机器人多指手灵巧抓持规划

抓持规划是机器人灵巧手要完成预期任务所面临的一个重要问题．本文采用主从操作方式进行灵巧手的指尖抓持规划，由人手决定抓持接触点的位置， 灵巧手通过调整其手掌的位置和姿态保证各手指在人手指定的位置上抓持物体．根据灵巧手的操作特点，提出以关节灵活度来描述关节运动各向同性的能力，并据此定义灵巧手操作灵活度，作为灵巧手抓持位形性能的评价指标．以最大操作灵活度作为优化目标函数，寻求最优的抓持性能．同时，借鉴人手的抓持经验，通过主从操作方式，建立从人手到灵巧手的运动映射关系，从而为手掌位置优化问题提供合理的初值．仿真实验结果说明了文中方法的有效性．     

虚拟手的姿态控制与变形仿真

iHandRehab应用机器人技术与虚拟现实技术对手部损伤患者进行康复训练。主动模式下的训练任务需要在虚拟环境中模拟人手抓持和释放物体的真实过程,难点集中在虚拟手与刚性物体接触时的姿态控制和局部变形计算。根据人手的组织结构和运动特征将食指和拇指简化为3指节串联机构,通过改变各关节的旋转角度来实现虚拟手的自由运动。虚拟抓持过程中以手指的运动学模型为基础,结合前后指节需要满足的约束关系确定虚拟手的最终姿态。指节和物体分别采用点壳和距离场模型进行碰撞检测,根据点壳上碰撞点的嵌入信息计算虚拟手在接触区域的局部变形量。实验结果表明:模型能够实时准确地模拟人手抓持刚性物体时的真实状态。     

4DOF仿人手概念设计与抓持能力分析

本文提出了一种4自由度仿人手的概念设计及自由度分配方案,拟通过进行自由度的合理分配,提高少自由度仿人手的抓持能力,从而解决仿人手目前面临的少自由度和高抓持性能需求的矛盾.借鉴人手抓持分类方法,分别就精度抓持、力度抓持及人手常用抓持手势、手语进行抓持规划,并利用虚拟样机技术进行抓持仿真,表明该手能够完成抓圆柱、圆锥、球及拇指与其余手指对捏等不同类型的抓持任务.同时该手还可做出握手、OK等手势以及13种聋哑人字母语的手语,表明该手具有一定的用人类手势手语方式与人交流的能力.     

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.     

基于图像的机器人指尖跟踪过程仿真分析

对机器人指尖运动轨迹的视觉跟踪是智能机器人手势识别研究的关键.在多自由度机器人指尖跟踪过程中,由于自由度较高,指尖运动速度很快,指尖在运动终端的变化细微、灵活.利用传统的算法进行指尖图像跟踪时,需要连续、形状运动关联的特征才能完成跟踪,在较高自由度运动的情况下,指尖的运动终端图像特征的突变性因为无法以连贯的图像特征形式被捕获而被忽略,导致指尖跟踪图像与指尖终端的实际运动过程存在较大偏差,跟踪准确性下降.提出一种新的机器人指尖图像定位跟踪方法.针对采集到的指尖跟踪图像进行初始化处理,获取图像的高频部分和低频部分的向量,针对上述向量进行小波变换处理,实现指尖图像的去噪处理,提高了图像的质量.对指尖跟踪图像像素进行归一化处理,为指尖跟踪提供了准确的数据基础.利用线性分析的方法,对图像中的指尖运动轨迹区域进行准确分割,实现了指尖运动轨迹的准确跟踪.实验结果表明,利用改进算法进行指尖跟踪,能够有效提高跟踪的准确率.     

滚动接触时机器人手的操纵

多自由度多手指,具有高度灵活性的机器人手(Robot hand)的研究受到了越来越多的学者和专家的重视,机器人手与物体的接触形式有3类:1)固定点接触;2)滚动接触;3)滑动接触。本文研究滚动接触时机器人手的操纵机理,导出了手指尖运动之间的相容关系。利用本文结果,一方面可以从已知的手指尖运动求出物体(被操纵体)的运动;另一方面,可实现对机器人手的操纵规划,从而使机器人手对物体实现给定操作。     

