Grasping impact force control of a flexible robotic gripper using a piezoelectric actuator

This paper presents robust force tracking control of a flexible beam during a grasping operation using a piezoceramic actuator. Equations describing the motion of the gripper in conditions of contact and noncontact are derived based on the cantilever beam. In this study, contact force is regulated, in addition to the impact force generated at the instant of contact, based on variable structure model reference adaptive control theory using only force measurements. For the derivation of the control law, it is assumed that parameters of the beam and the stiffness of the object are unknown. Computer simulations show the effectiveness the controller. This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999     

The complexity of oblivious plans for orienting and distinguishing polygonal parts

In the problem ofparts feeding we are given a class of feasible operations for reorienting a part, and we are asked to find a fixed sequence of these operations which is guaranteed to bring the part into a known goal orientation from any possible initial orientation. Goldberg addressed this problem in [2], and showed that, for planar polygonal parts, there is always a sequence of simple operations which can be performed by a simple parallel-jaw gripper, which is guaranteed to orient the part (up to symmetry) without the use of any sensor information; he also conjectured thatO(n) steps are sufficient.In this paper we prove Goldberg's conjecture by explicitly constructing plans of at most2n – 1 steps for orienting polygonal parts in this model. We also give a lower bound on the number of steps required for such plans to show that this upper bound is tight.Finally, we extend these results to the problem ofdistinguishing among a finite set of parts using minimal sensing. Specifically, we assume that we are given a set of known polygonal parts, and a parallel-jaw gripper able to sense the distance between its jaws upon closure. We construct a simple oblivious plan of linear complexity which, when presented with a polygonal part, determines the index of this part.This research was supported in part by the NSF under Grant CCR-9207422, and by a Zumberge Fellowship. A preliminary version of this paper appeared in theProceedings of the Fourth Canadian Conference on Computational Geometry [1].     

Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces

It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult tо analyze with conventional analytical methods. Here, a novel design of an adaptive neuro fuzzy inference system (ANFIS) for estimation contact forces of a new adaptive gripper is presented. Since the conventional analytical methods is a very challenging task, fuzzy logic based systems are considered as potential candidates for such an application. The main points of this paper are in explanation of kinetostatic analyzing of the new gripper structure using rigid body model with added compliance in every single joint. The experimental results can be used as training data for ANFIS network for estimation of gripping forces. An adaptive neuro-fuzzy network is used to approximate correlation between contact point locations and contact forces magnitudes. The simulation results presented in this paper show the effectiveness of the developed method. This system is capable to find any change in ratio of positions of the gripper contacts and magnitudes of the contact forces and thus indicates state of both finger phalanges.     

A compliant self-adaptive gripper with proprioceptive haptic feedback

Grippers and robotic hands are an important field in robotics. Recently, the combination of grasping devices and haptic feedback has been a promising avenue for many applications such as laparoscopic surgery and spatial telemanipulation. This paper presents the work behind a new self-adaptive, a.k.a. underactuated, gripper with a proprioceptive haptic feedback in which the apparent stiffness of the gripper as seen by its actuator is used to estimate contact location. This system combines many technologies and concepts in an integrated mechatronic tool. Among them, underactuated grasping, haptic feedback, compliant joints and a differential seesaw mechanism are used. Following a theoretical modeling of the gripper based on the virtual work principle, the authors present numerical data used to validate this model. Then, a presentation of the practical prototype is given, discussing the sensors, controllers, and mechanical architecture. Finally, the control law and the experimental validation of the haptic feedback are presented.     

Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult make decision strategies using conventional techniques. Here, an adaptive neuro fuzzy inference system (ANFIS) for controlling input displacement and object recognition of a new adaptive compliant gripper is presented. The grasping function of the proposed adaptive multi-fingered gripper relies on the physical contact of the finger with an object. This design of the each finger has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Fuzzy based controllers develop a control signal according to grasping object shape which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS strategy, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.     

