欧盟着力机器人手触觉传感技术研究

正由英国伯明翰大学科技人员领导的欧洲多学科NANOBIOTOUCH研发团队,正在开展机器人手触觉传感技术的神经与心理物理学基础研究,旨在通过从单个神经元提供实验数据的机器人学习算法到机器人手触摸物体表面的触觉反应,实现机器人手触觉传感技术的突破及机器人手指尖触觉的自我学习认知技能。     

三指机器人手的运动学研究

本文研究了指端与物体间为纯滚动接触时三指9关节机器人手操作物体的运动学问题,建立了手指关节运动与物体运动之间的位置关系、速度关系和加速度关系,给出了9个节间的运动约束条件。     

实用机器人制作讲座（二十四）：机器人的手部设计

前几期中设计的机器人的手臂上如果没有手则很不完善。在机器人领域里．手又被经常称为手钳(也叫操纵端)．因为这个词语能够比较接近的描述它的功能。用电机控制的机器人的手难以像人手一样很好地操纵物体，它们只是简单地抓住或夹住物体．所以称之为手钳。所以手和手钳常常混用。     

具有滚动接触的多指手操作运动学及其算法

研究多指手滚动操作的运动学及其算法.简要介绍了滚动接触运动方程,根据接触的 运动学约束,建立了描述物体与关节速度关系的关节--物体运动方程,并给出物体与手指表 面间相对角速度的表达式.得到的关节--物体运动方程、相对角速度表达式和接触运动方程 构成了形式简洁的滚动操作运动学方程.结合对方程的分析,进一步给出了多指手滚动操作物 体跟踪期望的运动轨迹时,关节运动轨迹的生成算法.     

机器人灵活手的运动分析和力分析

机器人灵活手可以稳定地抓持任意形状物体,或利用手指的运动操纵物体相对于机器人末杆(或手掌)的运动.它的运动学和力传递关系比一般开链机器人复杂得多.本文分析了在被抓持物体与手指指尖,手指指尖与手指关节之间力和虚位移的关系.利用线性变换的理论揭示了过约束、欠约束和奇异状态的形成条件.本文还分析了手指机构冗余自由度、亏缺自由度和奇异位形对抓持的影响.这些结果为机器人灵活手的设计和控制方案的规划提供了理论依据.     

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。     

灵巧手运动学分析

本文研究了灵巧手操作物体的运动学问题,在分析手指与物体接触条件的基础上,建立了手指与物体之间单点接触作纯滚动和作可控制滑动的运动学方程,给出了3R,4R 和5R 手指的运动约束条件,并对5-4-5型灵巧手进行了速度仿真和图形仿真.     

机器人灵巧手抓持分类器的设计与实现

机器人灵巧手的抓持分类是抓持规划的一个主要问题．本文应用模式识别技术设计和实现了 一种基于高斯混合模型GMM的分类器． 采用Expectation Maximization(EM)算法估计GMM的 参数，对人手的抓持动作进行识别与分类，经过人手到机器人手的关节空间运动映射，实现 了机器人灵巧手的抓持动作分类．该分类器可以应用于在线抓持规划．     

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.     

多指手协调操作中内力的计算

机器人多指手协调操作物体时,合理地确定手指对被操作物体的作用力是必要的.本文将手指尖与被操作物体之间接触模拟为具有摩擦的点接触,对由多指手与被操作物体组成的这样一个速度较低的系统作了静力分析,并对多指手操作物体时的操作力作了合理的分配,提出基于力矩最小的内力的最优计算方法,在计算中,充分考虑到手指只能推而不能拉物体这一实事.最后,以4个手指操作一个圆柱形物体为例.对操作过程作了图形仿真.     

Learning,Generation and Recognition of Motions by Reference-Point-Dependent Probabilistic Models

This paper presents a novel method for learning object manipulation such as rotating an object or placing one object on another. In this method, motions are learned using reference-point-dependent probabilistic models, which can be used for the generation and recognition of motions. The method estimates (i) the reference point, (ii) the intrinsic coordinate system type, which is the type of coordinate system intrinsic to a motion, and (iii) the probabilistic model parameters of the motion that is considered in the intrinsic coordinate system. Motion trajectories are modeled by a hidden Markov model (HMM), and an HMM-based method using static and dynamic features is used for trajectory generation. The method was evaluated in physical experiments in terms of motion generation and recognition. In the experiments, users demonstrated the manipulation of puppets and toys so that the motions could be learned. A recognition accuracy of 90% was obtained for a test set of motions performed by three subjects. Furthermore, the results showed that appropriate motions were generated even if the object placement was changed.     

Cyclic motion design and analysis for a passive object manipulation using an active plate

We propose a novel scheme for manipulating a passive object using an active plate. In previous studies, cyclic manipulations that transport objects on a plate, position control of an object on a plate, and juggling have been realized. In most manipulations using plates, object motions in the direction of the gravitational force are not considered. The objective of this study is to control an object’s orientation with respect to the gravitational force direction using an active plate for realizing hitherto unrealized object motion. In this context, a tumble doll, which is a planar rigid sphere, is defined as the object. Motions of the object and active plate are designed to be cyclic. A state vector composed of the object’s angle and angular velocity is defined, and the cyclic motion is expressed as a nonlinear discrete system. Fixed points of the state vector are searched for in the designed cyclic motion. A stability analysis around the fixed points is conducted using a Poincaré map. As a result, the fixed points are shown to be asymptotically stable. Finally, experimental results are used to verify that the object’s angle can be manipulated with the designed cyclic motion using the plate.     

