Query: micro gripper

title: Design and Control of a Passive Compliant Piezo Actuated Micro Gripper With Hybrid Flexure Hinges
abstract: This article presents the design, control, and test of a piezo based passive compliant micro gripper capable of combining position and force control. The gripper's structure is compact due to a super large amplification ratio of 52 that is achieved by a two stage displacement amplifier. The position/force switching control strategy is utilized for the position and force regulation. Particularly, a dual feedforward controller combining Gaussian process regression based hysteresis compensator and input shape technique based vibration suppressor is employed to the position regulation, while a hybrid controller composed of dual feedforward and PID feedback is used for the precision force control. Compared to the conventional design, the system is simplified because displacement sensor is removed. In order to protect the micro object from contact, the passive compliance design method is introduced to the gripper tip. An experimental study, executing grasp hold release operations of a gold wire, is carried out to validate the feasibility of the gripper system. The result confirms that the proposed scheme is feasible and the stable clamping operations can be achieved.

title: Capillary Forces between Concave Gripper and Spherical Particle for Micro Objects Gripping
abstract: The capillary action between two solid surfaces has drawn significant attention in micro objects manipulation. The axisymmetric capillary bridges and capillary forces between a spherical concave gripper and a spherical particle are investigated in the present study. A numerical procedure based on a shooting method, which consists of double iterative loops, was employed to obtain the capillary bridge profile and bring the capillary force subject to a constant volume condition. Capillary bridge rupture was characterized using the parameters of the neck radius, pressure difference, half filling angle, and capillary force. The effects of various parameters, such as the contact angle of the spherical concave gripper, the radius ratio, and the liquid bridge volume on the dimensionless capillary force, are discussed. The results show that the radius ratio has a significant influence on the dimensionless capillary force for the dimensionless liquid bridge volumes of 0.01, 0.05, and 0.1 when the radius ratio value is smaller than 10. The effectiveness of the theorical approach was verified using simulation model and experiments.

title: Development of an Annular Shaped Bernoulli Gripper for Contactless Gripping of Large Size Silicon Wafer
abstract: Silicone wafer gripping and transportation have been widely utilized in wafer fabrication process, such as chemical mechanical polishing, grinding, epitaxial growth, etc. This paper develops a novel annular shaped gripper based on Bernoulli effects with the aim of contactless gripping of large size and thin silicon wafer. The structural design and working principle of this annular shaped Bernoulli gripper are presented. A computational fluid dynamic (CFD) model is established to study the adsorption performance of the gripper when gripping an 8 inch silicon wafer. Numerical simulation results showed that the gripper can generate an annular shaped pressure distribution to adsorb the silicon wafer which can reduce the wafer's deformation. Then, the Bernoulli gripper was fabricated, and the 8 inch silicon wafer's gripping experiments demonstrated that the gripper has generally low suction curve gradient. Thus, the developed annular shaped Bernoulli gripper could be utilized for contactless gripping and transportation of large size and thin silicon wafers in potential industrial applications.

title: The design of a force feedback soft gripper for tomato harvesting
abstract: 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 095 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.

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

title: Controlling and Stabilizing the Position of Remotely Operated Underwater Vehicle Equipped with a Gripper
abstract: Gripper mounted remotely operated underwater vehicle (ROUV) delivers more attractive and flexible solution to grasp the wanted objects inside the deep water. The main aim of this research is to stabilize and control the position of fully actuated (ROUV) equipped with a gripper. The nonlinear model based observer along with proportional, integral and derivative (PID) controller designed to stabilize and control the position of fully actuated ROUV along with the motion of attached gripper in it. However, the PID controller utilize to control the altitude of the vehicle and nonlinear based observer with PID controller designed to control and stabilize the attitude of ROUV. The designed control algorithm applied on the model of six degrees of freedom (6 DOF) ROUV attached with a gripper that has (2 DOF) that makes total (8 DOF) to control. The hydrodynamic behavior, instability that produces by the arm that is the main reason to design two different controllers to control and stabilize the altitude and attitude of ROUV. Hence, it observes that designed control scheme has better stability and better transient behavior.

