面向任务的三指手机器人抓取规划研究

本文给出了一种面向任务的三指手爪抓取规划的思路及研究方法，首先根据人手抓取姿态的分类，总结出典型的机器人抓取姿态，并以三指手爪来完成抓取，然后综合考虑任务要求、对象物体的几何物理特性及环境信息，经任务分析，推理出抓取姿态，并通过寻找特征平衡，确定出抓取平面，再在抓取现上进一步规划出３个抓取点，最终完成抓取规划过程。     

机器人多传感器信息融合研究综述

多传感器信息融合是一门新兴的技术,在机器人领域有着广阔的应用前景。综述并分析了多传感器信息融合技术在工业机器人、机器手爪、飞行机器人、移动机器人、爬行机器人和水下机器人中应用的研究现状,从机器人传感器的研制、融合算法、多传感器管理和信息融合仿生机理等方面总结了机器人多传感器信息融合的发展趋势。     

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.     

基于Visual C + +的水下机器人手爪作业过程仿真与实现

针对水下机器人手爪作业过程的仿真实现,提出了一种界面友好的虚拟动态展示方法.该方法在已有水下机器人手爪的机械模型基础上,通过环境设置和相对位置调节,模拟水下作业过程;为改善虚拟作业过程的交互性并降低设计难度,在Visual C 环境下用帧动画方法实现了动态过程的演示.该方法的思路和程序结构适用于类似应用场合.     

智能化水下机械手的研究

水下机械手的研究和设计对水下机器人的应用和发展具有重要的影响,而智能化水下机械手要具有自主路径规划和目标类型判断的功能.文中首先简要叙述了水下机械手的发展现状和存在问题,然后通过分析抓取任务及环境信息的获取,设计了系统的硬件结构和软件组成.接着通过对水下机械手进行数学建模,阐述了抓取操作的工作原理,并给出了一个抓取过程的实例曲线.     

电动力液压驱动四足双臂机器人的设计与实现

针对定基座机器人在复杂环境下作业能力不足的问题,研制出电动力液压四足双臂机器人,将浮动基座与双臂系统的优势有机结合,能够代替人员完成复杂环境下应急处置、工程作业等任务。详细阐述了四足双臂机器人的机械结构、机载电液动力系统、分布式控制系统以及仿真与操作训练平台的设计与实现。提出基于全身虚拟模型的足底力分配方法与足臂协调运动规划方法,实现了躯干浮动基座与双臂系统的联动,大大提升了机器人的作业能力和效率。通过搭建的仿真与操作训练平台完成单臂作业以及双臂协同作业的仿真,验证了所提出控制方法的有效性,并对机器人操作员进行操作训练。在实际样机实验中,测试了单臂抓取以及双臂协同抓取的能力,证明了四足双臂机器人能够满足复杂环境下移动作业的需求。     

多用途欠驱动手爪的自主抓取研究

对欠驱动手爪自主抓取进行了研究,将其分为自主决策和抓取控制两个过程．首先分析了欠驱动手爪的特点、主要的抓取模式,并借鉴人的抓取经验,采用模糊输入方法,综合考虑抓取任务要求和物体本身的特征属性,利用模糊神经网络良好的分类特性选择合适的抓取模式．在此基础上,完成手指姿势调整,采用基于传感器反馈的控制策略,在被抓物体上形成的合适的力分布以获得稳定抓取,并通过抓取实例验证了抓取决策和控制的正确性,提高了欠驱动手爪抓取的自动化水平．     

机器人手爪的研究现状与进展

机器人手爪既是一个主动感知工作环境信息的感知器,又是机器人末端的执行器,是一个高度集成的、具有多种感知功能和智能化的机电系统,涉及多个研究领域和交叉学科.本文综合分析了现有通用和专用机器人手爪的设计优缺点,以及手爪上应用的传感器和控制的研究现状,并总结提出了今后机器人手爪的研究重点,最后对未来的发展方向做出了展望.     

