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。     

Primitive Object Grasping for Finger Motion Synthesis

We developed a new framework to generate hand and finger grasping motions. The proposed framework provides online adaptation to the position and orientation of objects and can generate grasping motions even when the object shape differs from that used during motion capture. This is achieved by using a mesh model, which we call primitive object grasping (POG), to represent the object grasping motion. The POG model uses a mesh deformation algorithm that keeps the original shape of the mesh while adapting to varying constraints. These characteristics are beneficial for finger grasping motion synthesis that satisfies constraints for mimicking the motion capture sequence and the grasping points reflecting the shape of the object. We verify the adaptability of the proposed motion synthesizer according to its position/orientation and shape variations of different objects by using motion capture sequences for grasping primitive objects, namely, a sphere, a cylinder, and a box. In addition, a different grasp strategy called a three‐finger grasp is synthesized to validate the generality of the POG‐based synthesis framework.     

A 3D shape segmentation approach for robot grasping by parts

Neuro-psychological findings have shown that human perception of objects is based on part decomposition. Most objects are made of multiple parts which are likely to be the entities actually involved in grasp affordances. Therefore, automatic object recognition and robot grasping should take advantage from 3D shape segmentation. This paper presents an approach toward planning robot grasps across similar objects by part correspondence. The novelty of the method lies in the topological decomposition of objects that enables high-level semantic grasp planning.In particular, given a 3D model of an object, the representation is initially segmented by computing its Reeb graph. Then, automatic object recognition and part annotation are performed by applying a shape retrieval algorithm. After the recognition phase, queries are accepted for planning grasps on individual parts of the object. Finally, a robot grasp planner is invoked for finding stable grasps on the selected part of the object. Grasps are evaluated according to a widely used quality measure. Experiments performed in a simulated environment on a reasonably large dataset show the potential of topological segmentation to highlight candidate parts suitable for grasping.     

虚拟手抓持力觉生成算法真实性的评价

目的 虽然许多学者研发了多种虚拟手交互触力觉生成算法,但是如何评价虚拟手交互触力觉生成算法的真实性是一个富有挑战性的新问题,值得深入研究.方法 构建手指抓持力测量平台,设计3种抓持姿态下指尖静力抓持球体实验内容,测得各指尖作用力的实测值;通过虚拟手静力抓持力觉生成算法,求得这3种抓持姿态下各手指作用力的理论值;对实测值进行统计和分析,并与理论值进行对比和讨论;结果 日常抓持经验和实测值是完全相符的,实测值和理论值很接近且偏差均在可接受范围之内.单个手指作用力或多个手指合力的实测值与理论值的偏差均在1%6%.结论 本文实现了一种基于物理的实验方法,评价和分析了虚拟手静力抓持力觉生成算法的真实性,证实此算法可以逼真地生成虚拟手抓持力,可应用于具有力反馈的自然的虚拟手交互.     

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.     

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.     

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.     

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.     

PASA-GB Hand: a Novel Parallel and Self-adaptive Robot Hand with Gear-belt Mechanisms

This paper introduces a novel underactuated hand, the PASA-GB hand, which has a hybrid grasping mode. The hybrid grasping mode is a combination of parallel pinching (PA) grasp and self-adaptive enveloping (SA) grasp. In order to estimate the performance of grasping objects, the potential energy method is used to analyze the grasping poses and stabilities of the PASA-GB hand. The calculation of force distribution shows the influence of the size and position of objects and provides a method to optimize the force distribution. The switch condition between pinching and enveloping grasp is analyzed in detail. Experimental results verify wide adaptability and high practicability of the PASA-GB hand.     

Human-inspired force compliant grasping primitives

We address the problem of grasping everyday objects that are small relative to an anthropomorphic hand, such as pens, screwdrivers, cellphones, and hammers from their natural poses on a support surface, e.g., a table top. In such conditions, state of the art grasp generation techniques fail to provide robust, achievable solutions due to either ignoring or trying to avoid contact with the support surface. In contrast, when people grasp small objects, they often make use of substantial contact with the support surface. In this paper we give results of human subjects grasping studies which show the extent and characteristics of environment contact under different task conditions. We develop a simple closed-loop hybrid grasping controller that mimics this interactive, contact-rich strategy by a position-force, pre-grasp and landing strategy for finger placement. The approach uses a compliant control of the hand during the grasp and release of objects in order to preserve safety. We conducted extensive robotic grasping experiments on a variety of small objects with similar shape and size. The results demonstrate that our approach is robust to localization uncertainties and applies to many everyday objects.     

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...     

Ensuring kinetostatic consistency in observation of human manipulation

The aim of this paper is to present a method to guarantee the kinetostatic consistency in observation of human manipulation, i.e. the consistency between the observed hand posture and the tactile information on the contact between the fingertips and the objects. The core idea of the proposed algorithm is to compare the fingertip contact information, obtained by tactile sensors, with the contact information computed in a virtual environment, that reproduces the real environment where the observation is carried out. In case the estimation of the joint angles and the relative pose between the hand and the object are not consistent, a correction of the hand posture is computed. For some tasks, collisions might occur between parts of the hand (e.g. palm) and the grasped object. To handle this problem, the corrected hand posture is computed by adopting a closed loop inverse kinematic (CLIK) approach that exploits the redundant Degrees of Freedom (DoFs) of the hand. The algorithm has been designed to work on-line. This feature is particularly important for Programming by Demonstration (PbD) applications, since it allows the trainer to actively adapt the demonstration to measurement noise and model errors. The effectiveness of the proposed method has been tested in five different tasks: grasping a cup, unscrewing a bottle, grasping a plate, grasping a ketchup bottle, and grasping a measuring cup.     

