弯扭耦合型软体机器人智能感知抓取性能研究

触觉感知功能对于生物或机器人进行目标物体的智能感知与抓取具有重要意义。首先，研究设计了一种新型弯扭耦合型软体机器人，并将压力传感器和弯曲传感器嵌入设计在软体机器人底层；其次，组合设计了能够感知抓取目标物体信息的二指软体夹爪；第三，搭建了二指软体夹爪抓取性能实验测试平台，系统分析了二指软体夹爪在抓取不同目标物体时的指尖触觉信息；最后，对生活中常见的12种物体进行了抓取性能测试，获得了不同压力下的压力序列信号并建立了抓取数据集，还采用多种机器学习算法对触觉数据集进行分类研究。结果表明：设计方案能够成功识别出目标物体的真实情况，其中随机森林分类算法具有最优的分类效果，准确率达到了93.33%。该研究有望为智能软体机器人的设计和智能抓取提供一定的理论和技术支持。     

基于多传感器的机器人遥操作人机交互系统

为了增强机器人人机交互系统的自然性,提出了基于多种传感器的非接触式人机交互系统设计方案,系统通过检测操作者手部动作和手部位置姿态的变化实现机器人的遥操作。研制了肌电传感器,获取手臂上一对拮抗肌上的表面肌电信号,并以此来判断机器人操作者的部分手部动作;利用Kinect体感设备和惯性测量单元获取手臂三维位置和姿态角信息。通过网络将人手的动作及位置姿态发送至机器人控制系统,以完成对机器人的控制。系统综合多种传感器的优点,极大减小了传统接触式交互方式对操作者运动范围的限制,实现了自然交互,实验表明了其有效性。     

面向灵巧操作的视觉目标识别

为了能够实现灵巧手对目标物体进行精准操作,研究了一种利用Kinect检测出目标物体,在帧差法的基础上对获取的深度进行背景相减,获取出目标物体的运动点,在此基础上利用获取的目标物体的特征采用T-S模糊逻辑判断出目标物体的方法,以BH8-280对目标物体进行抓取实验为例,在实验中,Kinect在帧差法的基础上检测出目标物体的位姿,大小,形状,以此为基础建立起T-S模糊逻辑系统,判断出目标物体的属性和类别,通过实验结果进一步说明了利用本文研究的方法显著地提高了判断物体的准确率和效率,为灵巧手的精细控制抓取奠定了基础。     

基于多传感器信息融合的人类抓握特征学习及物体识别

基于柔性可穿戴传感器及多模态信息融合,研究人类的抓握特征学习及抓取物体识别,探索人类在抓取行为中所依赖的感知信息的使用．利用10个可拉伸传感器、14个温度传感器及78个压力传感器构建了数据手套并穿戴于人手,分别测量人类在抓取行为中手指关节的弯曲角度、抓取物体的温度及压力分布信息,并在时间及空间序列上建立了跨模态信息表征,同时使用深度卷积神经网络对此多模态信息进行融合,构建人类抓握特征学习模型,实现抓取物体的精准识别．分别针对关节角度特征、温度特征及压力信息特征进行了融合实验及有效性分析,结果表明了基于多传感器的多模态信息融合能够实现18种物品的精准识别．     

基于双目视觉的人手定位与手势识别系统研究

提出了一种新的人手特征点提取方法,该方法将人手的质心作为匹配点,根据双目视觉定位数学模型计算目标位置信息,同时通过图像分割获取人手轮廓,利用轮廓凸包点特征来识别不同手势.在此基础上,研究设计了一种光学人手定位与手势识别系统,该系统在实时定位空间人手三维位置的同时,能够识别出相应的手势,可将其作为虚拟手的驱动接口,实现对虚拟物体的抓取、移动和释放操作.     

