多指灵巧手接触力控制研究

多指灵巧手与环境的接触力控制是其抓持物体并进行作业的基础，也是一个非常复杂的问题，本文用基于模糊控制的方法对多指灵巧手的接触力进行控制，并对非性性进行了补偿，实验结果表明所用方法是有效的。     

基于模糊逻辑控制的机器人灵巧手的抓取方式

机器人灵巧手抓取方式控制是整个灵巧手操作规划一个非常重要的环节。该文介绍了3种典型的抓取方式：平行抓取、聚中抓取和镊式抓取。以被抓取物体的尺寸为输入量，抓取方式作为输出量，提出了一种基于模糊逻辑的灵巧手抓取控制算法，并对这种算法进行了推导。在实际的机器人灵巧手遥操作系统中的应用表明，这种基于模糊控制的灵巧手抓取方式控制方法是正确有效的，具有使用价值。     

复杂环境下基于推抓协同操作的目标物体抓取

针对现有抓取技术在复杂环境下难以进行有效的目标导向性抓取的问题, 本文提出了一种基于深度强化学习的推动和抓取协同操作的方法. 相对于以往的抓取方法, 本方法使用深度学习来处理Intel-D435i相机所获得的RGB-D图像数据, 同时又在视觉网络中引入了注意力机制, 用来提高系统对工作区域内目标物体的敏感性. 其次,使用深度Q网络来学习UR5机械臂与环境之间的交互过程, 提出了密集奖励策略来评判推动或抓取操作的好坏. 随着训练次数的不断增加, UR5机械臂在训练过程中不断地优化两种操作之间的协同策略, 从而更高效的进行决策.最后, 在V-rep仿真平台上设计了仿真场景, 并进行测试, 平均抓取成功率达到92.5%. 通过与其他几种方法进行对比, 证明该方法可以在复杂环境下较好的完成目标物体的抓取任务.     

气动机器人多指灵巧手——ZJUT Hand

总结了现有灵巧手的缺点,例如结构复杂、难以控制等,并在此基础上提出了一种新型的气动驱动多指灵巧手,命名为ZJUT Hand.基于一种新型的气动柔性驱动器FPA,设计了气动刚柔性弯曲关节及侧摆关节;在此基础上给出了一种4自由度气动拟人手指;为了获得较高的模块化集成度,将5个完全相同的手指装配在拟人手掌上,构成具有5个手指、20个自由度的ZJUT Hand的本体结构;采用仿生学优化方法确定ZJUT Hand的结构参数,并对其本体结构进行了抓持仿真实验.仿真结果表明:ZJUT Hand能够对圆柱、长条形、球形等典型形状的物体实现抓持,并能够模拟人手实现对捏、夹持、勾拉等复杂拟人手形.详细设计了ZJUT Hand的力/位传感系统.完成了ZJUT Hand的抓取实验,结果表明:ZJUT Hand能够对典型形状目标物体实现稳定抓取.最后,简单总结了ZJUT Hand的特色之处.     

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.     

一种采用迭代优化的移动抓取规划方法

提出了一种无需对目标物体进行预建模的迭代优化移动抓取规划方法.该方法通过点云相机在线对目标物体进行立体模型测量和建模,通过深度卷积神经网络对目标点云生成的多个候选抓取位置的抓取成功率进行评价.然后,对机器人底盘和手爪的位置和姿态进行迭代优化,直到抓取目标物体时机器人达到一个最优的位形.再用A*算法规划一条从机器人当前位置到目标位置的运动路径.最后,在路径的基础上,用一种启发式随机路径逼近算法规划手臂的运动,实现边走边抓的效果.本文的深度学习抓取成功率评估算法在康奈尔数据集上取得了83.3%的精确度.所提运动规划算法能得到更平滑、更短且更有利于后续运动的路径.     

基于未知物体三维点云特征的机器人六自由度抓取

针对非结构化环境中任意位姿的未知物体,提出了一种基于点云特征的机器人六自由度抓取位姿检测方法,以解决直接从点云中获取目标抓取位姿的难题.首先,根据点云的基本几何信息生成抓取候选,并通过力平衡等方法优化这些候选;然后,利用可直接处理点云的卷积神经网络ConvPoint评估样本,得分最高的抓取将被执行,其中抓取位姿采样和评估网络都是以原始点云作为输入;最后,利用仿真和实际抓取实验进行测试.结果表明,该方法在常用对象上实现了88.33%的抓取成功率,并可以有效地拓展到抓取其他形状的未知物体.     

臂-手系统主动抓取策略及仿真

本文将多指手和关节型机械手臂结合起来，应用多种传感器，组成了仿真人臂－手系统，对多种抓取模式进行了分析，提出了主动抓取目标物体的策略，即将孩童抓取和知识抓取结合起来，由推理机获得抓取模式及算法，最后，对抓取过程进行了图形仿真。     

基于WTK的数据手套交互控制模块设计与实现

依据虚拟现实中数据手套操纵的虚拟手与虚拟场景的交互控制过程，在VC 环境下，设计了一个基于WTK的数据手套交互控制模块，重点研究其中的虚拟手手势合成技术和交互操作中的碰撞检测技术。提出用平行节点数据结构组织虚拟手模型，用空间图形变换法实现手势合成；结合包围盒和多边形碰撞检测法，研究了虚拟手与虚拟场景的碰撞检测。应用结果显示，虚拟手可动态地呈现与人手基本一致的手势，能够比较逼真地实现了虚拟手抓取物体、释放物体等交互操作。     

一种基于深度学习的机械臂抓取方法

提出了一种基于深度神经网络的机械臂最优抓取位置检测方法.相比传统手工设定的特征,基于深度神经网络的方法学习得到的特征具有较强的鲁棒性和稳定性,能够适应训练集中未曾出现的新物体.本方法首先使用基于深度学习的目标检测算法对图像中的目标物体进行检测,记录目标的类别和位置.然后根据分类检测结果,使用基于深度学习的机械臂抓取方法进行抓取位置学习.仿真实验表明所提方法能对图像中的目标物体进行较为准确的分类,在Universal Robot 5机械臂上得到的抓取实验结果证明了所提方法的有效性.     