Pose Planning for a Mobile Manipulator Based on Joint Motions for Posture Adjustment to End-Effector Error

This paper proposes a motion planning method for a mobile manipulator. In general, humans can grasp an object by various ways which depend on object posture, position and so on. The objective of this paper is to present how to detect the pose of a mobile manipulator under the condition that several ways of grasping are given to the robot. Motion errors and object position errors are considered to detect robot pose in our method because these affect the grasp motion of the robot hand. Coping with these errors, we will propose an effective pose searching method for a mobile manipulator from numerous pose candidates. The performance of the proposed method is illustrated by simulation and experiment.     

Automatic recognition of object size and shape via user-dependent measurements of the grasping hand

An investigation is conducted on the feasibility of using the posture of the hand during prehension in order to identify geometric properties of grasped objects such as size and shape. A recent study of Paulson et al. (2011) already demonstrated the successful use of hand posture for discriminating between several actions in an office setting. Inspired by their approach and following closely the results in motor planning and control from psychology (Makenzie and Iberall, 1994), we adopt a more cautious and punctilious approach in order to understand the opportunities that hand posture brings for recognizing properties of target objects. We present results from an experiment designed in order to investigate recognition of object properties during grasping in two different conditions: object translation (involving firm grasps) and object exploration (which includes a large variety of different hand and finger configurations). We show that object size and shape can be recognized with up to 98% accuracy during translation and up to 95% and 91% accuracies during exploration by employing user-dependent training. In contrast, experiments show less accuracy (up to 60%) for user-independent training for all tested classification techniques. We also point out the variability of individual grasping postures resulted during object exploration and the need for using classifiers trained with a large set of examples. The results of this work can benefit psychologists and researchers interested in human studies and motor control by providing more insights on grasping measurements, pattern recognition practitioners by reporting recognition results of new algorithms, as well as designers of interactive systems that work on gesture-based interfaces by providing them with design guidelines issued from our experiment.     

Shape Morphing-Based Control of Robotic Visual Servoing

We present an approach for controlling robotic interactions with objects, using synthetic images generated by morphing shapes. In particular, we attempt the problem of positioning an eye-in-hand robotic system with respect to objects in the workspace for grasping and manipulation. In our formulation, the grasp position (and consequently the approach trajectory of the manipulator), varies with each object. The proposed solution to the problem consists of two parts. First, based on a model-based object recognition framework, images of the objects taken at the desired grasp pose are stored in a database. The recognition and identification of the grasp position for an unknown input object (selected from the family of recognizable objects) occurs by morphing its contour to the templates in the database and using the virtual energy spent during the morph as a dissimilarity measure. In the second step, the images synthesized during the morph are used to guide the eye-in-hand system and execute the grasp. The proposed method requires minimal calibration of the system. Furthermore, it conjoins techniques from shape recognition, computer graphics, and vision-based robot control in a unified engineering amework. Potential applications range from recognition and positioning with respect to partially-occluded or deformable objects to planning robotic grasping based on human demonstration.     

Improving digital human modelling for proactive ergonomics in design

This paper presents the need to improve existing digital human models (DHMs) so they are better able to serve as effective ergonomics analysis and design tools. Existing DHMs are meant to be used by a designer early in a product development process when attempting to improve the physical design of vehicle interiors and manufacturing workplaces. The emphasis in this paper is placed on developing future DHMs that include valid posture and motion prediction models for various populations. It is argued that existing posture and motion prediction models now used in DHMs must be changed to become based on real motion data to assure validity for complex dynamic task simulations. It is further speculated that if valid human posture and motion prediction models are developed and used, these can be combined with psychophysical and biomechanical models to provide a much greater understanding of dynamic human performance and population specific limitations and that these new DHM models will ultimately provide a powerful ergonomics design tool.     

Improving digital human modelling for proactive ergonomics in design

This paper presents the need to improve existing digital human models (DHMs) so they are better able to serve as effective ergonomics analysis and design tools. Existing DHMs are meant to be used by a designer early in a product development process when attempting to improve the physical design of vehicle interiors and manufacturing workplaces. The emphasis in this paper is placed on developing future DHMs that include valid posture and motion prediction models for various populations. It is argued that existing posture and motion prediction models now used in DHMs must be changed to become based on real motion data to assure validity for complex dynamic task simulations. It is further speculated that if valid human posture and motion prediction models are developed and used, these can be combined with psychophysical and biomechanical models to provide a much greater understanding of dynamic human performance and population specific limitations and that these new DHM models will ultimately provide a powerful ergonomics design tool.     

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.     

A variable compliance, soft gripper

Autonomous grasping is an important but challenging task and has therefore been intensively addressed by the robotics community. One of the important issues is the ability of the grasping device to accommodate varying object shapes in order to form a stable, multi-point grasp. Particularly in the human environment, where robots are faced with a vast set of objects varying in shape and size, a versatile grasping device is highly desirable. Solutions to this problem have often involved discrete continuum structures that typically comprise of compliant sections interconnected with mechanically rigid parts. Such devices require a more complex control and planning of the grasping action than intrinsically compliant structures which passively adapt to complex shapes objects. In this paper, we present a low-cost, soft cable-driven gripper, featuring no stiff sections, which is able to adapt to a wide range of objects due to its entirely soft structure. Its versatility is demonstrated in several experiments. In addition, we also show how its compliance can be passively varied to ensure a compliant but also stable and safe grasp.