多指灵巧手的位置/力矩控制

提出了一种新的多指手位置/力矩控制策略.该策略基于滑模位置控制和PD力矩控制分别实现自由空间中的轨迹跟踪和约束空间中的力矩跟踪,利用简单的系统观测器实现灵巧手在过渡过程中的控制模式切换.该策略具有很好的位置/力矩跟踪能力,并且可以保证过渡过程的稳定性.实验结果证明了该控制策略的有效性和可行性.     

基于指尖力传感器的HIT 机器人灵巧手笛卡尔阻抗控制

介绍了基于5维指尖力／力矩传感器的HIT机器人灵巧手的笛卡尔阻抗控制．当手指与障碍物接触时，呈现出2阶机械阻抗特性．在阻抗控制算法中，不需直接计算加速度，从而避免了因大加速度误差给控制带来的不利因素．结合期望轨迹和实际位置及速度，产生一个参考轨迹，手指跟踪此参考轨迹即可自动获得期望阻抗特性．     

虚拟环境中灵巧手主从抓持的实现

研究了虚拟现实环境中人手和灵巧手的抓持动作.利用数据手套采集人手的运动信息,将人手的运动 映射给灵巧手,通过搭建人手和灵巧手的模型,在虚拟环境下实现了主从抓持操作.探讨了关键技术问题:异构系统 运动映射、碰撞检测、虚拟力建模、稳定抓持的判据.􀁱     

Robust object manipulation for tactile-based blind grasping

Tactile-based blind grasping addresses realistic robotic grasping in which the hand only has access to proprioceptive and tactile sensors. The robotic hand has no prior knowledge of the object/grasp properties, such as object weight, inertia, and shape. There exists no manipulation controller that rigorously guarantees object manipulation in such a setting. Here, a robust control law is proposed for object manipulation in tactile-based blind grasping. The analysis ensures semi-global asymptotic and exponential stability in the presence of model uncertainties and external disturbances that are neglected in related work. Simulation and hardware results validate the effectiveness of the proposed approach.     

机器人多指灵巧手基关节力矩/位置控制系统的研究

HIT-1型机器人手是一种具有多种感知功能的仿人多指灵巧手．目前对多指手单关 节单自由度控制的研究较多,而对两自由度的手指基关节的研究却较少．本文分析了HIT-1 型手两 自由度基关节的运动学和动力学模型,并在此基础上建立了基关节的位置和力矩反馈控制系 统．本文采用工业中常用的PID控制算法分别实现了手指基关节在自由空间的位置控制和在 约束空间的力矩控制,并对实验结果进行了分析．     

Artificial neural network dexterous robotics hand optimal control methodology: grasping and manipulation forces optimization

Optimal fingertip forces can always be computed through the well-known optimization algorithms. However, computation time has always remained a real-time constraint. This article presents an efficient scheme to compute optimal grasping and manipulation forces for dexterous robotics hands. This is expressed as a quadratic optimization problem, and an artificial neural network (ANN) is used to learn such quadratic optimization formulations. Computation has been based on a nonlinear model of fingertip contacts and slips. In achieving object grasping while in motion, the hand Jacobian is considered an important matrix to be computed, but it is also highly intensive for real-time computed applications. Consequently, we investigated an efficient approach using artificial neural networks to learn optimal grasping forces. An ANN is used here to learn the optimal contact forces relating hand joint-space torques to the resulting object force. The results have indicated that the ANN has reduced computation times to reasonable values owing to its ability to map nonlinear force relations. Furthermore, the results have revealed that ANNs are capable of learning highly nonlinear relations relating to distributed fingertip forces and joint torques. The technique developed has also proved to be suitable for off-line learning of computed fingertip forces, even with large training samples.     

A differential-geometric approach for 2D and 3D object grasping and manipulation

This article presents an expository work on a differential-geometric treatment of fundamental problems of 2D and 3D object grasping and manipulation by a pair of robot fingers with multi-joints under holonomic or nonholonomic constraints. First, Lagrange’s equation of motion of a fingers-object system whose motion is confined to a vertical plane is derived under holonomic constraints when rolling contacts between finger-ends and object surfaces are permitted. Then, a class of control signals called “blind grasping” and constructed without knowing the object kinematics or using any external sensing like vision or tactile sensation is shown to realize stable object grasping in a dynamic sense. Stability of motion and its convergence to an equibrium manifold are treated on the basis of differential geometry of solution trajectories of the closed-loop dynamics on the constraint manifolds. Second, a mathematical model of 3D object grasping and manipulation by a pair of multi-joint robot fingers is derived under the assumption that spinning motion of rotation around the opposing axis between contact points does no more arise. It is shown that, differently from the 2D case, the instantaneous axis of rotation of the object is time-varying, which induces a nonholonomic constraint expressed as a linear differential equation of rotational motion of the pinched object. It is shown that there is a class of control signals constructed without knowing the object kinematics or using external sensings that can realize “blind grasping” in a dynamic sense. Finally, it is shown that the proposed differential geometric treatment of stability can naturally cope with redundancy resolution problems of surplus degrees-of-freedom (d.f.) of the overall fingers-object system, which is closely related to Bernstein’s d.f. problem.     

