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。     

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.     

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．     

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%。     

Task-based compliance planning for multi-fingered robotic manipulations

Based on the analysis of the stiffness relation between the operational space and the fingertip space of multi-fingered hands, this paper provides a guideline of task-based compliance planning for multi-fingered robotic manipulations. In order to show the characteristics of the taskbased stiffness matrix, various two- and three-dimensional examples are illustrated. Also, it is shown that some of the coupling stiffness elements cannot be planned arbitrarily due to grasping geometry. Through the analytical results, it is concluded that the operational stiffness matrix should be carefully specified by considering the location of the compliance center and the grasp geometry of multifingered hands for successful grasping and manipulation tasks.     

Automatic grasp planning for multifingered robot hands

In this paper the development of a planning environment is described which was especially tailored for grasping and manipulating with multifinger robot hands. The research has been concerned with the programming and simulation system of the Karlsruhe dextrous hand, which has been in development for two years. The work presents the result of a geometric-mechanic approach to the object-handling problem with dextrous multifinger hands by selecting grasp points and searching grasp forces to perform desired assembly tasks. The knowledge representation for the sequence planning and command execution is based on object and task restrictions combined with routines for successive optimization and a constraint propagation 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.     

Dynamic grasp planning of multifingered robot hands based onasymptotic stability

We describe an approach for planning grasps of multifingered robot hands based on a small vibration model. Using features of the grasp configuration, we analyze asymptotic stability, contact situations, and uniaxial fingertip force constraints for the combined planning of finger posture and finger position, and characterize the generalized mass, damping, and stiffness. Choosing the largest time constant of the vibration model as an optimization criterion for planning finger postures and positions, the original problem of dynamic grasp planning is formulated as a nonlinear program. Simulation examples for a three-fingered robot hand grasping a spherical object demonstrate the effectiveness of the approach.     

Force-closed grasping with two hands

Two-handed grasping of rigid objects in two-dimensional space is studied. The hands considered in this article are either flat-surface palms or grippers with two angular-motion fingers. Presented in this article is a condition that establishes the existence of force-closed grasping without the knowledge of the shape of the grasped object and of the exact contact locations on the palms or fingers. Further, an algorithm is developed that determines force-closed grasping based on the position and orientation of the two hands.     

基于多感知的空间机械手爪控制研究

对融合了视觉、滑觉、角位移等多种传感器的欠驱动空间机械手爪,研究其对不同形状、质地的物体实现自适应抓取控制。通过传感器反馈控制机械手运动、抓取力,提高机械手的自主能力。在抓取模式选择中,采用基于专家系统的抓取规划,根据物体不同的形状、尺寸选择不同的抓取模式;在抓取力控制中,通过由PVDF制作的滑觉传感器反馈,采用基于滑觉信号的模糊控制方法,对不同质地的物体选择不同的控制参数。通过实验研究验证基于多感知的控制方法对各种物体可以进行可靠的抓取。     

Kinematic analysis and planning for form closure grasps by robotic hands

A new approach to the planning of form closure grasps by robotic hands is presented. The form closure grasp is to constrain a rigid workpiece by surrounding the object surface with mechanical fingers so that the object motion is geometrically constrained in all directions. First, kinematic conditions for the form closure grasp are obtained for workpieces with smooth surfaces, and are extended those one comprising edges and vertices. An efficient algorithm for examining the form closure grasp conditions is then developed by applying linear programming techniques. A computer-assisted planning system is also developed for the synthesis of grasping points as well as for the design of grippers. Fingertip locations are determined so as to accomplish the form closure grasp, given the geometry of a workpiece. A couple of examples demonstrate the usefulness of the method.     

On the manipulability ellipsoids of underactuated robotic hands with compliance

Underactuation in robotic hands is currently attracting a lot of interest from researchers. The challenging idea of underactuation in grasping is that hands, with reduced number of actuators, supported by suitable design and control, may not suffer from reduced performances. This trend is also strengthened by recent neuroscience studies which demonstrates that also humans use sensorimotor synergies to control the hand in performing grasping tasks. In this paper, we focus on the kinematic and force manipulability analyses of underactuated robotic hands. The performances of such hands, regarded as mechanical transformers of inputs as forces and speed into outputs as object wrench and displacements, are assessed by suitably defined manipulation indices. The whole analysis is not limited by rigid-body motion assumptions, but encompasses elastic motions and statically indeterminate configurations by introducing generalized compliance at contacts and actuation. Two examples show the validity of the proposed approach to evaluate underactuated hand performances.     

Current research in robotics and automation-an intelligent graspingsystem

A research project is described that focuses on building a comprehensive grasping environment capable of performing tasks such as locating moving objects and picking them up, manipulating man-made objects such as tools, and recognizing unknown objects through touch. In addition, an integrated programming environment is being designed that will allow grasping and grasping primitives within an overall robotic control and programming system that includes dextrous hands, vision sensors, and multiple-degree-of-freedom manipulators. A system overview is given, and the applications are discussed     

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.     

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.     

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.     

机器人抓取检测技术的研究现状

作为机器人在工厂、家居等环境中最常用的基础动作,机器人自主抓取有着广泛的应用前景,近十年来研究人员对其给予了较高的关注,然而,在非结构环境下任意物体任意姿态的准确抓取仍然是一项具有挑战性和复杂性的研究.机器人抓取涉及3个主要方面:检测、规划和控制.作为第1步,检测物体并生成抓取位姿是成功抓取的前提,有助于后续抓取路径的规划和整个抓取动作的实现.鉴于此,以检测为主进行文献综述,从分析法和经验法两大方面介绍抓取检测技术,从是否具有抓取物体先验知识的角度出发,将经验法分成已知物体和未知物体的抓取,并详细描述未知物体抓取中每种分类所包含的典型抓取检测方法及其相关特点.最后展望机器人抓取检测技术的发展方向,为相关研究提供一定的参考.     

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.     

Demonstration-based learning and control for automatic grasping

We present a method for automatic grasp generation based on object shape primitives in a Programming by Demonstration framework. The system first recognizes the grasp performed by a demonstrator as well as the object it is applied on and then generates a suitable grasping strategy on the robot. We start by presenting how to model and learn grasps and map them to robot hands. We continue by performing dynamic simulation of the grasp execution with a focus on grasping objects whose pose is not perfectly known.     

The SPRING Hand: Development of a Self-Adaptive Prosthesis for Restoring Natural Grasping

Commercially available prosthetic hands are simple grippers with one or two degrees of freedom; these pinch type devices have two rigid fingers in opposition to a rigid thumb. This paper focuses on an innovative approach for the design of a myoelectric prosthetic hand. The new prosthesis features underactuated mechanisms in order to achieve a natural grasping behavior and a good distribution of pinching forces. In this paper it is shown that underactuation allows reproducing most of the grasping behaviors of the human hand, without augmenting the mechanical and control complexity.