6自由度机械臂远程控制系统研究

机器人控制技术在全球范围内飞速发展,人与机器人之间的交互方式正朝着方便、直观的方向发展。通过体感表征操作人员的骨骼和腕部信息,可以为机器人的远程操控提供必要的控制信息。该文融合视觉和可穿戴体感,研究了交互式6自由度机械手臂控制系统,使用人体肩、肘的旋转角度和握拳动作,来分别控制机械手臂的6个关节和抓取;并构建了分布式远程操控实验平台,数据和指令通过无线方式进行传输。实验结果表明,采用融合视觉和可穿戴体感的交互式机器人能有效直观地控制机械臂进行物体抓取。     

基于Kinect与Dobot机械臂的人机交互系统

文中系统主要为通过Kinect体感器捕获人体手的坐标移动以及手势变化,利用坐标转化等手段,将其转化为指令发送到Dobot机械臂,使其能够在非特定条件下,按照人体手的轨迹做特定运动,并进行抓取物体等功能。系统中的Kinect体感器获取坐标为三维空间中的人体手的移动轨迹,使得Dobot的机械臂运动更加贴近人体实际操作,大大加强了实用价值。另外Kinect还将获取人体手势,用于控制Dobot末端夹具的开合。     

翻转装卸用机械手臂的设计

本文主要介绍了一种翻转装卸用机械手臂的设计与实现,该机械手臂包括机械臂与底板、下机械臂、推管、液压、电机、转轴、定位柱等装置,能够适用于较重、表面容易破损的物品的搬运和传递。该设计的主要特点由上机械臂与下机械臂组成,其中下机械臂用于物品的定位与固定,而上机械臂则用于物品的紧固以及再定位。     

仿人形理疗机械手整体架构研究与关键技术概述

仿人形理疗机械手是以手指手掌障碍者理疗为主,并以上肢障碍者复健为辅的医疗器械,其以提升障碍者佩带舒适度和器械运动合理性为科研目标。手掌手臂由拟态仿生、智能控制和传感器融合等技术要素相互合理构架、对比调试。并结合市场调研,根据目标人群的病理特性和损伤情况,利用仿生学中的信息与控制仿生科的研究方式,考察模拟目标人群使用机械手时的运动状态、身体状态和环境状态等影响使用因素,以达到复健理疗机械臂的精准控制效应。目前该器械已完成理论架构、三维组装、动态仿真和市场调研。从国内外发展现状、仿人形理疗机械手整体架构、仿人形理疗机械手的工作方式和理疗效果等内容出发,对复健理疗机械臂的技术架构进行系统的介绍。     

教你DIY手柄遥控的机械臂

机械臂、机械手相信大家都不陌生，医院里的手术机械手、工厂里的工业机械手，那在电机控制下特有的运动节奏、泛着令属光泽的机械部件，每次看到都让我这样的机电爱好者感到心动。如今我找到了一款小型的6自由度机械臂，终于能圆我一个玩机械手的梦了！     

采摘机器人机械臂的控制与联合仿真

采摘机器人属于非结构环境下作业的特种机器人,果实的采摘易受环境变化的影响,机械臂的控制直接决定采摘机器人的作业效果.根据番茄的特点及其植株的生长分布,设计了具有平行结构关节的四自由度机械臂,实现了对机械臂各个关节的控制.使用ADAMS/Controls与MATLAB/simulink进行联合仿真,根据仿真结果,不断调节控制参数,实现了机械臂的运动规划和性能指标的优化.     

基于3DMAX的流水线机械手仿真动画

本文就关于流水线机械手的运动轨迹做了主要阐述。全文从机械手臂的虚拟动画演练、抓取实物范例以及两个机械手之间的传递三方面进行论述说明,从而使大家可以简单清晰地全方位了解机械手的整个运动。     

基于残疾康复群体的一种上臂辅助机械

目前我国的残障数占总人口的6.34%,其中大部分患者有残疾康复的需求。针对社会需求,设计了一款针对上肢残疾患者使用的家用康复产品。设计的上臂康复机械是一个帮助患者康复的穿戴设备,患者将其穿戴在手臂上时,手臂固定处的传感器会感应患者施加的力并通过电动机带动机械使手臂弯曲,或通过蓝牙连接机械进行远程操控,协助患者做出相应动作,达到锻炼手臂的目的。     