灵巧手操作的重构规划

根据人类利用滑动或滚动方式进行灵巧操作的几种模式 ,在多指灵巧手—物体的抓持系统中 ,把实现这些灵巧操作模式的规划问题作为一个位形空间的重构问题进行全局和局部级的操作规划 ,开发了实现灵巧操作的规划算法 .仿真结果表明了方法的有效性和正确性 .     

Two-arm manipulation tasks with friction-assisted grasping

—The main objective of this paper is to study human dual-arm manipulation tasks and to develop a computational model that predicts the trajectories and force distribution for the coordination of two arms moving an object between two given positions and orientations in a horizontal plane. Our ultimate goal is to understand the dynamics of human dual-arm coordination in order to develop better robot control algorithms. We propose a computational model based on the hypothesis proposed by Uno et al. that suggests that human movements minimize the integral of the norm of the rate of change of actuator torques. We compare the experimental trajectories and force distributions with those obtained from the computational model. The observed trajectories show a significant degree of repeatability across trials and across subjects. We show that the computational model predicts the trajectories and the distribution of forces (torques) for a certain class of trajectories. However, the trajectories in the sagittal and frontal plane are characterized by asymmetric features that are hard to model using any integral cost function. Finally, we show that the computational model can be used to generate smooth trajectories and actuator forces for cooperating robots and discuss the advantages of such an approach to motion planning.     

DexHand: dexterous hand manipulation motion synthesis for virtual reality

Virtual Reality - Natural object manipulation is one of the important human skills. However, generating natural hand manipulation motions that are adaptive to object shapes and the tasks at hand in...     

基于连杆力矩传感器动态补偿的机器人灵巧手笛卡儿阻抗控制

为了对连杆空间力矩传感器进行动态补偿,提出了适用于求取串联机器人任意连杆中任意一点处所受的内力和内力矩的算法.该算法采用连杆假想截断原理利用牛顿-欧拉方程推导而出.推导过程综合考虑了串联机器人是否处于静态以及末端是否受外力作用的情况,以及串联机器人的关节是否是回转关节的情况.然后利用该算法计算动态补偿值,构建了基于连杆力矩传感器动态补偿的笛卡儿阻抗控制器.最后在HIT/DLR Hand II五指灵巧手上进行了实验验证.实验结果一方面验证了该算法的有效性,另一方面也验证了本文所构建的笛卡儿阻抗控制器的有效性.     

Tactile data modeling and interpretation for stable grasping and manipulation

This paper is devoted to present the latest results on the exploitation of the force/tactile sensor developed by the authors in terms of modeling and interpretation of the data provided by the device. An analytical nonlinear model of the elastically deformable sensor is derived and validated, which allows to reconstruct the position and orientation of the surface in contact with a rigid object on the basis of the sensor signals. The reconstruction is performed via an Extended Kalman Filter able to counteract the measurement noise and to handle the nonlinearity of the model at the same time. The contact plane position and orientation information together with the contact force vector measured by the sensor are used to estimate the physical parameter most relevant to manipulation control purposes: the friction coefficient. A slippage control algorithm is presented which exploits the estimated friction and a novel slipping detection algorithm is proposed to cope with the unavoidable uncertainties of the real world and its effectiveness is experimentally proved in comparison with the existing techniques.     

Slippage control in soft finger grasping and manipulation

Handling objects with robotic soft fingers without considering the odds of slippage are not realistic. Grasping and manipulation algorithms have to be tested under such conditions for evaluating their robustness. In this paper, a dynamic analysis of rigid object manipulation with slippage control is studied using a two-link finger with soft hemispherical tip. Dependency on contact forces applied by a soft finger while grasping a rigid object is examined experimentally. A power-law model combined with a linear viscous damper is used to model the elastic behavior and damping effect of the soft tip, respectively. In order to obtain precise dynamic equations governing the system, two second-order differential equations with variable coefficients have been designed to describe the different possible states of the contact forces accordingly. A controller is designed based on the rigid fingertip model using the concept of feedback linearization for each phase of the system dynamics. Numerical simulations are used to evaluate the performance of the controller. The results reveal that the designed controller shows acceptable performance for both soft and rigid finger manipulation in reducing and canceling slippage. Furthermore, simulations indicate that the applied force in the soft finger manipulation is considerably less than the rigid “one.”.     