Motion planning of skillful motions in assembly process through human demonstration

ABSTRACT

Skillful motions in the actual assembly process are challenging for the robot to generate with conventional motion planning approaches because some states during the human assembly can be too skillful to realize automatically due to the narrow passage. To deal with this problem, this paper develops a motion planning method using the human demonstration, which can be applied to complete skillful motions in the robotic assembly process. To demonstrate conveniently without redundant third-party devices, we attach augmented reality (AR) markers to the manipulated object to track and capture its poses during the human demonstration. To overcome the problem brought by the coarse resolution of the vision system, we extract the most important key poses from the demonstration data and employ them as clues to execute motion planning to suit the target precise task. As for the selection of key poses, two policies are compared, where the first and the second derivative of the main changing parameter of every key pose serve as criteria to determine the priority of utilizing key poses. Besides, a solution to deal with colliding key poses is also proposed. The effectiveness of the presented method is verified through some simulation examples and actual robot experiments.     

Coordination and Control of Multi-fingered Robot Hands with Rolling and Sliding Contacts

In this paper, the problem of controlling multi-fingered robot hands with rolling and sliding contacts is addressed. Several issues are explored. These issues involve the kinematic analysis and modeling, the dynamic analysis and control, and the coordination of a multi-fingered robot hand system. Based on a hand-object system in which the contacts are allowed to both roll and slide, a kinematic model is derived and analyzed. Also, the dynamic model of the hand-object system with relative motion contacts is studied. A control law is proposed to guarantee the asymptotic tracking of the object trajectory together with the desired rolling and/or sliding motions along the surface of the object. A planning approach is then introduced to minimize the contact forces so that the desired motion of the object and the relative motions between the fingers and the object can be achieved. Simulation results which support the theoretical development are presented.     

Motion segmentation and pose recognition with motion history gradients

This paper presents a fast and simple method using a timed motion history image (tMHI) for representing motion from the gradients in successively layered silhouettes. This representation can be used to (a) determine the current pose of the object and (b) segment and measure the motions induced by the object in a video scene. These segmented regions are not “motion blobs”, but instead are motion regions that are naturally connected to parts of the moving object. This method may be used as a very general gesture recognition “toolbox”. We demonstrate the approach with recognition of waving and overhead clapping motions to control a music synthesis program. Accepted: 13 August 2001     

Heavy material handling by cooperative robot control

Multifingered dextrous robot hands can perform more complex tasks than simpler end effectors. Teleoperation, in which a robot mimics the motion of a remote operator, provides a convenient method for controlling these robot hands. Damage to the object being manipulated by the robot hand may result in the case of open-loop control if there is no force-feedback sensation to the operator from the robot hand. The need arises for new actuator technology to provide force feedback to the hand master. These actuators have to meet the tight space and light weight requirements of a dextrous master. One candidate is the shape memory alloy (SMA) actuator. SMAs, such as Nitinol, are materials with a unique 'mechanical memory' and high force/weight ratio. A prototype SMA actuator and its hardware interface were designed and tested. Results showed that the actuator met the space and weight limitations of the master (Exos DHM?) and provided adequate reactive force feedback to the operator. However, the actuator had a low bandwidth of operation, due to relatively slow engagement and disengagement motions. This makes it unusable in real-time control situations.     

Robot motion command simplification and scaling

It has been observed that human limb motions are not very accurate, leading to the hypothesis that the human motor control system may have simplified motion commands at the expense of motion accuracy. Inspired by this hypothesis, we propose learning schemes that trade motion accuracy for motion command simplification. When the original complex motion commands capable of tracking motion accurately are reduced to simple forms, the simplified motion commands can then be stored and manipulated by using learning mechanisms with simple structures and scanty memory resources, and they can be executed quickly and smoothly. We also propose learning schemes that can perform motion command scaling, so that simplified motion commands can be provided for a number of similar motions of different movement distances and velocities without recalculating system dynamics. Simulations based on human motions are reported that demonstrate the effectiveness of the proposed learning schemes in implementing this accuracy-simplification tradeoff.     

Dynamic Manipulation Inspired by the Handling of a Pizza Peel

This paper discusses dynamic manipulation inspired by the handling mechanism of a pizza chef. The chef handles a tool called “pizza peel,” where a plate is attached at the tip of a bar, and he remotely manipulates a pizza on the plate. We found that he aggressively utilizes only two degrees of freedom (DOFs) from the remote handling location during manipulation: translation along the bar and rotation about the bar. From the viewpoint of a dynamic system, the inertial loads for these specific DOFs are never affected by the length of the bar. This is important for the production of quick plate motions so that the object on the plate can be dynamically and remotely manipulated. Applying this handling mechanism to a robot system, we first reveal how to make the object's motion for three DOFs by using two DOFs of plate motion. We then show that it is guaranteed to achieve an arbitrary desired set of position and orientation of the object by the proposed manipulation scheme. The proposed method has good manipulability because the translational motion of the object can be fully decoupled from the rotational motion (though not vice versa). Finally, we show a couple of experiments that confirm the basic idea.