面向水下抓取作业的复合腔体仿生软体手设计

水下机械手在水生物采样、考古打捞等水下作业中起着关键作用,而现有水下机械手存在耦合安全性差、适应性弱及抓取不稳定等问题,使得作业效果并不理想。针对以上问题,本文开展了新型水下软体手的设计与研究。首先提出了具有密封管结构的复合腔体仿生软体驱动器,基于该驱动器设计出一种三指包络型水下软体手;硅胶材料的弹性和流体的可压缩性能够保证目标物的无损抓取,密封管结构可提高深水高压适应能力,保持腔和弯曲腔的复合结构及指纹、指甲的仿生结构提升了软体手耦合适应能力。利用基于Yeoh模型的有限元分析方法及注塑、3D打印等加工技术对软体手进行了结构优化和制作。设计了一套具有较高精度的水压驱动系统来提高软体手耦合稳定性。最后,针对影响机械手水下作业能力的重要因素,模拟相应实验场景对软体手进行了实验测试,实验结果显示软体手纵向抓取力达到26 N,驱动深度可达3000 m,较现有水下软体手均有较大提升;水下目标物抓取测试及与传统水下刚性手的对比实验证明了所提出软体手具有稳定的抓取性能和更好的适应性及安全性,适用于水下目标物的无损抓取作业。     

基于表面肌电信号的人机交互软件设计

设计了一种基于Qt的人机交互软件,用于从表面肌电信号中解码出手势,控制空间机械臂灵巧手作业;介绍了肌电信号解码手势并控制仿真灵巧手系统的组成,包括下位机肌电采集接口部分和上位机人机交互软件两部分;详细说明了人机交互软件的3个功能模块,即接收并显示16路神经接口向上位机发送的肌电信号、对肌电信号进行实时手势解码以及控制仿真灵巧手;分析了软件设计过程中的几个关键技术,信号与槽机制、多线程与多进程结合、UDP通信等;最后,设计了基于肌电信号解码3种手势并控制仿真灵巧手的实时实验,手势识别率在98％以上,控制延迟为200 ms左右;实验结果表明,人机交互软件运行稳定,功能齐全,在航天遥操作人机交互系统中具有应用前景.     

基于模糊逻辑控制的机器人灵巧手的抓取方式

机器人灵巧手抓取方式控制是整个灵巧手操作规划一个非常重要的环节。该文介绍了3种典型的抓取方式：平行抓取、聚中抓取和镊式抓取。以被抓取物体的尺寸为输入量，抓取方式作为输出量，提出了一种基于模糊逻辑的灵巧手抓取控制算法，并对这种算法进行了推导。在实际的机器人灵巧手遥操作系统中的应用表明，这种基于模糊控制的灵巧手抓取方式控制方法是正确有效的，具有使用价值。     

一种基于蜂巢气动网络的软体夹持器抓取策略研究

提出一种新的基于蜂巢气动网络的软体夹持器,并进行抓取策略的研究.基于软体机器人具有无限自由度的特性,可以实现夹持器和物体表面极好的贴合.结合蜂巢气动网络的运动特性和抓取特性,针对每个备选抓取点进行抓取过程模拟,以确定其最终抓取形态.对于每个备选抓取点的最终抓取形态,提取出判定点进行相对形封闭性判定以确定可行解集合.对于每个可行解方案,通过计算适用于蜂巢气动网络的软体夹持器的评价函数以获取最优值,得到对应的最佳抓取方案.实验结果表明,该软体夹持器及其抓取策略大幅提高了对常见几何形状物体的抓取成功率.     

基于仿人机械手的五指力封闭抓取算法

手是人类在长期进化过程中形成的最完美的工具,能够灵活、精细的进行抓取物体等操作。机械手设计初衷是取代人手完成工作,是机器人系统的重要组成部分,因此抓取物体等操作一直是仿人机械手的研究重点。传统的抓取方法是利用机械手三指形成力封闭完成任务。但由于机械手本身结构复杂等原因,易出现控制信号偏差或某自由度未达到要求水平,使得抓取过程中目标物体脱落等问题。为了使机械手达到稳定抓取的效果,本文提出了一种效仿人手抓取的五指力封闭抓取算法,其本质是利用冗余机制解决传统三指抓取过程中可能出现的抓取不平稳或脱落的问题。首先,基于三指力平衡算法的思想上提出了满足五指力封闭抓取算法的条件。然后,对五指力封闭抓取算法进行了充分性和必要性的证明。最后,通过仿真环境下的实验抓取不同目标物体,验证了五指力封闭算法的可行性及必要性。     