虚拟手抓持规则融合策略研究

群体虚拟手抓持规则是虚拟手和虚拟物体进行抓持操作的交互规则,用于判定虚拟手是否能够成功抓持物体。对基于几何的虚拟手抓持规则和基于物理的虚拟手抓持规则分别进行了研究,针对基于几何的虚拟手抓持规则规则简单、仿真效果较差,基于物理模型的虚拟手抓持规则计算复杂、难以实现实时仿真的问题:（1）改进基于几何的虚拟手抓持规则,通过接触点位置、法矢和抓持面法矢制定抓持规则,使其效果逼近力封闭虚拟手抓持规则;（2）利用力封闭计算中抓持接触点和法矢不变的特性,通过内力配比避免了抓持操作中的非线性规划求解,使抓持操作阶段实现实时仿真;（3）通过几何约束进行初始抓持判断-力封闭计算校正-内力配比力封闭计算的策略,实现了完整的抓持过程实时仿真。设计的交互实验说明该抓持规则能实现高沉浸感和实时性的抓持仿真,可以应用到虚拟训练、虚拟装配等仿真平台。     

基于深度神经网络的多指灵巧手抓取手势优化

基于深度神经网络模型,提出了一种适用于多指灵巧手的抓取手势优化方法。首先,在仿真环境下构建了一个抓取数据集,并在此基础上训练了一个卷积神经网络,依据目标物体单目视觉信息和多指灵巧手抓取位形来预测抓取质量函数,由此可以将多指灵巧手的抓取规划问题转化为使抓取质量最大化的优化问题,进一步,基于深度神经网络中的反向传播和梯度上升算法实现多指灵巧手抓取手势的迭代与优化。在仿真环境中,比较该网络和仿真平台对同一抓取位形的抓取质量评估结果,再利用所提出的优化方法对随机搜索到的初始手势进行优化,比较优化前后手势的力封闭指标。最后,在实际机器人平台上验证本文方法的优化效果,结果表明,本文方法对未知物体的抓取成功率在80%以上,对于失败的抓取,优化后成功的比例达到90%。     

From 3 fingers to 5 fingers dexterous hands

Abstract

This document presents a resume of several robotic hands from 1983 to 2016, its aim is to cover this rapidly evolving field of robotics and provide relevant information about the designs and current developments. The characteristics of the hands are enlisted and compared in terms of weight, grasping power/load capacity, degrees of actuation, degrees of freedom and finger configuration. Some prosthetic hands are presented to show their similarities with the robotic ones. A clear design tendency is seen while the gap between merely robotic hands and prosthetic limbs closing. The final parts of this document expose the trends in design of robotic hands, human inspired or not. The design tends to be compliant providing high number of degrees of freedom achieving high dexterity but decreasing the number of actuators, these characteristics can provide robust performance. Finally, we suggest the standardisation on the development process of the robotic hands as most of them are meant to be used in household robots and human–robot interaction.     

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.     

机器人手灵巧操作中变接触点的几何求解方法

灵巧手操作中的接触状态和接触点检测对应操作尤为重要,传统研究中,多采用分布式触觉压力传感器直接测量接触点和接触力,但其精度受触觉传感器单元分布密度影响较大,而将操作中接触点简化为固定接触点则会引入较大误差.本文分析了固定接触点模型的不足,分析了接触面轮廓曲线,以刚性接触为接触模型,从几何角度提出了一种不同位姿下灵巧手与被操作对象的变接触点的求解算法,实现不借用触觉传感器确定接触点,并通过MATLAB求解出一特定操作中接触点的变化规律.     

Shape optimization of screwdriver handle for improving grasping comfort of precision and power grips

Improving grasping comfort is a significant factor in enhancing the value of industrial products, as most products are handled by human hands. Our aim was to optimize hand tool shapes and maximize grasping comfort, considering multiple‐shape parameters and grasping types. A screwdriver handle was used as the reference tool for this case study. The measurements of handle length, end diameter, and middle diameter were utilized as the shape parameters of the handle. Twelve participants were included in this study. We measured the participants' subjective perceptions of comfort while grasping the precision and power grips during screw‐driving and screw‐tightening tasks, respectively. The design of the screwdriver handle was formulated as a bi‐objective optimization problem with respect to the grasping comfort of the precision and power grips. A Pareto frontier was determined by optimizing the formulated problem. Well‐balanced and optimal shapes for the precision and power grips were identified.