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。     

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.     

Proposal of a shape adaptive gripper for robotic assembly tasks

This paper proposes a novel robotic gripper used for assembly tasks that can adaptively grasp objects with different shapes. The proposed hand has a combined structure between two kinds of shape adaptive mechanisms where one is the granular jamming and the other is a multi-finger mechanism driven by a single wire. Due to the effect of the two shape adaptive mechanisms, the pose of a grasped object does not change during an assembly operation. The proposed hand has four fingers where two are the active ones and the other two are the passive ones. The pose of the grasped object can be uniquely determined since the passive fingers are used to orient an object placed on a table before the active fingers are closed to grasp it. Assembly experiments of some kinds of parts are shown to validate the effectiveness of our proposed gripper.     

基于八角环二维力传感器抓取力控制研究

设计了一种小型八角环二维力传感器,利用ANSYS仿真软件对传感器进行了有限元和应变节点分析,通过传感器静态标定实验得到了标定解耦矩阵,解决了由于加工误差和应变计粘贴误差带来的力矩耦合问题.设计了二指平行手爪,搭建了基于力外环控制的夹持器系统.运用手爪测量了不同材料之间的最大静摩擦系数.实际的抓取实验验证了基于力比例控制的稳定抓取具有良好效果.     

SCARA based peg-in-hole assembly using compliant IPMC micro gripper

Robotic assembly is difficult as there always exist position errors between two mating parts. Compliance is added in a selective compliant assembly robot arm (SCARA) in the form of a two ionic polymer metal composite (IPMC) fingers based micro gripper. This micro gripper is integrated at the end effector position of a SCARA robot. Peg-hole interaction is analytically modeled and based on it the force required to correct the lateral and angular errors by IPMC is calculated. A proportional-derivative (PD) controller is designed to actuate the IPMC to get the desired force for correcting the peg position before assembly. Simulations and experiments were carried out by developing an IPMC micro gripper and using it to analyze various cases of peg in hole assembly. The experimental results prove that adding compliance through IPMC helps in peg-in-hole assembly.     

Position/force hybrid control system for high precision aligning of small gripper to ring object

A position/force hybrid control system based on impedance control scheme is designed to align a small gripper to a special ring object. The vision information provided by microscope vision system is used as the feedback to indicate the position relationship between the gripper and the ring object. Multiple image features of the gripper and the ring object are extracted to estimate the relative positions between them. The end-effector of the gripper is tracked using the extracted features to keep the gripper moving in the field of view. The force information from the force sensor serves as the feedback to ensure that the contact force between the gripper and the ring object is limited in a small safe range. Experimental results verify the effectiveness of the proposed control strategy.     

虚拟手抓持力觉生成算法真实性的评价

目的 虽然许多学者研发了多种虚拟手交互触力觉生成算法,但是如何评价虚拟手交互触力觉生成算法的真实性是一个富有挑战性的新问题,值得深入研究.方法 构建手指抓持力测量平台,设计3种抓持姿态下指尖静力抓持球体实验内容,测得各指尖作用力的实测值;通过虚拟手静力抓持力觉生成算法,求得这3种抓持姿态下各手指作用力的理论值;对实测值进行统计和分析,并与理论值进行对比和讨论;结果 日常抓持经验和实测值是完全相符的,实测值和理论值很接近且偏差均在可接受范围之内.单个手指作用力或多个手指合力的实测值与理论值的偏差均在1%6%.结论 本文实现了一种基于物理的实验方法,评价和分析了虚拟手静力抓持力觉生成算法的真实性,证实此算法可以逼真地生成虚拟手抓持力,可应用于具有力反馈的自然的虚拟手交互.     

水下机器人手爪力感知系统研究

提出了一种水下机器人手爪力感知系统的组成结构,该力感知系统由一个六维力/力矩传感器和三指夹持器指端的三个指力传感器组成,本文介绍了上述两种传感器的设计和标定,并对利用该手爪系统抓取物体进行了实际测试和分析,实验结果表明;所设计的力感知系统能够实时地感知腕力和夹持力信息,可以满足机械手力控制的需要.     