共圆滑杆直线平夹自适应机器人手的研制

在平夹模式下,传统机器人手指末端的运动轨迹为圆弧,工作空间小,不适合抓取工作台上的薄板物体.为此,本文提出了一种共圆滑杆直线机构,分析了该机构的工作原理、运动特性和工作空间,并基于该直线机构设计了一种新型的直线平夹自适应机器人手.设计的机器人手包含2根手指,共4个自由度,仅采用2根电机驱动,结构简单.每个手指由基座、电机、簧件、L型连杆和2个指段等组成.该装置具备直线平夹和自适应包络两种抓取模式,捏持精度高,无需借助额外的传感和控制系统即可适应不同位置、姿态和形状的物体.针对设计的机器人手进行了不同抓取模式分析、运动分析和受力分析,研究了不同参数对抓取力的影响,为机器人手的设计和优化提供依据.并且研制了原理样机,开展了抓取实验,结果表明:机器人手的设计和分析合理,该装置可以实现直线平夹和自适应抓取功能,既能直线平夹物体,也能稳定包络抓取形状、大小各异的物体.     

抓持式对接机构的设计及分析

针对多机器人崎岖地形刚性对接问题,提出了“相互独立! 不完全约束! 完全约束”的顺次对接方 式,并设计了一种抓持式对接机构．该机构能够实现俯仰、偏摆两个自由度方向的姿态调整,并利用凸轮槽形控制 机械手的张合,具备抓持物体、多机器人刚性对接、分工协作等多种作业功能．对该机构进行了运动学分析,给出 了其运动学正解、逆解和工作空间模型．在此基础上研制JL-2 型自重构模块化移动机器人样机,并对抓持式对接机 构性能进行了实验验证．实验证明该机构具有较强的纠偏能力和较宽的抓持范围,可以完成多个机器人之间的相互 对接、协作等多种任务．     

基于物理的虚拟手抓持力觉生成和反馈

提出了一种基于物理的虚拟手静力抓持虚拟物体力觉生成和反馈方法,借鉴机械手抓持原理,在建立基于物理的虚拟手静力抓持通用力学模型并对其进行可解性分析的基础上,针对通用力学模型的多解性,提出了虚拟手最小力螺旋模型以生成力觉,并根据抓持物体的不同,进行模型实例化,实时求得各虚拟手指上的力和(或)力矩,实验结果表明,借助于本文的力觉生成和反馈方法,利用CyberGrasp力觉反馈数据手套,用户可在抓持虚拟物体时感受到真实的接触力。     

差动瓦特连杆直线平夹自适应抓持器

现有的平夹自适应抓持器不能实现末端直线轨迹运动,导致其存在抓取盲区。针对此问题,本文提出一种新型平夹自适应手指——Watt手指。该手指采用瓦特连杆机构实现末端指段的直线轨迹运动,结合平行四连杆机构保持末端指段在初始阶段的固定姿态,并利用三角形差动连杆机构实现自适应抓取。Watt手指在初始阶段具有直线平行夹持功能,适合捏持桌面上不同尺寸的物体;当第1或第2指段接触物体被阻挡时,该装置自动进入自适应抓取模式,可以适应不同形状、尺寸的物体。对该Watt手指进行了运动学分析,获得了该手指的稳定抓取空间和相对优化的参数集合。研制了二指Watt手,理论分析与实验结果表明,Watt手可实现良好的直线平夹与自适应抓取效果,直线轨迹使得该手具有更大的抓取空间,适应范围更广。     

On invariant configuration of a three-fingered grasp

When grasping an object using three-fingered end-effectors, a certain geometrical entity can be created based on the grasping points and a definition of an auxiliary point. This geometric configuration is referred to as the invariant configuration of the three-fingered grasp. This article exploits the geometric properties of such configuration using screw theory and inner product spaces. The results are shown to present themselves as an elegant and efficient framework in calculating the friction forces between the fingers and the grasped object and/or in defining the instantaneous motion of the finger-tips in accomplishing the desired motion of the grasped object.     

一种欠驱动水下机器人手爪的作业能力研究

在分析当前机器人手爪研究现状的基础上,根据水下作业任务特点,确定了作业型水下机器人多传感器手爪的设计需求,研究了水下手爪原理样机的作业能力,结合典型物体的抓取给出了关键参数关系.该样机可满足在指定环境下进行目标识别、抓取、简单装配、搬运等典型操作任务的要求,为操作型水下机器人多传感器手爪感知系统的研究提供了硬件平台.     