基于DSP的机械手控制系统

基于步进电机的机械手控制平台的设计,硬件以TMS320F2812DSP芯片为控制核心,由水平方向转动的腰部步进电机、垂直方向转动的手臂步进电机和电磁吸棒三部分组成,机械手臂均能360度任意转动。机械手软件包括主控程序、电机运行程序、键盘中断程序、液晶显示接口程序。操作者能通过手动、循环自动和远程遥控等多种控制形式来控制机械手的运行。     

同步仿生机械臂设计

本设计利用安装在人手臂部的加速度传感器采集运动信号,单片机智能运算后发出控制指令,实现机械臂与人臂的同步运动。系统将在采集传感器的输出模拟量经过单片机的处理产生PWM波,使用PWM波驱动舵机实现准确定位,使机械臂实现三自由度的运动。而且还可通过编程和仿生来完成各种预期的作业任务,在构造和性能上兼有人和机器各自的优点,体现了人的智能和适应性。     

Bio-driven system-based virtual reality for prosthetic and rehabilitation systems

Current prosthetic and rehabilitation devices, used for those who are limbless or born with congenital defects or required rehabilitation, are difficult to use. The users have problems to adapt to their new hosts or receiving any bio-feedback despite rehabilitation process and retraining, particularly when working with electromyogram (EMG) signals. In characterizing virtual human limbs, as a potential prosthetic device in three dimensions (3D) virtual reality, patients are able to familiarize themselves with their new appendage and its capabilities or can see their movements’ intention in a Virtual Training Environment. This paper presents a virtual reality (VR)-based design and implementation of a below-shoulder 3D human arm capable of 10-class EMG-based motions driven system of biomedical EMG signal. The method considers a signal classification output as potential control stimulus to drive the virtual limb. A hierarchical design methodology is adopted based on anatomical structure to congruent with virtual reality modeling language (VRML) architecture used in order to progressively build the user interface model and its inherent functionality. The resulting simulation is based on a portable, self-contained VRML prototype implementation paired with an instrumental virtual control-select board capable of actuating any combinations of singular or paired kinematic of 10-class EMG motions. The simulation allows for multiple degree-of-freedom profiles as the classes can be activated independently, or in conjunction with others, allowing enhanced arm movement. Provisions for direct classified control inputs are built into the prototype for holistic system construction.     

Variable admittance time-delay control of an upper limb rehabilitation robot based on human stiffness estimation

This paper proposes a new variable admittance time-delay control strategy based on human stiffness estimation for improving the effectiveness of robot-assisted cooperative rehabilitation training. This control strategy is developed and implemented on a planar upper limb rehabilitation robot. Given the minimum-jerk-based desired trajectories of human hand position, in the developed control strategy, a time-delay approximator is utilized to estimate the external disturbances and modeling errors without exact knowledge of dynamics parameters, a sliding mode admittance controller is applied to obtained objective admittance characteristics, and an iterative optimization algorithm is used to estimate human arm stiffness and adjust human-robot interaction compliance. The closed-loop stability of the proposed control method is demonstrated via Lyapunov function theory. Experimental investigations involving ten subjects are conducted to validate the feasibility of the proposed control scheme. The experimental results show that the interaction compliance during cooperative rehabilitation training can be accurately adjusted based on selected admittance parameters and human arm stiffness, and it contributes to satisfying the specific training requirements of patients with different weakness levels and promoting the effectiveness of the robot-assisted training.     

Newton-method based iterative learning control for robot-assisted rehabilitation using FES

Precise control of useful movement is critical in providing effective upper limb stroke rehabilitation using functional electrical stimulation (FES). To address the lack of accuracy currently available in clinical practice, this paper develops a general framework based on iterative learning control (ILC), an approach that has been successfully employed in three clinical treatment trials. An upper limb model is first developed to encompass unconstrained movements of the upper arm. In line with clinical need, additional assistance is then incorporated via a general class of robotic support mechanism. An iterative learning scheme is then developed to enable a subset of joint angles to be controlled via stimulation of an arbitrary set of muscles. This scheme is the first ILC approach which explicitly addresses coupled multivariable nonlinear dynamics in upper-limb rehabilitation, enforcing convergence over multiple executions of a reaching task. Experiments with six participants confirm practical utility and performance.     