Friction and part curvature in parallel- jaw grasping

When grasping a part with a parallel-jaw gripper, the part will generally rotate due to kinematic constraints. Predicting the part's final orientation is useful for grasping and for planning sequences of gripper motions to orient parts. For a given part geometry, the grasp function maps initial orientations to final orientations. Previously, we studied polygonal and algebraic parts in the absence of friction. This article considers how Coulomb friction affects the grasp function. We consider two models of Coulomb friction. For a deterministic model, we show that the grasp function of any polygonal part can be represented with a piecewise linear function that we call a step-ramp function. We then show that any step-ramp function is the grasp function of a curved part operating under zero friction. Both contain ranges of orientations where the part does not rotate when grasped. This yields our primary result, that any part with deterministic friction has equivalent grasp mechanics to a “dual” part under zero friction. We then apply previous results to derive grasp plans that orient parts in the presence of friction. We also give an algorithm for planning under a non-deterministic model of Coulomb friction, and give bounds on the friction coefficient needed to insure the existence of such plans. © 1995 John Wiley & Sons, Inc.     

Viable cell handling with high aspect ratio polymer chopstick gripper mounted on a nano precision manipulator

This paper presents the development of an optimized contact technique for viable cell manipulation utilizing a high aspect ratio polymer chopstick gripper. The gripper consists of a 2 μm thick metal heater layer and a 60 μm thick SU-8 layer and is fabricated by a typical UV-LIGA process using SiO₂ as sacrificial layer. The grippers were completely released, de-tethered and assembled as end-effectors on to a nano precision manipulator to perform cell manipulation. Successful pick-and-place of a suspended normal rat kidney cell in phosphate buffered saline solution was demonstrated. The major cell-damage mechanisms associated with contact techniques were identified and alleviated by optimizing the handling force and operating temperature of the polymer gripper. The viability of cells handled with this optimized contact technique was demonstrated by labeling cells with a fluorescent dye. The developed technique will enable incorporation of simple, viable, and repeatable cell handling capabilities into the generic micromanipulators used in the biological laboratories.     

A novel electrostatic based microgripper (cellgripper) integrated with contact sensor and equipped with vibrating system to release particles actively

This paper presents design and simulation of a novel electrostatic microelectromechanical systems gripper with an integrated capacitive contact sensor. Moreover, this microgripper is able to employ vibration to release micro objects (cells) actively. Lateral comb drive system is used to close the gap between the gripper arms and hold the objects while the transverse comb differential capacitances act as a contact sensor to prevent damaging the fragile micron-sized particles specifically biological cells. In addition, the capability of the microgripper in generating vibration at the end-effectors electrostatically is an advantage to facilitate releasing process by overbalancing the adhesion forces between the particle and the gripper arm. Finite element analysis based simulations are carried out to estimate the behavior of the microgripper while the standard SOI-MUMPs micromachining process is proposed for fabrication of the microgripper.     

Modeling, analysis, and performance evaluation of a robotic grippersystem for limp material handling

This paper presents an analytical model of a flat surfaced robotic gripper designed to automate the process of reliable, rapid and distortion-free limp material handling. The designed gripper prototype is integrated with an industrial robot manipulator. The gripper geometry and its grasp stability are justified. Performance of the overall system is experimentally tested, based on a set of industry dictated operational constraints. It is found that the gripper system has high reliability, grasp stability, and that it is capable of rapid rates of manipulation.     

Easily manageable, electrothermally actuated silicon micro gripper

This paper presents a new batch process to fabricate thermally driven silicon micro grippers for handling and manipulation objects smaller than 25 μm. To achieve a robust gripper gearing with fine gripping tips, silicon on insulator (SOI) technology is used. The flexure gearing is driven by two integrated thermal expansion actuators that are moving in opposite directions and are actuated by Joule heating. In addition, a customized gripper mounting mechanism is presented, which offers fast and easy gripper handling, resulting in reduced tooling time and lower costs for the user. Finally, the experimental results and electrical characteristics for the sophisticated gripper design are presented.     