Design and analysis of a flexible robotic hand with soft fingers and a changeable palm

This study describes the design of a novel flexible robotic hand that can adapt its configurations to different grasping demands. Firstly, a mathematical model, based on the Yeoh strain energy function and virtual work principle, is established to investigate deformation properties of the designed soft finger. To achieve a flexible grasping capability, a changeable palm is presented with its variable configurations in terms of target objects with different sizes and shapes. A kinematic model of the flexible robotic hand is established, and then the numerical simulations based on the Monte-Carlo method and Matlab is applied to analyse the workspace of the hand and address the parameter optimisation problem of the rigid-flexible coupled system. Furthermore, an optimised grasping strategy on the basis of the principle of optimal efficiency is proposed to obtain an optimal grasping pose for the target object. Finally, a prototype is developed and tested in a laboratory to demonstrate the feasibility and effectiveness of our proposed hand. The results of practical experiments show that the robotic hand cannot only stably grasp objects with different sizes and shapes but also flexibly manipulate soft and fragile ones.     

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.

     

气动机器人多指灵巧手——ZJUT Hand

总结了现有灵巧手的缺点,例如结构复杂、难以控制等,并在此基础上提出了一种新型的气动驱动多指灵巧手,命名为ZJUT Hand.基于一种新型的气动柔性驱动器FPA,设计了气动刚柔性弯曲关节及侧摆关节;在此基础上给出了一种4自由度气动拟人手指;为了获得较高的模块化集成度,将5个完全相同的手指装配在拟人手掌上,构成具有5个手指、20个自由度的ZJUT Hand的本体结构;采用仿生学优化方法确定ZJUT Hand的结构参数,并对其本体结构进行了抓持仿真实验.仿真结果表明:ZJUT Hand能够对圆柱、长条形、球形等典型形状的物体实现抓持,并能够模拟人手实现对捏、夹持、勾拉等复杂拟人手形.详细设计了ZJUT Hand的力/位传感系统.完成了ZJUT Hand的抓取实验,结果表明:ZJUT Hand能够对典型形状目标物体实现稳定抓取.最后,简单总结了ZJUT Hand的特色之处.     

A human–robot interface for mobile manipulator

This paper presents a remote manipulation method for mobile manipulator through operator’s gesture. In particular, a track mobile robot is equipped with a 4-DOF robot arm to grasp objects. Operator uses one hand to control both the motion of mobile robot and the posture of robot arm via scheme of gesture polysemy method which is put forward in this paper. A sensor called leap motion (LM), which can obtain the position and posture data of hand, is employed in this system. Two filters were employed to estimate the position and posture of human hand so as to reduce the inherent noise of the sensor. Kalman filter was used to estimate the position, and particle filter was used to estimate the orientation. The advantage of the proposed method is that it is feasible to control a mobile manipulator through just one hand using a LM sensor. The effectiveness of the proposed human–robot interface was verified in laboratory with a series of experiments. And the results indicate that the proposed human–robot interface is able to track the movements of operator’s hand with high accuracy. It is found that the system can be employed by a non-professional operator for robot teleoperation.     

Detection and prevention of slip using sensors with different properties embedded in elastic artificial skin on the basis of previous experience

In dexterous robotic manipulation, it is essential to control the force exerted by the robot hands while grasping. This paper describes a method by which robot hands can be controlled on the basis of previous experience of slippage of objects held by the hand. We developed an anthropomorphic human scale robot hand equipped with an elastic skin in which two types of sensor are randomly embedded. One of these sensors is a piezoelectric polyvinylidenefluoride (PVDF) film which can be used for the detection of pressure changes. The other is a strain gauge which can measure static pressure. In our system, PVDF films are used to detect slipping, and strain gauges to measure stresses which are caused by normal and shear forces. The stress measured by the strain gauges is used as input data to a neural network which controls the actuators of the robot. Once slip is detected, the neural network is updated to prevent it. We show that this system can control the grasp force of the robot hand and adapt it to the weight of the object. By using this method, it was shown that robots can hold objects safely.     