title: A Multimodal, Enveloping Soft Gripper: Shape Conformation, Bioinspired Adhesion, and Expansion Driven Suction
abstract: A key challenge in robotics is to create efficient methods for grasping objects with diverse shapes, sizes, poses, and properties. Grasping with hand like end effectors often requires careful selection of hand orientation and finger placement. Here, we present a fingerless soft gripper capable of efficiently generating multiple grasping modes. It is based on a soft, cylindrical accordion structure containing coupled, parallel fluidic channels, which are controlled via pressure supplied from a single fluidic port. Inflation opens the gripper orifice for enveloping an object, while deflation allows it to produce grasping forces. The interior is patterned with a gecko like skin that increases friction, enabling the gripper to lift objects weighing up to 20 N. Our design ensures that fragile objects, such as eggs, can be safely handled, by virtue of a wall buckling mechanism. In reverse, the gripper can be deflated to reach into an opening or orifice then inflated to grasp objects with handles or cavities. The gripper may also integrate a lip that enables it to form a seal and, upon inflating, to generate suction for lifting objects with flat surfaces. In this article, we describe the design and fabrication of this device and present an analytical model of its behavior when operated from a single fluidic port. In experiments, we demonstrate its ability to grasp diverse objects, and show that its performance is well described by our model. Our findings show how a fingerless soft gripper can efficiently perform a variety of grasping operations. Such devices could improve the ability of robotic systems to meet applications in areas of great economic and societal importance.

title: End to End Deep Learning by MCU Implementation: An Intelligent Gripper for Shape Identification
abstract: This paper introduces a real time processing and classification of raw sensor data using a convolutional neural network (CNN) The established system is a microcontroller unit (MCU) implementation of an intelligent gripper for shape identification of grasped objects. The pneumatic gripper has two embedded accelerometers to sense acceleration (in the form of vibration signals) on the jaws for identification. The raw data is firstly transferred into images by short time Fourier transform (STFT) and then the CNN algorithm is adopted to extract features for classifying objects. In addition, the hyperparameters of the CNN are optimized to ensure hardware implementation. Finally, the proposed artificial intelligent model is implemented on a MCU (Renesas RX65N) from raw data to classification. Experimental results and discussions are introduced to show the performance and effectiveness of our proposed approach.

title: Analytical Model and Computing Optimization of a Compliant Gripper for the Assembly System of Mini Direct Current Motor
abstract: This study proposes a combination of kinematics based design method, statistic method, and TLBO algorithm to solve computing optimization for a compliant gripper. It is employed in an assembly system of mini direct current motor. First, the kinematic models of the gripper are developed. The static model is paid attention. Next, the dynamic model is established based on Lagrange's principle. Then, a multi criteria optimization for the gripper is conducted by Taguchi method integrated with TLBO algorithm. Finally, the FEA and experiments are implemented to verify the optimal results and evaluate the performances of the compliant gripper. The results indicated that theoretical models are in good accord with the results from simulations and experiments. Additionally, the performance of the present method is superior to PSO algorithm. The results revealed that the compliant gripper allows a displacement up to 3000 um and the amplification ratio of 12 times. The compliant gripper is a potential application for the mini direct current motor assembly system.

title: Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks
abstract: Soft grippers are gaining significant attention in the manipulation of elastic objects, where it is required to handle soft and unstructured objects, which are vulnerable to deformations. The crucial problem is to estimate the physical parameters of a squeezed object to adjust the manipulation procedure, which poses a significant challenge. The research on physical parameters estimation using deep learning algorithms on measurements from direct interaction with objects using robotic grippers is scarce. In our work, we proposed a trainable system which performs the regression of an object stiffness coefficient from the signals registered during the interaction of the gripper with the object. First, using the physics simulation environment, we performed extensive experiments to validate our approach. Afterwards, we prepared a system that works in a real world scenario with real data. Our learned system can reliably estimate the stiffness of an object, using the Yale OpenHand soft gripper, based on readings from Inertial Measurement Units (IMUs) attached to the fingers of the gripper. Additionally, during the experiments, we prepared three datasets of IMU readings gathered while squeezing the objects two created in the simulation environment and one composed of real data. The dataset is the contribution to the community providing the way for developing and validating new approaches in the growing field of soft manipulation.