Design of a self-adaptive gripper with rigid fingers for Industrial Internet

Grippers are widely used in Industrial Internet for facilitating various operations such as logistics, materials handling, assembly, etc. Current grippers are specifically designed for a specific application so that it is difficult to adapt to a wide variety of shapes and sizes. Soft grippers have been developed to grasp objects with high surface complexity in Industrial Internet. Some challenges such as low controllability and long response time still exist. Rigid robot gripper shows good characters like robustness, accuracy and short response time. This paper thus presents a robot self-adaptive gripper using rigid fingers, where four rigid fingers are connected by springs that are tied to a screw rod. The screw rod is actuated by a rotary motor. Tip force of the fingers could be precisely controlled by the linear movement of the screw rod. The shapes and sizes of the object could be adaptively grasped due to the elasticity of the connecting springs. The proposed gripper is tested and verified to be highly flexible and controllable so that it could be suitable for most of the applications in production systems in the context of Industrial Internet.     

Intelligent robotic gripper with adaptive grasping force

The on-off control robot gripper is widely employed in pick-and-place operations in Cartesian space for handling hard objects between two positions. Without contact force monitoring, it can not be applied in fragile or soft objects handling. Although, an appropriate grasping force or gripper opening for each target could be searched by trial-and-error process, it needs expensive force/torque sensor or an accurate gripper position controller. It has too expensive and complex control strategy disadvantages for most of industrial applications. In addition, it can not overcome the target slip problem due to mass uncertainty and dynamic factor. Here, an intelligent gripper is designed with embedded distributed control structure for overcoming the uncertainty of object’s mass and soft/hard features. A communication signal is specified to integrate both robot arm and gripper control kernels for executing the robotic position control and gripper force control functions in sequence. An efficient model-free intelligent fuzzy sliding mode control strategy is employed to design the position and force controllers of gripper, respectively. Experimental results of pick-and-place soft and hard objects with grasping force auto-tuning and anti-slip control strategy are shown by pictures to verify the dynamic performance of this distributed control system. The position and force tracking errors are less than 1 mm and 0.1 N, respectively.

     

A hybrid-type micro-gripper with an integrated force sensor

A new hybrid-type micro-gripper that uses an integrated force sensor to control the gripping force was developed for handling micro-objects. The micro-gripper is composed of a piezoelectric multilayer bender for actuating the gripper fingers, silicon fingertips fabricated by use of silicon-based micromachining, and supplementary supports. The micro-gripper is referred to as a hybrid-type micro-gripper because it is composed of two main components: micro-fingertips fabricated using micromachining technology to integrate a very sensitive force sensor for measuring the gripping force, and piezoelectric gripper finger actuators that are capable of large gripping forces and moving strokes. A systematic design approach was applied to the design of each of components of the developed gripper, which made it possible to establish the functional requirements and design parameters of the micro-gripper. The micro-gripper was installed on a manual manipulator to assess its performance in tasks such as moving micro-objects from one position to a desired position. The gripping force signal was found to have a sensitivity of 667 N/V and several micro-objects were successfully moved (grasped and released) with the developed gripper. It was found during the testing experiments that the frictional forces between the working plane and the micro-object could be utilized to facilitate the release of micro-objects from the micro-gripper. We would like to thank Dr. W. K. Chung and Dr. S. S. Lee for their helpful discussions. We also express our appreciation to Mr. S. J. Kwon and S. C. Ko for their help in the experiments. This work is supported partly by the project Development of Core Technologies for Fabrication of Micro Tele-Manipulators from Ministry of Science and Technology, partly by the project Brain Korea 21 from Ministry of Education, and partly by Posco.     

基于CAN总线的微型灵巧手指力传感器研究

设计一种用于多指灵巧手的实用微型三维指力传感器.该传感器采用类双E型膜片的高度集成式弹性体结构;对该指力传感器的弹性体结构、测量电路、上下位机软件和CAN总线通讯协议进行了设计,并对传感器标定数据进行分析;实验结果表明,该传感器设计理论和设计过程正确,并且具有一定的实用价值,为具有感知功能的机器人灵巧手的研究奠定了基础.     