多自由度仿生假手嵌入式控制系统及其抓取策略

为一种能够实现5 指独立动作以及具备人机交互能力的多自由度仿生假手设计了手部嵌入式控制系 统．该系统由传感器系统和运动控制系统构成,集成于假手机体内部,通过通信总线与上层控制器交换信息．传感 器系统包括3 种类型,共12 个传感器,可为假手自主抓取以及人机交互中的感觉反馈提供数据,运动控制系统用于 控制、驱动各手指动作．此外,本文以基于位置的阻抗控制为底层,以动作预构形为上层设计了分层控制策略．实 验表明,该嵌入式控制系统和分层控制策略使假手实现了自主抓取功能,提高了抓取的柔顺性、稳定性和适应性．     

A strategy for fast grasping of unknown objects using partial shape information from range sensors

In this paper, we present a strategy for fast grasping of unknown objects based on the partial shape information from range sensors for a mobile robot with a parallel-jaw gripper. The proposed method can realize fast grasping of an unknown object without needing complete information of the object or learning from grasping experience. Information regarding the shape of the object is acquired by a 2D range sensor installed on the robot at an inclined angle to the ground. Features for determining the maximal contact area are extracted directly from the partial shape information of the unknown object to determine the candidate grasping points. Note that since the shape and mass are unknown before grasping, a successful and stable grasp cannot be in fact guaranteed. Thus, after performing a grasping trial, the mobile robot uses the 2D range sensor to judge whether the object can be lifted. If a grasping trial fails, the mobile robot will quickly find other candidate grasping points for another trial until a successful and stable grasp is realized. The proposed approach has been tested in experiments, which found that a mobile robot with a parallel-jaw gripper can successfully grasp a wide variety of objects using the proposed algorithm. The results illustrate the validity of the proposed algorithm in term of the grasping time.     

Novel soft bending actuator-based power augmentation hand exoskeleton controlled by human intention

This article presents the development of a soft material power augmentation wearable robot using novel bending soft artificial muscles. This soft exoskeleton was developed as a human hand power augmentation system for healthy or partially hand disabled individuals. The proposed prototype serves healthy manual workers by decreasing the muscular effort needed for grasping objects. Furthermore, it is a power augmentation wearable robot for partially hand disabled or post-stroke patients, supporting and augmenting the fingers’ grasping force with minimum muscular effort in most everyday activities. This wearable robot can fit any adult hand size without the need for any mechanical system changes or calibration. Novel bending soft actuators are developed to actuate this power augmentation device. The performance of these actuators has been experimentally assessed. A geometrical kinematic analysis and mathematical output force model have been developed for the novel actuators. The performance of this mathematical model has been proven experimentally with promising results. The control system of this exoskeleton is created by hybridization between cascaded position and force closed-loop intelligent controllers. The cascaded position controller is designed for the bending actuators to follow the fingers in their bending movements. The force controller is developed to control the grasping force augmentation. The operation of the control system with the exoskeleton has been experimentally validated. EMG signals were monitored during the experiments to determine that the proposed exoskeleton system decreased the muscular efforts of the wearer.     

A New Methodology for Human Grasp Prediction

Grasping is an essential requirement for digital human models (DHMs). It is a complex process and thus a challenging problem for DHMs, involving a skeletal structure with many degrees-of-freedom (DOFs), cognition, and interaction between the human and objects in the environment. Furthermore, grasp planning involves not only finding the shape of the hand and the position and orientation of the wrist but also the posture of the upper body required for producing realistic grasping simulations. In this paper, a new methodology is developed for grasping prediction by combining a shape-matching method and an optimization-based posture prediction technique. We use shape matching to pick a hand shape from a database of stored grasps, then position the hand around the object. The posture prediction algorithm then calculates the optimal posture for the whole upper body necessary to execute the grasp. The proposed algorithm is tested on a variety of objects in a 3-D environment. The results are realistic and suggest that the new method is more suitable for grasp planning than conventional methods. This improved performance is particularly apparent when the nature of the grasped objects is not known a priori , and when a complex high-DOF hand model is necessary.