects, while the soft and rigid gripper success rates are 81.90% and 91.43% on average, respectively. We finally demonstrate the use of our gripper installed on a robot arm for pick-and-place and pouring tasks.

A dual-mode and enclosing soft robotic gripper with stiffness-tunable and high-load capacity

A dual-mode and enclosing soft robotic gripper with stiffness-tunable and high-load capacity

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.

Multimaterial 4D printing with a tunable bending model

Shape-memory polymer (SMP)-based functional structures may now be produced more efficiently via four-dimensional (4D) printing, benefiting from the recent advances in multi-material three-dimensional printing technologies. Composite material design using 4D printing has opened new possibilities for customizing the shape memory property of smart polymers. This work studies a design strategy to harness desirable morphing by 4D printing multimaterial composites with a focus on the detailed finite element (FE) procedure, experimental results, and soft robotic application. Composites with bilayer laminates consisting of a SMP and a flexible elastomer are constructed with variable thickness ratios to control the self-bending of the composite. FE simulations are used to understand the underlying processes of composite materials and to generate accurate predictions for the experimental results, which reduces cost and development time. The application of 4D printing and multi-material composite programming is demonstrated with a soft robotic gripper for manipulating fragile objects.

Thin wire segmentation and reconstruction based on a novel image overlap-partitioning and stitching algorithm in apple fruiting wall architecture for robotic picking

The layout of orchards usually requires the use of wires, to provide sturdy support. Such is the case of apple trees in fruiting wall architecture, where wires are conducive for mechanical harvesting and especially robotic picking. However, wires may cause damage to the robotic gripper, especially when direct picking occluded apple fruits. Hence, the importance of identifying the wires of a fruit wall architecture. In this study, a pixel-level segmentation network, BlendMask, was adopted to segment wires. The wires are thin and normally behind the branches or leaves, making difficult for their identification. Therefore, a novel data processing algorithm called image overlap-partitioning and stitching (IOS) is proposed for BlendMask to segment the wires. A total of 82 RGB (Red, Green, and Blue) images registered to create a raw dataset. The dataset was augmented and then overlap-partitioned into 12,736 images with a resolution of 800 × 1024 pixels and corresponding annotation files based on input size of BlendMask to make an overlap-partitioned dataset. Then BlendMask was trained with the overlap-partitioned dataset and tested on the other images, where additional stitching for the overlap-partitioned images was needed in the image testing dataset. Results showed that BlendMask with IOS obtained Intersection over Union (IoU) and pixel accuracy of 43.86 % and 61.01 %, respectively. It achieved a better average precision of 38.75 % with IoU of 0.5 on the overlap-partitioned dataset, which was 38.42 % higher than the full image dataset. Moreover, a reconstruction method based on feature point extraction and fitting was proposed to estimate wire skeletons, which achieved a reconstruction accuracy of 90.70 %. These results showed a promising potential using segmentation and reconstruction methods for identifying wires and thus providing a basis for robotic picking in modern orchards.

Estimation and early prediction of grip force based on sEMG signals and deep recurrent neural networks

Hands are used for communicating with the surrounding environment and have a complex structure that enables them to perform various tasks with their multiple degrees of freedom. Hand amputation can prevent a person from performing their daily activities. In that event, finding a suitable, fast, and reliable alternative for the missing limb can affect the lives of people who suffer from such conditions. As the most important use of the hands is to grasp objects, the purpose of this study is to accurately predict gripping force from surface electromyography (sEMG) signals during a pinch-type grip. In that regard, gripping force and sEMG signals are derived from 10 healthy subjects. Results show that for this task, recurrent networks outperform nonrecurrent ones, such as a fully connected multilayer perceptron (MLP) network. Gated recurrent unit (GRU) and long short-term memory (LSTM) networks can predict the gripping force with R-squared values of 0.994 and 0.992, respectively, and a prediction rate of over 1300 predictions per second. The predominant advantage of using such frameworks is that the gripping force can be predicted straight from preprocessed sEMG signals without any form of feature extraction, not to mention the ability to predict future force values using larger prediction horizons adequately. The methods presented in this study can be used in the myoelectric control of prosthetic hands or robotic grippers.

Design and Implementation of an Artificial Intelligence of Things-Based Autonomous Mobile Robot System for Cleaning Garbage

This article proposes an autonomous mobile robot (AMR) system based on the artificial intelligence of things (AIoT) for collecting garbage. The proposed system consists of an AMR subsystem, a robot operating system (ROS) robot subsystem, an AI garbage recognition subsystem, solar power supply subsystem, and a smart trash can subsystem. The AMR subsystem uses a motor drive, which is built on an aluminum alloy metal base and is connected to an AI edge computing module via an app, to control the robot. The ROS subsystem is built on an edge computing module, which is coupled with the motor drive module to control the robot arm and the robot gripper, and is connected to the AI edge computing module to control the robot arm through the app. The AI garbage recognition subsystem recognizes the types of waste in real time through an image sensor (camera). This subsystem adopts multiple sensors that can monitor the level of waste in the trash can. Moreover, a map on a web platform locates all trash cans. Servomotors enable automatic opening and closing of the lid to prevent the unpleasant smell of garbage from wafting. A user connects to the AI edge computing module through an app to monitor the robot and control the movement of the robot to any location on the map.

Soft and lightweight fabric enables powerful and high-range pneumatic actuation.