Multi-fingered robot hand optimal task force distribution: Neural inverse kinematics approach

Grasping and manipulation force distribution optimization of multi-fingered robotic hands can be formulated as a problem for minimizing an objective function subject to form-closure constraints, kinematics, and balance constraints of external force. In this paper we present a novel neural network for dexterous hand-grasping inverse kinematics mapping used in force optimization. The proposed optimization is shown to be globally convergent to the optimal grasping force. The approach followed here is to let an artificial neural network (ANN) learn the nonlinear inverse kinematics functional relating the hand joint positions and displacements to object displacement. This is done by considering the inverse hand Jacobian, in addition to the interaction between hand fingers and the object. The proposed neural-network approach has the advantages that the complexity for implementation is reduced, and the solution accuracy is increased, by avoiding the linearization of quadratic friction constraints. Simulation results show that the proposed neural network can achieve optimal grasping force.     

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．     

Intelligent control of multi-fingered hands

This paper is concerned with intelligent control for grasping and manipulation of an object by multi-fingered robot hands with rigid or soft hemispheric finger ends that induce rolling contacts with the object. Even in the case of 2D motion like pinching by means of a pair of multi-degrees of freedom robot fingers, there arises an interesting family of Lagrange’s equations of motion with many geometric constraints, which are under-actuated, redundant, and non-holonomic in some sense. Regardless of underactuation of dynamics, it is possible to find a class of sensory feedback signals that realize secure grasp of an object together with control of object orientation. In regard to the secure grasping, a problem of force/torque closure for 2D objects in a dynamic sense plays a crucial role. It is shown that proposed sensory feedback signals satisfying the dynamic force/torque closure can be constructed without knowing object kinematic parameters and location of the mass center. To prove the convergence of motion of the overall fingers–object system under the circumstance of redundancy of joints, new concepts called “stability on a manifold” and “asymptotic stability on a manifold” are introduced. Based on the results found for intelligent control of robotic hands, the last two sections attempt to discuss why human multi-fingered hands can become so dexterous at grasping and object manipulation.     

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。     

Stable grasp planning based on minimum force for dexterous hands

Intelligent Service Robotics - The paper investigates a grasp planning method for dexterous hands grasping different objects. It aims at planning the robotic hands’ grasping position and...     

水下多指手研究现状

通过对国内外水下多指手研究进展的充分调研和总结,对水下多指手的现状和结构进行了综述,对影响水下多指手发展的密封与防腐技术、力感知技术、抓取最优规划技术及抓取力控制技术等关键技术进行了总结和分析,并展望其未来的发展趋势．     

A human-like real-time grasp synthesis method for humanoid robot hands

This paper addresses a real-time grasp synthesis of multi-fingered robot hands to find grasp configurations which satisfy the force closure condition of arbitrary shaped objects. We propose a fast and efficient grasp synthesis algorithm for planar polygonal objects, which yields the contact locations on a given polygonal object to obtain a force closure grasp by a multi-fingered robot hand. For an optimum grasp and real-time computation, we develop the preference and the hibernation process and assign the physical constraints of a humanoid hand to the motion of each finger. The preferences consist of each sublayer reflecting the primitive preference similar to the conditional behaviors of humans for given objectives and their arrangements are adjusted by the heuristics of human grasping. The proposed method reduces the computational time significantly at the sacrifice of global optimality, and enables grasp posture to be changeable within 2-finger and 3-finger grasp. The performance of the presented algorithm is evaluated via simulation studies to obtain the force-closure grasps of polygonal objects with fingertip grasps. The architecture suggested is verified through experimental implementation to our developed robot hand system by solving 2- or 3-finger grasp synthesis.     

Flexible robotic grasping strategy with constrained region in environment

Grasping is a significant yet challenging task for the robots. In this paper, the grasping problem for a class of dexterous robotic hands is investigated based on the novel concept of constrained region in environment, which is inspired by the grasping operations of the human beings. More precisely, constrained region in environment is formed by the environment, which integrates a bio-inspired co-sensing framework. By utilizing the concept of constrained region in environment, the grasping by robots can be effectively accomplished with relatively low-precision sensors. For the grasping of dexterous robotic hands, the attractive region in environment is first established by model primitives in the configuration space to generate offline grasping planning. Then, online dynamic adjustment is implemented by integrating the visual sensory and force sensory information, such that the uncertainty can be further eliminated and certain compliance can be obtained. In the end, an experimental example of BarrettHand is provided to show the effectiveness of our proposed grasping strategy based on constrained region in environment.     

基于分散神经网络的多指灵巧手位置控制

针对多指灵巧手钢缆传动系统的非线性，提出一种基于分散神经网络的位置控制方法．通过 对复杂的钢缆传动系统施加不同的输入可以得到特定的相对简单的输入输出数据，利用这种 特定的输入输出数据学习传动系统的非线性关系得到多个分散的神经网络，再根据传动系统 的结构特性用分散的神经网络求取钢缆传动系统的逆模型，用于直接逆控制，从而达到补偿 非线性误差的目的．同时应用在线神经网络的适时补偿使系统长时间保持良好的运行状态． 实验证明这种方法可大大提高位置跟踪精度，取得比较满意的结果．