具有感觉反馈的仿生假手人机交互研究

仿生假手是一个典型的生机电一体化系统.针对现有假手缺乏感觉反馈的问题,研究了具有双向信息传输能力的生机电一体化假手人机交互系统.采用压力传感器阵列检测肌肉压力分布信号,通过压力分布图的构建,基于主成分分析的特征提取和基于KNN分类器的特征识别方法实现了人手多运动模式的解码.基于假手力传感器,采用电刺激方式实现了假肢接触力向人体的感觉反馈.实验表明,基于肌肉压力分布信号的手部多运动模式识别方法具有较高的正确率,采用具有感觉反馈的交互控制系统可提高假手的抓取能力.     

基于FPGA的仿人假手控制系统设计

为了满足生机电一体化仿人假手的控制需求,提出了基于FPGA的仿人假手电气控制系统设计方案。采用模块化设计思想,由FPGA构成的主控芯片模块便于功能拓展与二次开发;由DSP构成的手指运动控制模块、肌电信号采集模块、电刺激模块、USG接口模块和电池管理模块均可独立工作,与主控芯片模块间通过通用接口连接。系统集成度高,可完全放置于假手内部。应用该控制系统在HIT V代手上进行多指抓取实验,实验结果证明其工作效果良好。     

仿生假手抓握力控制策略

为了使仿生假手完成各种精细作业,提出一种抓握力控制策略.在自由空间和约束空间中分别使用基于位置的阻抗控制和力跟踪阻抗控制.在过渡过程中使用模糊观测器切换控制模式.两种控制模式采用同一个基于位置的阻抗控制器,在约束空间向阻抗控制器中引入参考力,以满足约束空间的抓握力控制要求.这种方法可以使关节在自由空间和约束空间中分别实现良好的轨迹跟踪和力矩跟踪,在过渡过程中实现控制模式的可靠切换和系统的稳定过渡.提出一种自适应滑模摩擦力补偿方法,利用终端滑模思想设计了滑模函数,使得系统跟踪误差在有限时间内收敛,避免了传统线性滑模面状态跟踪误差无法在有限时间内收敛至0的问题.根据指数形式摩擦力的特点,利用终端滑模控制思想获得包含摩擦力参数估计的滑模控制律,并基于李亚普诺夫稳定性定理推导了估计参数的在线自适应律.对该抓握力控制策略在HIT假手上进行了抓取实验,实验结果证明了控制策略的有效性.     

Tracking hand rotation and various grasping gestures from an IR camera using extended cylindrical manifold embedding

This paper presents a new approach for tracking hand rotation and various grasping gestures through an infrared camera. For the complexity and ambiguity of an observed hand shape, it is difficult to simultaneously estimate hand configuration and orientation from a silhouette image of a grasping hand gesture. This paper proposes a dynamic shape model for hand grasping gestures using cylindrical manifold embedding to analyze variations of hand shape in different hand configurations between two key hand poses and in simultaneous circular view change by hand rotation. An arbitrary hand shape between two key hand poses from any view can be generated using a cylindrical manifold embedding point after learning nonlinear generative models from the embedding space to the corresponding hand shape observed. The cylindrical manifold embedding model is extended to various grasping gestures by decomposing multiple cylindrical manifold embeddings through grasping style analysis. Grasping hand gestures with simultaneous hand rotation are tracked using particle filters on the manifold space with grasping style estimation. Experimental results for synthetic and real data indicate that the proposed model can accurately track various grasping gestures with hand rotation. The proposed approach may be applied to advanced user interfaces in dark environments by using images beyond the visible spectrum.     

兼顾多动作与轻量化的仿生假肢手设计创成

假肢手的动作数量与轻量化之间存在矛盾关系，为兼顾两者之间的平衡，满足假肢手多动作和轻量化的要求，该研究通过分析人手的16种日常抓取动作，设计了一种合理的电机驱动结构。该结构在四指中应用了多关节同时屈曲传动，在拇指中应用了定轨迹适应性传动，掌骨使用可自动切换为弧面和平面的对称弹性串联驱动式传动，以及配置五指的自动伸展，将这些机能融合在假肢手中，仅用3个电机实现了11 种假肢手动作，达到了132.1 g的轻量化设计。该文还通过肌电信号结合神经网络算法，实现了假肢手直觉控制，并验证了其具有良好的抓取稳定性和操作性。     

多自由度仿生假手嵌入式控制系统及其抓取策略

为一种能够实现5 指独立动作以及具备人机交互能力的多自由度仿生假手设计了手部嵌入式控制系 统．该系统由传感器系统和运动控制系统构成,集成于假手机体内部,通过通信总线与上层控制器交换信息．传感 器系统包括3 种类型,共12 个传感器,可为假手自主抓取以及人机交互中的感觉反馈提供数据,运动控制系统用于 控制、驱动各手指动作．此外,本文以基于位置的阻抗控制为底层,以动作预构形为上层设计了分层控制策略．实 验表明,该嵌入式控制系统和分层控制策略使假手实现了自主抓取功能,提高了抓取的柔顺性、稳定性和适应性．     