利用滑觉传感器实现机器人握力自适应控制

介绍了一个应用滑觉传感器组成的握力自适应控制系统,并给出了一种新型滑觉传感器的设计及结构,实验证明,它能够满足自适应控制的需要。     

Automated Gripper Jaw Design and Grasp Planning for Sets of 3D Objects

An algorithm for automatically generating a common jaw design and planning grasps for a given set of polyhedral objects is presented. The algorithm is suitable for a parallel‐jaw gripper equipped with three cylindrical fingers. The common jaw design eliminates the need for custom made grippers and tool changing. The proposed jaw configuration and planning approach reduces the search associated with locating the finger contacts from six degrees‐of‐freedom to one degree‐of‐freedom. Closed‐form algorithms for checking force closure and for predicting jamming are developed. Three quality metrics are introduced to improve the quality of the planned grasps. The first is a measure of the sensitivity of the grasp to errors between the actual and planned finger locations. The second is a measure of the efficiency of the grasp in terms of the contact forces. The third is a measure of the dependence of force closure on friction. These quality metrics are not restricted to cylindrical fingers and can be applied to n finger grasps. Running on a standard PC, the algorithm generated a solution in less than five minutes for a set of five objects with a total of 456 triangular facets. © 2003 Wiley Periodicals, Inc.     

基于DSP混合滤波夹持器力检测

针对夹持器系统存在的力检测问题,提出了一种基于DSP的混合滤波力信号检测处理方法;以多片半导体应变片作为敏感元件构成检测电桥,采用3阶有源切比雪夫硬件滤波,通过DSP进行采样检测,构成硬件信号采样处理电路,并在软件上采用限幅消抖滤波和低通巴特沃斯滤相结合的方法,对系统进行混合滤波处理;将所提出方法应用于实际系统,实验结果证实了该方法可以大大地提高夹持器力检测系统的抗干扰能力,验证了方法的有效性。     

Whole-finger manipulation with a two-fingered robot hand

This paper describes a method for whole-finger rolling manipulation using a two-fingered robot hand. 'Whole-finger' refers to the use of the complete phalangeal surface during the manipulation. An example of whole-finger manipulation by the human hand is the rolling of a pen between two fingers. The proposed method is based on a two-dimensional model for modelling an object manipulation and is derived from a study of the movement of the contact line between both fingers. Also, the method uses tactile sensor information to estimate the contact point position together with the local curvature of the object. This whole-finger dexterous manipulation is demonstrated on a prototype two-fingered hand. This 5 d.o.f. hand consists of a tendon driven index and thumb, and is equipped with force and tactile sensors. The dimensions and performance of this device are 'human-sized'. A hybrid force-position control scheme is used. The hierarchical control structure is implemented on a dual transputer system. This paper first describes the kinematic model used for whole-finger manipulation. In the second part, the main emphasis is put on the mechanical design and on the transputer-based control system.     

多感知机器人夹持器设计

介绍了一种具有力觉、触觉和位置信息的机器人夹持器设计。讨论了力觉传感器、触觉传感器和位置传感器的设计与实现，概述了夹持器的机械结构及其控制系统设计。     

用光学阵列传感器的机器人物体分类系统

本文介绍用光学阵列传感器的机器人物体分类系统。传感器直接安装在机器人的两个手指上。被抓物体的阴影通过光导纤维传到安放在“安全区”的光敏元件上。计算机识别物体的轮廓后命令机器人抓握物体,并把它运送到指定的地点从而达到物体分类的目的。     

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.     

A sensor for dynamic tactile information with applications in human–robot interaction and object exploration

We present a novel tactile sensor, which is applied for dextrous grasping with a simple robot gripper. The hardware novelty consists of an array of capacitive sensors, which couple to the object by means of little brushes of fibers. These sensor elements are very sensitive (with a threshold of about 5 mN) but robust enough not to be damaged during grasping. They yield two types of dynamical tactile information corresponding roughly to two types of tactile sensor in the human skin. The complete sensor consists of a foil-based static force sensor, which yields the total force and the center of the two-dimensional force distribution and is surrounded by an array of the dynamical sensor elements. One such sensor has been mounted on each of the two gripper jaws of our humanoid robot and equipped with the necessary read-out electronics and a CAN bus interface. We describe applications to guiding a robot arm on a desired trajectory with negligible force, reflective grip improvement, and tactile exploration of objects to create a shape representation and find stable grips, which are applied autonomously on the basis of visual recognition.