基于数据手套的仿人机械手控制系统

为了以自然、直观的方式控制仿人机械手,提高仿人机械手的操作性能,设计和实现了基于数据手套的仿人机械手控制系统.提出了从14-传感器数据手套到仿真虚拟手和五指型仿人机械手的关节角度映射方案.设计了实时仿真控制和在线控制两类控制模式,提出的借助仿真虚拟手可及时展示控制效果,解决了在线控制仿人机械手时运动时延带来的控制不直观的问题.实验结果证明了基于数据手套的仿人机械手控制系统的正确性和实用性.     

基于云平台交互的手部康复训练评估手套

为了协助医生实现远程指导病人进行手部康复训练,提出了一种基于云平台交互的手部康复训练评估手套的设计。该系统利用示教手套采集医生的手部姿势数据,将其通过云平台转发至气动式的康复训练手套,控制康复训练手套执行对应动作以辅助患者同步运动。实验结果表明,该系统具备在线康复训练指导的能力,为提高医护人员工作效率和减轻患者负担提供了一个有效途径。     

一种面向遥操作的新型数据手套研制

本文主要介绍了自制的BHG-Ⅱ型数据手套的设计、原理和实验,给出了该数据手套 的性能、优缺点和实验数据,并借助图形仿真模型验证了利用数据手套在虚拟环境下进行人 机交互的可行性,最后通过数据手套对灵巧手控制实验说明了其在遥操作领域应用的有效性 ．     

一种基于气动柔性驱动器的仿人机械手设计

为了提高柔性机械手结构刚性，保证其柔顺性以及实现灵巧运动，本文基于柔性驱动器技术，分析人手的外形、结构和运动特点，采取以气动柔性驱动器作为驱动关节的驱动方式，再结合刚性机械结构，设计出一种具有气动柔性手指、拇指对掌关节、刚性掌部的仿人机械手，并对其进行了姿态及抓握实验。实验结果表明，该仿人机械手具有良好的灵巧性和稳定的抓取能力。     

五指形仿人机械手的数学模型研究

以人手的解剖学研究成果为基础，对具有五个手指和手掌的仿人机械手（以下简称仿人机械手）的数学模型进行研究。首先，以现有的工业机器人研究成果为基础，提出了仿人机械手的坐标系建立方法。然后，从人手的解剖学特点出发，采用D—H变换矩阵建立了仿人机械手运动学模型。     

一种微细零件上下料机械手的设计与仿真

针对研制一种微细零件的CNC加工装备,提出了能快速反应的移动机械手的总体方案,实现仿人动作。基于多体系统运动学理论和齐次变换矩阵,建立了机械手的运动学模型,得出了机械手末端的向量方程,并在此基础上利用蒙特卡洛法求解上下料机械手的工作空间。对机械手的结构进行了适当简化后,将所建立的三维模型导入到Adams软件中进行运动学仿真,得到了机械手卜任意一点的位移、速度和受力等方面的分析曲线,对机械手控制的实现及物理样机的制作奠定了基础。     

基于物理的虚拟手抓持力觉生成和反馈

提出了一种基于物理的虚拟手静力抓持虚拟物体力觉生成和反馈方法,借鉴机械手抓持原理,在建立基于物理的虚拟手静力抓持通用力学模型并对其进行可解性分析的基础上,针对通用力学模型的多解性,提出了虚拟手最小力螺旋模型以生成力觉,并根据抓持物体的不同,进行模型实例化,实时求得各虚拟手指上的力和(或)力矩,实验结果表明,借助于本文的力觉生成和反馈方法,利用CyberGrasp力觉反馈数据手套,用户可在抓持虚拟物体时感受到真实的接触力。     

结合机器视觉的采摘机械手的定位仿真研究

对水果采摘机械手空间定位机理进行了研究,分析了双目立体视觉系统的定位误差并建立视觉误差补偿机制,利用虚拟机械手开发软件和CCD视觉硬件构建了仿真系统,通过双目立体视觉获取空间位置数据映射到虚拟环境下引导机械手进行模拟采摘。该系统利用多领域知识融合实现了采摘机构与视觉关联精确定位的仿真,能有效地指导实际作业环境中采摘机械手精确定位的优化设计。     

仿人机器人复杂动作设计中人体运动数据提取及分析方法

提出了仿人机器人复杂动作设计中人体运动数据提取及分析方法. 首先, 通过运动捕捉系统获取人体运动数据, 并采用运动重定向技术, 输出人--机简化模型的数据; 然后, 对运动数据进行分析和运动学解算, 给出基于人体运动数据的仿人机器人逆运动学求解方法, 得到仿人机器人模型的关节角数据; 再经过运动学约束和稳定性调节后, 生成能够应用于仿人机器人的运动轨迹. 最终, 通过在仿人机器人BHR-2上进行刀术实验验证了该方法的有效性.     