基于软体手的机器人遥操作人机交互系统研制

为了提升抓取软体物体、易碎物体的完整性,设计制作了一种基于软体手的机器人遥操作人机交互系统。研制了一种嵌入了弯曲传感器的软体主手,获取人手的弯曲电信号,以此判断人手的弯曲程度,实现对软体从手系统的遥操作。并在远端软体从手中嵌入压力传感器,获取软体从手抓取目标时的表面压力电信号,以此来判断软体手抓取目标时的力度,实现力反馈。软体手人机交互系统能够减小传统刚体机械手抓取目标的限制,可有效抓取软体或易碎目标,实验表明了其有效性。     

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.     

Proposal of a Single-Trocar Assemblable Hand for Laparoscopic Surgery

Large instruments are not suitable for laparoscopic surgery because they cannot pass through trocars, which are typically less than 12 mm in diameter; however, operating on large internal organs with a slender instrument is often difficult. To overcome this limitation, we have proposed an 'assemblable instrument', whose parts are inserted through trocars and assembled inside the abdominal cavity to form a large instrument. We have previously reported the development of assemblable hands that require two trocars for assembly. In this paper, we propose a simple three-fingered hand whose function is limited to retracting or grasping, but whose assembly requires a single trocar. We develop two types of finger units: one for a retracting hand and the other for a grasping hand. The two finger units can be connected to a single main unit of the hand. We conduct in vivo experiments. The retracting hand can retract several large internal organs and lift up the stomach with its three fingers from the bottom. The grasping hand can grasp the spleen stably with its two fingers from the bottom.     

Design of an underactuated anthropomorphic hand with mechanically implemented postural synergies

Neurology shows that human controls dozens of muscles for hand poses in a coordinated manner and such coordination is referred as to a postural synergy. The concept of postural synergies was recently adopted in the control of robotic hands. With the synergies implemented digitally in a controller, all the motors in an anthropomorphic robotic hand can be controlled via a few synergy inputs. Aiming at exploring alternative approaches for synergy realization, this paper proposes to implement the postural synergies using a mechanical transmission unit. Two rotation inputs can be scaled, combined, and mapped to the rotary outputs to drive an anthropomorphic hand, enabling not only various grasping tasks but also a manipulation motion. Synergy synthesis and design of the anthropomorphic hand are firstly presented. The transmission unit as the implementation of the postural synergies is then elaborated. Tests were performed to quantify how well the synergies could be reproduced via this transmission unit. The results suggest it might be promising to construct a low cost yet versatile prosthetic hand by implementing the postural synergies mechanically.     

Mutual adaptation among man and machine by using f-MRI analysis

A prosthetic device for disabled people requires new and reliable robotics technology. This paper describes the interesting reaction of our brain to an adaptable prosthetic system. The adaptable prosthetic system is composed of an EMG signal controlled robot hand with an EMG pattern recognition learning function for Transradial (below elbow) prostheses. The mutual adaptation between the system and the human body is analyzed using functional magnetic resonance imaging (f-MRI) in order to clarify the plasticity of the motor and sensory cortex area according to the changes in the prosthetic system. The developed prosthetic hand has 13 DOF: three motors on the thumb, two motors for each finger, and two motors for the wrist. The tactile feedback is applied by using surface electrical stimulus. The f-MRI data shows the process of replacement from a phantom limb image to the prosthetic hand image.     

基于肌电信号的多模式抓握力估计

针对大多肌电控制的假肢只研究模式识别而没有对抓握力和抓握模式同步解码的问题,提出一种同时分析抓握模式和抓取力的方法。首先,采用4通道表面电极采集人体手臂肌电信号(EMG),采用力敏电阻(FSR)采集抓取力信号;然后,分别利用线性判别分析(LDA)方法和人工神经网络(ANN)进行抓握模式识别和力估计。在4种抓握模式下分别建立4个肌电信号-力关系,一旦判别出抓取模式,则调用相应模式下肌电信号-力模型估计抓握力大小以实现模式识别和力估计的结合。实验结果表明,当进行模式和力的同步解码时,模式平均分类精度约为77.8%,力估计的准确率约为90%。该方法可以用于假肢的肌电控制,不仅可以解码使用者的抓取动作的意图,还可以解码使用者期望的抓取力,辅助假肢实现稳定抓取。     

Electromyographic prosthetic hand using grasping-force-magnification mechanism with five independently driven fingers