基于红外和彩色图像传感器的机械手臂控制系统

提出了一种机械手臂控制系统。硬件系统主要由包含红外和彩色图像传感器的Kinect、Atom平台上位机、舵机控制器和机械手臂组成。使用图像分割识别、SVM、线性滤波、人机关节映射等算法实现了机械手臂在多种模式下的实时人体追踪和模仿功能。开发了基于C#的图形用户界面和基于Java3D的三维仿真平台,增强用户开发和使用体验。本系统精度高,延迟小,对环境有较强的鲁棒性,具有较强的应用前景。     

An intelligent robotic system for rehabilitation of joints andestimation of body segment parameters

An an application of robotics in physical rehabilitation therapy, a robotic system consisting of two planar robot arms, each with two degrees of freedom, is considered. This robotic system, when coupled across a human joint, provides a vehicle for rehabilitation of the joint following surgery or trauma. A novel approach for estimation of body segment parameters is formulated that uses state and output information from the robot system to improve these estimates. In addition, redundant sensors are used to improve the accuracy of the estimates. The dynamic equations for a single robot arm are provided and the system is simulated. Therapeutic applications of the robotic system are discussed and the sensitivity of the measured forces with respect to the robot arm joint angles is studied in order to find an optimum orientation of the system for the best possible estimation. The application of this system to both rehabilitation and sports medicine is also discussed     

基于ATmega32的遥控采摘机器人设计

针对机器视觉采摘机器存在的技术不成熟,成本过高等不足,提出采用人工操控的半自动采摘技术,并进行了探索研究。设计了一款模拟采摘机器人,机器人以履带底盘为基座,三自由度机械臂,红外遥控操作。硬件电路以ATmega32为系统控制核心,采摘机器人的动作采用BL35P12为核心红外遥控控制。系统软件基于AVR Studio4开发环境,实现对机械臂运动的全方位精确控制。采摘机器人采用模块化设计,抓取速度快,成本低,易于实现,可扩展性强。     

基于SVM的上肢康复机械臂轨迹跟踪预测控制

针对上肢康复训练机械臂具有强耦合、非线性和时变的特点,设计了基于SVM（支持向量机）的轨迹跟踪预测控制器。采集机械臂系统的输入和输出数据,通过SVM辨识得到广义逆系统,与原系统串联实现解耦。对解耦后的系统,采用SVM辨识预测模型和PSO优化滚动控制序列的预测函数控制方法,并从其内模结构分析了系统的稳定性和鲁棒性。实验结果表明,该方法能够平稳高精度地实现轨迹跟踪。     

Application of rubber artificial muscle manipulator as arehabilitation robot

The application of a robot to rehabilitation has become a matter of great concern. This paper deals with functional recovery therapy, one important aspect of physical rehabilitation. Single-joint therapy machines have already been achieved. However, for more efficient therapy, multjoint robots are necessary to achieve more realistic motion patterns. This kind of robot must have a high level of safety for humans. A pneumatic actuator may be available for such a robot, because of the compliance of compressed air. A pneumatic rubber artificial muscle manipulator has been applied to construct a therapy robot with two degrees of freedom (DOF). Also, an impedance control strategy is employed to realize various motion modes for the physical therapy modes. Further, for efficient rehabilitation, it is desirable to comprehend the physical condition of the patient. Thus, the mechanical impedance of the human arm is used as an objective evaluation of recovery, and an estimation method is proposed. Experiments show the suitability of the proposed rehabilitation robot system     

A robust ensemble data method for identification of human joint mechanical properties during movement

This paper describes a perturbation method for the identification of linear time-varying systems with an unknown input (voluntary joint input) using ensemble data. The method separates the unknown input and the perturbation through high-pass filtering and recasts the multi-input single-output system identification into single-input single-output system identification. The method is robust to intertrial variation, and can track changes of system dynamics up to 5 Hz. Analysis and simulation are given for the conditions similar to those for the human arm experiments. Experiments show that mechanical properties of the human elbow joint change with voluntary movement speed and that the mean stiffness with voluntary movement is in the range of the posture and is higher than reported before.     

基于自适应按需辅助的上肢镜像控制策略

为了提高偏瘫患者在镜像康复训练过程中的主动性与系统抗干扰性,该文提出一种基于自适应按需辅助的上肢镜像控制策略。该策略主要包括镜像控制和自适应按需辅助控制两个模块,镜像模块采集健侧位置解算出患侧期望位置,与患侧实际位置比较得到位置偏差;按需辅助模块结合传统阻抗控制和一种患肢运动状态评估方法,自动实时调节对患肢的辅助力大小,以实现患肢主动力矩的最大化。该文设计了两项实验,分别验证本策略对患者主动性和系统抗干扰性的提升效果,实验结果表明,对于镜像康复训练系统使用所提方法有效减小了56.9%的平均辅助力,患肢跟随健肢的位置精度为5.6%,能有效补偿89%的干扰外力。因此所提方法有效提升了训练者的主动性,同时具有良好的抗干扰性,满足镜像康复训练的要求。