机器人机构学基础

1.前言机器人机构学在于研究机器人的构成、运动传递方法以及机器人关节运动与手爪抓取物体运动之间的关系。此外,还包括手爪机构和移动机器人的机构研究。下面以比较的方法就大家熟悉的机器人手臂做一简单的介绍。     

基于Kinect的服务机器人任务操控学习方法的研究

本文围绕中国科学院深圳先进技术研究院认知技术研究中心自行研发的搭载有Kinect传感器的服务机器人操控平台,从Kinect传感器带来的彩色图像、深度图像和真三维点云信息中提取基于图像的2D和基于点云的3D特征,并将它们进行融合,作为待识别物体几何模型归类的依据,为手爪选择合适抓取姿态提供判断准则。同时结合人体示范学习框架（Learning from demonstration,LFD）,研究了一种通过提高机器人的认知学习能力来完成人类生活环境中室内日常物品操控任务的方法,如：自行识别门把手的位置并完成开门动作,从橱柜中识别出目标物,抓取目标物体并送到指定目标地点等。最后,我们通过实验验证,该方法能够保证服务机器人成功抓取一些类似圆柱状、长方体等几何形态的物体并能在抓取之后顺利完成与周围环境进行交互过程中的轨迹规划这一复杂任务。     

灵巧手抓持规划的虚拟仿真方法

本文介绍在虚拟环境中,通过仿真分析的手段来研究机器人灵巧手抓持规划方案的方法。研究中以人的经验为指导,根据手、物的形状及尺寸等相对关系初步给出定性的抓持方案,以此为基础在虚拟环境中对机器人灵巧手的抓持过程进行仿真分析,判定所给出的抓持规划是否能实现在虚拟环境中的稳定抓持。然后在可行方案的基础上进一步对灵巧手的抓持点位置及抓持姿态进行优化,最终可得到机器人灵巧手对于特定被抓持物的较令人满意的抓持规划方案。     

Classification of grasps by robot hands

Grasping robotic hands is classified into three categories based on the object connectivity. We decompose the space of contact forces into four subspaces and develop a method to determine the dimensions of the subspaces with respect to the connectivity of the grasped object. The relationships we obtain reveal the kinematic and static characteristics of three categories of grasps. It indicates how contact forces can be decomposed corresponding to each type of grasp. The technique also provides a guideline for determining the distribution of contact forces on grasped objects. We analyze how power grasps are identified from the object connectivity and used to synthesize hand configurations for grasping and manipulation tasks. A physical interpretation of the subspaces and the determination of their dimensions are illustrated by examples.     

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．     

Extracting data from human manipulation of objects towards improving autonomous robotic grasping

Humans excel in manipulation tasks, a basic skill for our survival and a key feature in our manmade world of artefacts and devices. In this work, we study how humans manipulate simple daily objects, and construct a probabilistic representation model for the tasks and objects useful for autonomous grasping and manipulation by robotic hands. Human demonstrations of predefined object manipulation tasks are recorded from both the human hand and object points of view. The multimodal data acquisition system records human gaze, hand and fingers 6D pose, finger flexure, tactile forces distributed on the inside of the hand, colour images and stereo depth map, and also object 6D pose and object tactile forces using instrumented objects. From the acquired data, relevant features are detected concerning motion patterns, tactile forces and hand-object states. This will enable modelling a class of tasks from sets of repeated demonstrations of the same task, so that a generalised probabilistic representation is derived to be used for task planning in artificial systems. An object centred probabilistic volumetric model is proposed to fuse the multimodal data and map contact regions, gaze, and tactile forces during stable grasps. This model is refined by segmenting the volume into components approximated by superquadrics, and overlaying the contact points used taking into account the task context. Results show that the features extracted are sufficient to distinguish key patterns that characterise each stage of the manipulation tasks, ranging from simple object displacement, where the same grasp is employed during manipulation (homogeneous manipulation) to more complex interactions such as object reorientation, fine positioning, and sequential in-hand rotation (dexterous manipulation). The framework presented retains the relevant data from human demonstrations, concerning both the manipulation and object characteristics, to be used by future grasp planning in artificial systems performing autonomous grasping.     