This paper proposes an electromyographic (EMG) prosthetic hand that has five independently driven fingers, a flexion drive, and a force-magnification drive. The flexion drive allows for rapid finger motion, and the force-magnification drive allows for a firm grasp. To realize the natural feeling of control similar to that of movements with nonamputated parts, the control system includes the impedance model of human forearms and utilizes the muscle contraction level extracted from a user’s EMG signals. We experimentally verified that the maximum fingertip force of the hand exceeds 20 N, and the time required to fully close the hand by the flexion drive is 0.53 s. We also experimentally verified that in response to EMG signals, the fingers can flex smoothly and the grasping force can be modulated. Furthermore, we show that taking EMG signals as inputs makes it possible to control six operations, including ones that use the five fingers in distinctive ways.     

虚拟环境下物体的手抓取

本文分析了手模型及其虚拟环境下物体的操作方法，提出了点接触平面法矢的手握持抓取方法。在此基础上运用虚拟手模型和抓取规则实现了在虚拟环境下对物体的抓取、移动和释放，实例说明能够使虚拟手正确地抓取虚拟环境中的物体。     

On the Shared Control of an EMG-Controlled Prosthetic Hand: Analysis of User–Prosthesis Interaction

An anthropomorphic underactuated prosthetic hand, endowed with position and force sensors and controlled by means of myoelectric commands, is used to perform experiments of hierarchical shared control. Three different hierarchical control strategies combined with a vibrotactile feedback system have been developed and tested by able-bodied subjects through grasping tasks used in activities of daily living (ADLs). The first goal is to find a good tradeoff between good grasping capabilities and low attention required by the user to complete grasping tasks, without addressing advanced algorithm for electromyographic processing. The second goal is to understand whether a vibrotactile feedback system is subjectively or objectively useful and how it changes users' performance. Experiments showed that users were able to successfully operate the device in the three control strategies, and that the grasp success increased with more interactive control. Practice has proven that when too much effort is required, subjects do not do their best, preferring, instead, a less-interactive control strategy. Moreover, the experiments showed that when grasping tasks are performed under visual control, the enhanced proprioception offered by a vibrotactile system is practically not exploited. Nevertheless, in subjective opinion, feedback seems to be quite important.     

虚拟手模型及其抓取技术

在深入分析手的解剖结构和运动特点的基础上，采用层次结构建模方法，建立了虚拟手模型、然后针对已有的虚拟手抓取规则过于简化的特点，提出了一种新的虚拟手抓取规则．该规则能够使虚拟手正确地抓取虚拟环境中的物体．最后运用虚拟手模型和抓取规则实现了虚拟手对虚拟物体的抓取、移动和释放操作．     

面向任务的三指手机器人抓取规划研究

本文给出了一种面向任务的三指手爪抓取规划的思路及研究方法，首先根据人手抓取姿态的分类，总结出典型的机器人抓取姿态，并以三指手爪来完成抓取，然后综合考虑任务要求、对象物体的几何物理特性及环境信息，经任务分析，推理出抓取姿态，并通过寻找特征平衡，确定出抓取平面，再在抓取现上进一步规划出３个抓取点，最终完成抓取规划过程。     

A novel coupled and self-adaptive under-actuated multi-fingered hand with gear–rack–slider mechanism

Aiming to overcome the serious disadvantages of two kinds of under-actuated fingers: coupled finger and self-adaptive finger, this paper proposed a novel grasping mode, called Coupled and Self-Adaptive (COSA) grasping mode, which includes two stages: first coupled and self-adaptive grasping. A 2-joint COSA finger with a double gear–rack–slider mechanism (called COSA-GRS finger), is developed based on the COSA grasping mode: at the beginning, the 2-joint finger bends with coupled mode, two joints of the finger rotate simultaneously with a fixed ratio until the proximal phalanx touches the grasped object, then the finger will automatically decouple and rotate with self-adaptive mode, the distal phalanx quickly rotates until it touches the object. The new finger unit has the advantages of coupled fingers and self-adaptive fingers. The finger is not only able to rotate all joints simultaneously to pre-shape before grasping objects, but also able to self-adapt different sizes and shapes of objects. Using the same mechanism as the 2-joint finger, a 3-joint COSA finger is designed. Force analyses and a structure optimization rule of the new finger are given and discussed. The simulation results show that the finger unit is effective: it can successfully realize coupling and decoupling and it can stably grasp objects. An under-actuated humanoid robot hand is developed, called the COSA-GRS Hand. The hand has 5 fingers, 15 joints and 6 motors. All fingers of the hand are COSA fingers. The hand is more similar to human hand in appearance and actions, able to grasp different objects more dexterously and stably than traditional coupled or self-adaptive under-actuated hands.     

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.     

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.