Automatic work objects calibration via a global–local camera system

In a human–robot collaborative manufacturing application where a work object can be placed in an arbitrary position, there is a need to calibrate the actual position of the work object. This paper presents an approach for automatic work-object calibration in flexible robotic systems. The approach consists of two modules: a global positioning module based on fixed cameras mounted around robotic workspace, and a local positioning module based on the camera mounted on the robot arm. The aim of the global positioning is to detect the work object in the working area and roughly estimate its position, whereas the local positioning is to define an object frame according to the 3D position and orientation of the work object with higher accuracy. For object detection and localization, coded visual markers are utilized. For each object, several markers are used to increase the robustness and accuracy of the localization and calibration procedure. This approach can be used in robotic welding or assembly applications.     

Exploring and modeling unimanual object manipulation on multi-touch displays

Touch-sensitive devices are becoming increasingly wide-spread, and consequently gestural interfaces have become familiar to the public. Despite the fact that many gestures require frequently dragging, pinching, spreading, and rotating the finger-tips, there currently does not exist a human performance model describing this interaction. In this paper, a novel user performance model is derived for virtual object manipulation on touch-sensitive displays, which involves simultaneous translation, rotation, and scaling of the object. Two controlled experiments with dual-finger unimanual manipulations were conducted to validate the new model. The results indicate that the model fits the experimental data well (with R² and R values above 0.9), and performs the best among several alternative models. Moreover, based on the analysis of the empirical data, the simultaneity nature of manipulation in the task is explored and several design implications are provided.     

Model-based and neural-network-based adaptive control of two robotic arms manipulating an object with relative motion

In the study of constrained multiple robot control, the relative motion between the constraint object and the end effectors of manipulators are usually neglected in the literature. However, in many industrial applications, such as assembly and machining, the constraint object is required to move with respect to not only the world coordinates but also the end effectors of the robotic arms. In this paper, dynamic modelling of two robotic arms manipulating an object with relative motion is presented first, then a model-based adaptive controller and a model-free neural network controller are developed. Both controllers guarantee the asymptotic tracking of the constraint object and the boundedness of the constraint force. Asymptotic convergence of the constraint force can also be achieved under certain conditions. Simulation studies are conducted to verify the effectiveness of the approaches.     

Design and control of a three-fingered tendon-driven robotic hand with active and passive tendons

This paper presents a design of a three-fingered robotic hand driven by active and passive tendons and proposes control methods for this hand. The tendon-driven robotic hand consists of the thumb, the index and the middle fingers. The robotic thumb can move all the joints independently. In contrast, the index and the middle robotic fingers are under-actuated using the combination of active and passive tendons, and move the terminal two joints synchronously, which is one of the important features of the human digits. We present passivity-based impedance and force controllers for tendon-driven robotic fingers and discuss how to combine them for fast and secure grasps. We experimentally validate that the robotic hand moves fast and manipulates an object and demonstrate that the robotic hand grasps objects in diverse ways.     

Soft object deformation monitoring and learning for model-based robotic hand manipulation

This paper discusses the design and implementation of a framework that automatically extracts and monitors the shape deformations of soft objects from a video sequence and maps them with force measurements with the goal of providing the necessary information to the controller of a robotic hand to ensure safe model-based deformable object manipulation. Measurements corresponding to the interaction force at the level of the fingertips and to the position of the fingertips of a three-finger robotic hand are associated with the contours of a deformed object tracked in a series of images using neural-network approaches. The resulting model captures the behavior of the object and is able to predict its behavior for previously unseen interactions without any assumption on the object's material. The availability of such models can contribute to the improvement of a robotic hand controller, therefore allowing more accurate and stable grasp while providing more elaborate manipulation capabilities for deformable objects. Experiments performed for different objects, made of various materials, reveal that the method accurately captures and predicts the object's shape deformation while the object is submitted to external forces applied by the robot fingers. The proposed method is also fast and insensitive to severe contour deformations, as well as to smooth changes in lighting, contrast, and background.     

Online urban object recognition in point clouds using consecutive point information for urban robotic missions

Urban object recognition is the ability to categorize ambient objects into several classes and it plays an important role in various urban robotic missions, such as surveillance, rescue, and SLAM. However, there were several difficulties when previous studies on urban object recognition in point clouds were adopted for robotic missions: offline-batch processing, deterministic results in classification, and necessity of many training examples. The aim of this paper is to propose an urban object recognition algorithm for urban robotic missions with useful properties: online processing, classification results with probabilistic outputs, and training with a few examples based on a generative model. To achieve this, the proposed algorithm utilizes the consecutive point information (CPI) of a 2D LIDAR sensor. This additional information was useful for designing an online algorithm consisting of segmentation and classification. Experimental results show that the proposed algorithm using CPI enhances the applicability of urban object recognition for various urban robotic missions.