Neural network approach to firm grip in the presence of small slips

This paper presents a two stage method for constructing a firm grip that can tolerate small slips of the fingertips. The fingers are assumed to be of frictionless contact type. The first stage was to formulate the interaction in the gripper–object system as a linear complementarity problem (LCP). Then it was solved using a special neural network to find minimal fingers forces. The second stage was to use the obtained results in the first stage as a static mapping in training another neural network. The second neural network training included emulating the slips by random noise in the form of changes in the positions of the contact points relative to the reference coordinate system. This noisy training increased robustness against unexpected changes in fingers positions. Genetic algorithms were used in training the second neural network as global optimization techniques. The resulting neural network is a robust, reliable, and stable controller for rigid bodies that can be handled by a robot gripper. © 2001 John Wiley & Sons, Inc.     

多传感器手爪的研究

本文介绍了一种多传感器机械手爪，手爪上安装了接近觉接触觉滑觉滑力觉滑热觉与温觉等５种传感器。该手爪与简易视觉结合能使机器人完成一些不同截面形状和材质工件的识别。     

Grasping and in-hand manipulation: experiments with a reconfigurable gripper

This paper addresses the problem of grasping and manipulating three-dimensional objects with a reconfigurable gripper equipped with two parallel plates whose distance can be adjusted by a computer-controlled actuator. The bottom plate is a bare plane and the top one carries a rectangular grid of actuated pins that can translate in discrete increments under computer control. We propose to use this gripper to immobilize objects through frictionless contacts with three of the pins and the bottom plate, and to manipulate an object within a grasp by planning the sequence of pin configurations that will bring this object to a desired position and orientation. A detailed analysis of the problem geometry in configuration space was used in a previous paper to devise simple and efficient algorithms for grasp and manipulation planning. We have constructed a prototype of the gripper and this paper presents our experiments.     

Adaptive neuro-fuzzy prediction of grasping object weight for passively compliant gripper

The development of universal grippers able to pick up unfamiliar objects of widely varying shapes and surfaces is a very challenging task. Passively compliant underactuated mechanisms are one way to obtain the gripper which could accommodate to any irregular and sensitive grasping objects. The purpose of the underactuation is to use the power of one actuator to drive the open and close motion of the gripper. The fully compliant mechanism has multiple degrees of freedom and can be considered as an underactuated mechanism. This paper presents a new design of the adaptive underactuated compliant gripper with distributed compliance. The optimal topology of the gripper structure was obtained by iterative finite element method (FEM) optimization procedure. The main points of this paper are in explanation of a new sensing capability of the gripper for grasping and lifting up the gripping objects. Since the sensor stress depends on weight of the grasping object it is appropriate to establish a prediction model for estimation of the grasping object weight in relation to sensor stress. A soft computing based prediction model was developed. In this study an adaptive neuro-fuzzy inference system (ANFIS) was used as soft computing methodology to conduct prediction of the grasping objects weight. The training and checking data for the ANFIS network were obtained by FEM simulations.     

Development of a new noncontact gripper using swirl vanes

In this paper, we proposed a new noncontact gripper called as swirl gripper. It generates swirling air flow to create an upward lifting force. This force can be used to pick up a work piece placed underneath the swirl gripper without any contact. In comparison with conventional pneumatic noncontact grippers, the uniqueness of the new gripper lies in that it is electrically (rather than pneumatically) activated. We carry out this study for clarifying the mechanism of the swirl gripper. First, we show the design of the swirl gripper and briefly illustrate the mechanism by which it forms a negative pressure to create a lifting force. Then, we experimentally investigate the characteristics of the pressure distribution, based on which a theoretical analysis on the swirling flow is conducted. Furthermore, we measure the relationship between the lifting force and gap clearance and reveal that there exists a levitation zone where a work piece can maintain a stable levitation. Finally, we verify the practicability by successfully noncontact handling a Φ300 mm silicon wafer with four swirl grippers.     