Towards autonomous robotic servicing: Using an integrated hand-arm-eye system for manipulating unknown objects

Executing complex robotic tasks including dexterous grasping and manipulation requires a combination of dexterous robots, intelligent sensors and adequate object information processing. In this paper, vision has been integrated into a highly redundant robotic system consisting of a tiltable camera and a three-fingered dexterous gripper both mounted on a puma-type robot arm. In order to condense the image data of the robot working space acquired from the mobile camera, contour image processing is used for offline grasp and motion planning as well as for online supervision of manipulation tasks. The performance of the desired robot and object motions is controlled by a visual feedback system coordinating motions of hand, arm and eye according to the specific requirements of the respective situation. Experiences and results based on several experiments in the field of service robotics show the possibilities and limits of integrating vision and tactile sensors into a dexterous hand-arm-eye system being able to assist humans in industrial or servicing environments.     

人手到灵巧手的运动映射实现

本文研究主从操作中人手到灵巧手的运动映射．提出了一种基于虚拟关节和虚拟手指的关节空间运动映射方法，实现了人手和灵巧手的三维运动仿真．以数据手套为人机接口，在虚拟环境下，通过直观地比较映射效果，验证了映射算法．     

Grasp capability analysis of multifingered robot hands

This paper addresses the problem of grasp capability analysis of multifingered robot hands. The aim of the grasp capability analysis is to find the maximum external wrench that the multifingered robot hands can withstand, which is an important criterion in the evaluation of robotic systems. The study of grasp capability provides a basis for the task planning of force control of multifingered robot hands. For a given multifingered hand geometry, the grasp capability depends on the joint driving torque limits, grasp configuration, contact model and so on. A systematic method of the grasp capability analysis, which is in fact a constrained optimization algorithm, is presented. In this optimization, the optimality criterion is the maximum external wrench, and the constraints include the equality constraints and the inequality constraints. The equality constraints are for the grasp to balance the given external wrench, and the inequality constraints are to prevent the slippage of fingertips, the overload of joint actuators, the excessive forces over the physical limits of the object, etc. The advantages of this method are the ability to accomodate diverse areas such as multiple robot arms, intelligent fixtures and so on. The effectiveness of the proposed method is confirmed with a numerical example of a trifingered grasp.     

On the manipulability ellipsoids of underactuated robotic hands with compliance

Underactuation in robotic hands is currently attracting a lot of interest from researchers. The challenging idea of underactuation in grasping is that hands, with reduced number of actuators, supported by suitable design and control, may not suffer from reduced performances. This trend is also strengthened by recent neuroscience studies which demonstrates that also humans use sensorimotor synergies to control the hand in performing grasping tasks. In this paper, we focus on the kinematic and force manipulability analyses of underactuated robotic hands. The performances of such hands, regarded as mechanical transformers of inputs as forces and speed into outputs as object wrench and displacements, are assessed by suitably defined manipulation indices. The whole analysis is not limited by rigid-body motion assumptions, but encompasses elastic motions and statically indeterminate configurations by introducing generalized compliance at contacts and actuation. Two examples show the validity of the proposed approach to evaluate underactuated hand performances.     

Automatic grasp planning for multifingered robot hands

In this paper the development of a planning environment is described which was especially tailored for grasping and manipulating with multifinger robot hands. The research has been concerned with the programming and simulation system of the Karlsruhe dextrous hand, which has been in development for two years. The work presents the result of a geometric-mechanic approach to the object-handling problem with dextrous multifinger hands by selecting grasp points and searching grasp forces to perform desired assembly tasks. The knowledge representation for the sequence planning and command execution is based on object and task restrictions combined with routines for successive optimization and a constraint propagation algorithm.     

On the generation of a variety of grasps

In everyday life, people use a large diversity of hand configurations while reaching out to grasp an object. They tend to vary their hands position/orientation around the object and their fingers placement on its surface according to the object properties such as its weight, shape, friction coefficient and the task they need to accomplish. Taking into account these properties, we propose a method for generating such a variety of good grasps that can be used for the accomplishment of many different tasks. Grasp synthesis is formulated as a single constrained optimization problem, generating grasps that are feasible for the hand’s kinematics by minimizing the norm of the joint torque vector of the hand ensuring grasp stability. Given an object and a kinematic hand model, this method can easily be used to build a library of the corresponding object possible grasps. We show that the approach is adapted to different representations of the object surface and different hand kinematic models.