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.􀁱     

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.     

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.     

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 .     

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.     

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%。     

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.     

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.     

基于多约束条件的机器人抓取策略学习方法

针对机器人在多类别物体不同任务下的抓取决策问题,提出基于多约束条件的抓取策略学习方法.该方法以抓取对象特征和抓取任务属性为机器人抓取策略约束,通过映射人类抓取习惯规划抓取模式,并采用物体方向包围盒(OBB)建立机器人抓取规则,建立多约束条件的抓取模型.利用深度径向基(DRBF)网络模型结合减聚类算法(SCM)实现抓取策略的学习,两种算法的结合旨在提高学习鲁棒性与精确性.搭建以Refiex 1型灵巧手和AUBO六自由度机械臂组成的实验平台,对多类别物体进行抓取实验.实验结果表明,所提出方法使机器人有效学习到对多物体不同任务的最优抓取策略,具有良好的抓取决策能力.     

图像识别的机器手抓握滑移检测系统开发

为实现机器手抓握物体时不发生脱落,首先应检测其与被抓握物体接触面上的滑移信号.提出一种基于图像识别的机器手抓握滑移检测方法,采用中心区域匹配思想的归一化互相关算法(NCC)匹配由视觉传感器实时采集到的被抓握物体表面图像,得到被抓握物体在采集图像期间的滑移情况.实验结果表明:此系统可以准确检测被抓握物体是否发生滑移及滑移的方向和大小,具有高准确度、高灵敏度等优点.     

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.     

Analysis of the musculoskeletal system of the hand and forearm during a cylinder grasping task

Musculoskeletal disorders of the hand are mostly due to repeated or awkward manual tasks in the work environment and are considered a public health issue. To prevent their development, it is necessary to understand and investigate the biomechanical behavior of the musculoskeletal system during the movement. In this study a biomechanical analysis of the upper extremity during a cylinder grasping task is conducted by using a parameterized musculoskeletal model of the hand and forearm. The proposed model is composed of 21 segments, 28 musculotendon units, and 20 joints providing 24 degrees of freedom. Boundary conditions of the model are defined by the three-dimensional coordinates of 43 external markers fixed to bony landmarks of the hand and forearm and tracked with an optoelectronic motion capture system. External marker positions from five healthy participants were used to test the model. A task consisting of closing and opening fingers around a cylinder 25 mm in diameter was investigated. Based on experimental kinematic data, an inverse dynamics process was performed to calculate output data of the model (joint angles, musculotendon unit shortening and lengthening patterns). Finally, based on an optimization procedure, joint loads and musculotendon forces were computed in a forward dynamics simulation. Results of this study assessed reproducibility and consistency of the biomechanical behavior of the musculoskeletal hand system.     

Control of twisting manipulation using a multi-fingered robotic hand with a common rotational axis

The main purpose of the present study is to prove the usability of a mechanism with a common rotational axis during twisting manipulation using a multi-fingered robotic hand where two fingers and two other fingers can independently rotate in inner and outer circles with a dual turning mechanism. Although various types of conventional multi-fingered hands have potential capability to achieve twisting manipulations such as opening a bottle cap from within a hand, it is well-known that such tasks are difficult to execute quickly due to limited working space of the fingers and complexity of control. The proposed hand with a common rotational axis is effective in rotational manipulation around a particular axis, where each joint role assignment is completely decoupled into internal force control for grasping an object and velocity control around the axis for rotating the object. We prove the usability of this mechanism with a common rotational axis through the use of a control scheme, and show experimental results involving manipulation tasks where twisting manipulation is dominant.

     

含弧形自由度仿人灵巧手掌的机构设计

提出了一种人手掌的简化模型,根据该模型,设计了仿人灵巧手掌的机构,使得只作为仿人灵巧手手指及其控制部件机架的手掌形成弧形自由度参加手部动作,提高了仿人灵巧手通用性、灵活性和抓持物体的适应性。经优化设计和仿真,机构完全符合灵巧手进行运动时手掌的运动要求。     

Neurofuzzy Inverse Jacobian Mapping for Multi-Finger Robot Hand Control

Fuzzy systems and models are useful for describing processes where the underlying physical mechanisms are not completely known and where a system behavior is understood in qualitative terms. Neurofuzzy systems have been employed in large number of intelligent based control systems and robotics, that is due to the ability to deal with large number of inputs and with the ability to learn and remember specific learned patterns. This paper investigates the employment of a neurofuzzy system for a multi-finger robot hand control and manipulation tasks. The approach followed here is to let a defined neurofuzzy system to learn the nonlinear functional relation that maps the entire hand joint positions and displacements to object displacement. This is done by avoiding the use of the Inverse Hand Jacobian, while observing the interaction between hand fingers and the object being grasped and manipulated. The developed neurofuzzy system approach has been trained for several object training patterns and hand postures within a cartesian based palm dimension. The paper demonstrates the proposed algorithm for a four fingered robot hand motion, where inverse hand Jacobian plays an important role in the hand dynamics and control.     

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.     

基于MSP430的肌电假手系统设计

在对人体表面肌电信号研究的基础上,设计出一种肌电假手系统,其中包括肌电信号采集调理系统和假手控制系统。肌电信号经信号调理电路放大、滤波、陷波后,由低功耗的MSP430F149单片机进行A/D转换、特征计算。单片机结合肌电信号与触滑觉传感器反馈的信息来控制电机转向与转速,从而控制假手做出相应动作。通过实际采集的肌电信号在示波器上显示的波形与假手的动作进行对比,说明系统设计是合理有效的。