Soft structures and actuation allow robots, conventionally consisting of rigid components, to perform more compliant, adaptive interactions similar to living creatures. Although numerous functions of these types of actuators have been demonstrated in the literature, their hyperelastic designs generally suffer from limited workspaces and load-carrying capabilities primarily due to their structural stretchability factor. Here, we describe a series of pneumatic actuators based on soft but less stretchable fabric that can simultaneously perform tunable workspace and bear a high payload. The motion mode of the actuator is programmable, combinable, and predictable and is informed by rapid response to low input pressure. A robotic gripper using three fabric actuators is also presented. The gripper demonstrates a grasping force of over 150 N and a grasping range from 70 to 350millimeters. The design concept and comprehensive guidelines presented would provide design and analysis foundations for applying less stretchable yet soft materials in soft robots to further enhance their practicality.

Highly Sensitive, Ultrawide Range, and Multimodal Flexible Pressure Sensor with Hierarchical Microstructure Prepared By a One‐Step Laser Printing Process

AbstractThe hierarchical microstructures composed of surface microstructures and porous structures on soft materials have attracted more and more attention because of their effectiveness in improving flexible sensor performance. However, the preparation methods of the hierarchical microstructures are faced with the problems induced by complex process, high cost, and low efficiency. Up to now, there is no one‐step method that can directly realize the hierarchical microstructure on soft materials, even polydimethylsiloxane (PDMS) as the most common soft material. Herein, using glucose additive as porogen and endothermic agent, a simple, low‐cost, and fast laser thermal printing one‐step process is developed for the preparation of porous PDMS film with surface microstructure. Based on the hierarchical structural PDMS film, a multimodal flexible pressure sensor that can sense tension, compression, and torsion is realized. Due to the hierarchical microstructure composed of surface microstructure and porous structure, the sensor exhibits a high sensitivity of 36.66 kPa−1 in 0–0.6 kPa, a ultrawide sensing range of 3000 kPa, a 100% elongation, and a high gauge factor of 315.13. The application of the sensor in the monitoring of physiological signals, dynamic loads, and robotic flexible gripper demonstrates the potential of the laser thermal printing process in porous PDMS‐based sensors.

An Electrostatically Actuated Gecko Adhesive Clutch

AbstractThis work presents a new variation on electrostatic clutches that uses gecko‐inspired adhesives instead of friction for its braking force. As a result, it requires no power or normal pressure to remain engaged or disengaged. It requires only a brief pulse of voltage to switch states. In some applications, this capability is desirable for safety reasons. As an illustration, the clutch is incorporated into the needle‐driving axis of a magnetic resonance compatible teleoperated robotic system. Adding the clutch has no effect on imaging quality and provides a fail‐safe brake to prevent the needle axis from dropping in the event of a power failure. As a second application, the clutch is integrated into a force‐controlled robotic gripper where it allows the motor to be turned off while maintaining a static grasping force. In both applications, the 20 ms response time of the clutch prototypes is advantageous to prevent any motion immediately after receiving a braking command. This work additionally presents details on the design and manufacturing process of the gecko‐inspired clutch, including a new, non‐uniform profile for the microscopic adhesive features. The fabricated prototypes are thin (305 µm per layer) and flexible. They provide a controllable, adhesive braking force of 60 kPa per layer. Multiple layers can be assembled to increase the braking force.

ROS-Industrial based robotic cell for Industry 4.0: Eye-in-hand stereo camera and visual servoing for flexible, fast, and accurate picking and hooking in the production line

The fourth industrial revolution envisages the use of modern smart technologies to automate traditional manufacturing and industrial practices. However, industrial robots execute mostly pre-programmed jobs and are not able to face challenging tasks in unstructured environments. Industry 4.0 pushes for flexibility on target changes and autonomy. In line with the new principles of Industry 4.0, the proposed work describes an autonomous industrial cell that employs several smart technologies for loading jewelry pieces from a conveyor belt to a hooking frame built on purpose. The cell involves an industrial robot, a custom gripper, pneumatic and electric actuators with the aim of moving and opening the frame hooks, and a custom vision pipeline for detecting the feature of interest during the picking and hooking phases. The implemented pipeline makes use of a stereo camera pair mounted under the robot gripper and two fixed monocular cameras. The method employs HOG feature descriptors and machine learning algorithms for the detection. The software architecture is a component-based designed architecture that uses ROS as the underlying framework and ROS-Industrial packages to control the robot. The robot is controlled with position-based commands to reach intermediate positions in the workspace and with velocity command to implement a visual servoing control scheme that runs at 30 Hz and adjusts the robot position with the feedback of the vision during picking and hooking. The proposed visual servoing approach, thanks to the design of the stereo camera and choice of the optics, is able to perceive the features until the final movement phase, differently from most of the visual servoing employed in the literature that, due to the use of RGB-D camera or other vision apparatus, use an open control loop at a standoff distance. The presented work reaches an accuracy of 95% with a cycle time under 8 s.

A Hierarchical Manipulation Scheme for Robotic Sorting of Multiwire Cables With Hybrid Vision

A key step in manufacturing multiwire cables in the factory is to sort the wires according to the desired color sequence. Owing to their deformable nature, it is challenging for robots to carry out sorting tasks on wires. Specifically, the wires are very small in size, multiple wires are closely coupled, and the initial color sequence is random. In this article, a new hierarchical manipulation scheme is proposed for the robotic sorting of multiwire cables. The sorting task is carried out by a vision-guided robotic gripper, whose control objective is formulated in two layers: 1) the high-level planner outputs an optimal grasping sequence and the grasping position, according to the initial status of wires; and 2) the low-level planner shapes the gripper's reference trajectory by monitoring the wires' changes online. The proposed hierarchical manipulation scheme explores the advantages of both 3-D and 2-D vision to effectively deal with the existing issues (e.g., overlapping and occlusion) of multiple wires and also unforeseen changes during the sorting task (e.g., slipping and collision), which then guarantees the feasibility of wire sorting. Experimental results in various scenarios are presented to validate the performance of the proposed scheme.