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。     

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.     

Detection and prevention of slip using sensors with different properties embedded in elastic artificial skin on the basis of previous experience

In dexterous robotic manipulation, it is essential to control the force exerted by the robot hands while grasping. This paper describes a method by which robot hands can be controlled on the basis of previous experience of slippage of objects held by the hand. We developed an anthropomorphic human scale robot hand equipped with an elastic skin in which two types of sensor are randomly embedded. One of these sensors is a piezoelectric polyvinylidenefluoride (PVDF) film which can be used for the detection of pressure changes. The other is a strain gauge which can measure static pressure. In our system, PVDF films are used to detect slipping, and strain gauges to measure stresses which are caused by normal and shear forces. The stress measured by the strain gauges is used as input data to a neural network which controls the actuators of the robot. Once slip is detected, the neural network is updated to prevent it. We show that this system can control the grasp force of the robot hand and adapt it to the weight of the object. By using this method, it was shown that robots can hold objects safely.     

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...     

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.     

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%。     

Telemanipulation of cooperative robots: a case of study

This article addresses the problem of dexterous robotic grasping by means of a telemanipulation system composed of a single master and two slave robot manipulators. The slave robots are analysed as a cooperative system where it is assumed that the robots can push but not pull the object. In order to achieve a stable rigid grasp, a centralised adaptive position-force control algorithm for the slave robots is proposed. On the other hand, a linear velocity observer for the master robot is developed to avoid numerical differentiation. A set of experiments with different human operators were carried out to show the good performance and capabilities of the proposed control-observer algorithm. In addition, the dynamic model and closed-loop dynamics of the telemanipulation is presented.     

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．     

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.     

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.     

Dextrous hands: human, prosthetic, and robotic

The sensory and motor capacities of the human hand are reviewed in the context of providing a set of performance characteristics against which prosthetic and dextrous robot hands can be evaluated. The sensors involved in processing tactile, thermal, and proprioceptive (force and movement) information are described, together with details on their spatial densities, sensitivity, and resolution. The wealth of data on the human hand's sensory capacities is not matched by an equivalent database on motor performance. Attempts at quantifying manual dexterity have met with formidable technological difficulties due to the conditions under which many highly trained manual skills are performed. Limitations in technology have affected not only the quantifying of human manual performance but also the development of prosthetic and robotic hands. Most prosthetic hands in use at present are simple grasping devices, and imparting a "natural" sense of touch to these hands remains a challenge. Several dextrous robot hands exist as research tools and even though some of these systems can outperform their human counterparts in the motor domain, they are still very limited as sensory processing systems. It is in this latter area that information from studies of human grasping and processing of object information may make the greatest contribution.     

Robust grasping strategy for assembling parts in various shapes

In a robotic cell, assembly robots have to grasp parts in various shapes robustly and accurately even under some uncertainties in the initial poses of the parts. For this purpose, it is necessary to develop a universal robotic hand and robust grasping strategies, i.e. finger motions that can achieve planned grasping robustly against the initial pose uncertainty of parts. In this paper, we propose a methodology to plan robust grasping strategies of a universal robotic hand for assembling parts in various shapes. In our approach, parts are aligned toward planned configurations during grasping actions, and the robustness of grasping strategies is analyzed and evaluated based on pushing operation analysis. As an application example, we plan robust grasping strategies for assembling a three-dimensional puzzle, and experimentally verify the robustness and effectiveness of the planned strategies for this assembly task.     

A virtual centrifugal force based navigation algorithm for explorative robotic tasks in unknown environments

In many robotic tasks, there is no a priori knowledge of the environment. This makes it necessary for robots to explore the environment. Navigation algorithms for robots to map the environment completely in a short time play a very important role in the robotic task completion. A navigation algorithm based on virtual centrifugal force is proposed to complete the robotic exploration of the unknown environment using rang sensors in this paper. Collisions between a robot and an obstacle or between robots can be avoided with the application of the proposed navigation rules. The kinematics and dynamics equations of robots adopting the algorithm are also given. The simulation experiments demonstrate the operation of the algorithm. Several simulation experiments of various representative robotic tasks are carried out, based on the explorative navigation algorithm, which successfully validate the virtual centrifugal force based navigation algorithm.     

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．     

Tactile object recognition in early phases of grasping using underactuated robotic hands

Intelligent Service Robotics - Identifying objects during the early phases of robotic grasping in unstructured environments is a crucial step toward successful dexterous robotic manipulation....     

Survey of robotic manipulation studies intending practical applications in real environments -object recognition,soft robot hand,and challenge program and benchmarking-

Aiming at accelerating the creation of new techniques on dexterous robotic manipulations, this paper surveyed the recent results on object recognition, soft robotic hands, and benchmarks and challenge/competition programs. The former two topics construct the elemental components of the new technologies on robotic manipulations, while the last one is a key for proceeding the creation and realization of the actual robotic manipulations with the new techniques. With these surveys, we will reveal the solved and unsolved issues for the next step to realize robotic dexterous manipulation systems comparable to human being.     

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.     

Linear dynamic system method for tactile object classification

Lots of tactile sequences can be obtained by using a dexterous hand for grasping different objects. The ability of robotic environmental perception and dexterous manipulation will be significantly improved after these tactile sequences are correctly classified. Therefore, tactile sequences are separated into series of subgroups, and a method based on linear dynamical system （LDS） is used to extract features. Since these LDSs lie in non- Euclidean space, the Martin distance, which is a measurement different from Euclidean distance, is applied to calculate the distance between two LDSs, and the K-Medoid algorithm is used for clustering. The codebook is obtained after clustering and is used to represent time sequences to get a Bag-of-System （BoS）. Then the BoS and labels are sent to Extreme Learning Machine （ELM） to train a classifier. Finally, three databases, KTH-7, KTH-10 and TSH-8 are used to evaluate our algorithm.     

Templates for pre-grasp sliding interactions

In manipulation tasks that require object acquisition, pre-grasp interaction such as sliding adjusts the object in the environment before grasping. This change in object placement can improve grasping success by making desired grasps reachable. However, the additional sliding action prior to grasping introduces more complexity to the motion planning process, since the hand pose relative to the object does not need to remain fixed during the pre-grasp interaction. Furthermore, anthropomorphic hands in humanoid robots have several degrees of freedom that could be utilized to improve the object interaction beyond a fixed grasp shape. We present a framework for synthesizing pre-grasp interactions for high-dimensional anthropomorphic manipulators. The motion planning is tractable because information from pre-grasp manipulation examples reduces the search space to promising hand poses and shapes. In particular, we show the value of organizing the example data according to object category templates. The template information focuses the search based on the object features, resulting in increased success of adapting a template pose and decreased planning time.