gripper

Grasping of objects has been a challenging task for robots. The complex grasping task can be defined as object contact control and manipulation subtasks. In this paper, object contact control subtask is defined as the ability to follow a trajectory accurately by the fingers of a gripper. The object manipulation subtask is defined in terms of maintaining a predefined applied force by the fingers on the object. A sophisticated controller is necessary since the process of grasping an object without a priori knowledge of the object's size, texture, softness, gripper, and contact dynamics is rather difficult. Moreover, the object has to be secured accurately and considerably fast without damaging it. Since the gripper, contact dynamics, and the object properties are not typically known beforehand, an adaptive critic neural network (NN)-based hybrid position/force control scheme is introduced. The feedforward action generating NN in the adaptive critic NN controller compensates the nonlinear gripper and contact dynamics. The learning of the action generating NN is performed on-line based on a critic NN output signal. The controller ensures that a three-finger gripper tracks a desired trajectory while applying desired forces on the object for manipulation. Novel NN weight tuning updates are derived for the action generating and critic NNs so that Lyapunov-based stability analysis can be shown. Simulation results demonstrate that the proposed scheme successfully allows fingers of a gripper to secure objects without the knowledge of the underlying gripper and contact dynamics of the object compared to conventional schemes.     

Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper

The prerequisite for new versatile grippers is the capability to locate and perceive protests in their surroundings. It is realized that automated controllers are profoundly nonlinear frameworks, and a faultless numerical model is hard to get, in this way making it troublesome to control utilizing tried and true procedure. Here, a design of an adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict optimal inputs displacement of the gripper according to experimental tests and shapes of grasping objects. Instead of minimizing the observed training error, SVR _poly and SVR _rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach compared to other soft computing methodology.     

The influence of the radial basis function training strategy on the performance of a neural gripper

In this article, a neural network-based grasping system that is able to collect objects of arbitrary shape is introduced. The grasping process is split into three functional blocks: image acquisition and processing, contact point estimation, and contact force determination. The paper focuses on the second block, which contains two neural networks. A competitive Hopfield neural network first determines an approximate polygon for an object outline. These polygon edges are the input for a supervised neural network model [radial basis function (RBF) or multilayer perceptions], which then defines the contact points. Tests were conducted with objects of different shapes, and experimental results suggest that the performance of the neural gripper and its learning rate are significantly influenced by the choice of supervised training model and RBF learning algorithm.     

Adaptive neuro fuzzy controller for adaptive compliant robotic gripper

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult то control using conventional techniques. Here, a novel design of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling input displacement of a new adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Since the conventional control strategy is a very challenging task, fuzzy logic based controllers are considered as potential candidates for such an application. Fuzzy based controllers develop a control signal which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS controller, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.     

Design, implementation and testing of electrostatic SOI MUMPs based microgripper

This paper presents a detail modeling, finite element analysis and testing results of MEMS based electrostatically actuated microgripper. Interdigitated lateral comb pairs have been used to actuate the microgripper. The microgripper is optimized using standard SOI-MUMPs technology in L-Edit of MEMS-Pro with dual jaws actuation at low voltages. Coupled electromechanical finite element analysis performed in COVENTOR-WARE shows total displacement of 15.5 μm at jaws tip at 50 V, which is quite comparable to experimental result of 17 μm displacement at the tip of gripper jaw for the same voltage. Micromanipulation experiments have successfully demonstrated the gripping, holding micro-objects between 53 and 70 μm in size. The simulated model is used to study detail profile of Von Mises stresses and deformations in the model. It is noted that maximum stress in microgripper is 200 MPa which is much smaller than yield stress of 7 GPa. The slight difference between finite element analysis and experimental results is because of small variations in process material parameters. The total size of gripper is 5.03 × 6.5 mm².