面向乒乓球对弈作业的七自由度仿人臂多目标轨迹规划

人类能对乒乓球拥有快速对弈作业的运动技能,并能以一种动作能耗小且舒适自然的手臂构型进行动作,其原因是由于人在长期的学习训练过程中积累了丰富的具有相应运动特性的"知识"信息.受人臂动作此行为机制启发,提出一种七自由度仿人臂面向乒乓球作业的多目标轨迹规划方法.该方法考虑仿人臂乒乓球作业的机构物理约束、障碍约束与任务约束条件,以仿人臂作业轨迹的能量消耗与臂姿构型舒适性为优化准则,采用多目标粒子群优化方法优选动作轨迹的决策变量获得一组最优的Pareto解集,决策者可根据实际决策需求选择其中一非支配解;在此基础上利用仿人臂多目标轨迹优选方法对其整个作业任务空间学习训练,构建仿人臂乒乓球对弈作业"知识"库.仿真试验结果表明该方法能较好地逼近真实的Pareto前沿,实现全局多目标寻优,且所优选的轨迹能满足仿人臂乒乓球作业的机构物理约束、障碍约束与任务约束条件,实际仿人臂动作测试也验证了该方法的有效性.     

Robot teaching system based on hand-robot contact state detection and motion intention recognition

This paper presents a robot teaching system based on hand-robot contact state detection and human motion intent recognition. The system can detect the contact state of the hand-robot joint and extracts motion intention information from the human surface electromyography (sEMG) signals to control the robot's motion. First, a hand-robot contact state detection method is proposed based on the fusion of the virtual robot environment with the physical environment. With the use of a target detection algorithm, the position of the human hand in the color image of the physical environment can be identified and its pixel coordinates can be calculated. Meanwhile, the synthetic images of the virtual robot environment are combined with those of the physical robot scene to determine whether the human hand is in contact with the robot. Besides, a human motion intention recognition model based on deep learning is designed to recognize human motion intention with the input of sEMG signals. Moreover, a robot motion mode selection module is built to control the robot for single-axis motion, linear motion, or repositioning motion by combining the hand-robot contact state and human motion intention. The experimental results indicate that the proposed system can perform online robot teaching for the three motion modes.     

A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models

The Programming by Demonstration (PbD) technique aims at teaching a robot to accomplish a task by learning from a human demonstration. In a manipulation context, recognizing the demonstrator's hand gestures, specifically when and how objects are grasped, plays a significant role. Here, a system is presented that uses both hand shape and contact-point information obtained from a data glove and tactile sensors to recognize continuous human-grasp sequences. The sensor fusion, grasp classification, and task segmentation are made by a hidden Markov model recognizer. Twelve different grasp types from a general, task-independent taxonomy are recognized. An accuracy of up to 95% could be achieved for a multiple-user system.     

结合深度学习的机器人示教系统设计

传统的机器人示教系统在使用上受到应用对象和硬件设备的限制,导致其开放性和易用性较低,为了降低示教系统的使用门槛,提高人机交互的效率,利用ROS（Robot Operating System）的开放性和跨平台性,设计了手势引导机器人示教系统,可以控制机器人进入学习、编码、执行等模式。系统采用YCbCr与RGB空间相结合的肤色分割算法,利用CNN深度学习框架进行特征提取完成手势识别;基于ROS集成手势对机器人模式控制。通过在公开数据集上实验验证手势识别准确率可达96.49%,并测试了系统的有效性与可靠性。     

Augmented reality-based robot teleoperation system using RGB-D imaging and attitude teaching device

Augmented reality (AR)-based programming using the demonstration method has been widely studied. However, studies on AR-based programming for remote robots are lacking because of the limitation of human–computer interaction. This paper proposes an AR-based robot teleoperation system and method using RGB-D imaging and an attitude teaching device. By sending the color and depth images of the remote robot environment to the local side, the operators can complete the teleoperation of the robot at the local side. First, the operators select key positions on the motion path of the robot endpoint from color images via a mouse, and the computer calculates the 3D coordinates of these key points in the robot base coordinate system to complete the position teaching process. In the robot attitude teaching process, the AR technology is used to superimpose the virtual robot model onto the color images of the robot teleoperation environment, so as to make the virtual robot endpoint to move along the teaching path. An operator can use the portable attitude teaching device designed in this study to control the robot movement parameters, such as the attitude and motion speed, during the movement of the virtual robot. After the position and attitude teaching processes, the robot movement trajectory can be generated. To make the base coordinate system of the virtual model consistent with that of the physical robot, we propose an online AR registration method, which does not require manually placing the AR registration marker. The proposed AR-based robot teleoperation system can quickly and easily complete robot teleoperation at the local side.     

基于问题的学习在机器人教学中的应用初探

机器人教学作为一门新兴的课程,有其自身的特点,传统的教学模式已经无法适应机器人学科。探讨了基于问题的学习模式在机器人教学中的应用,指出了基本问题的学习应用于机器人教学的优势,设计了PBL应用于智能机器人教学的基本流程,并提出了PBL应用中需要注意的一些问题。     

The manipulator language with the characteristic of a functional programming language

This article describes the motion-oriented robot language IML (interactive manipulator language) with the characteristic of a functional programming language. The main functions of IML are (1) It is possible to describe the iterative motions without using a loop. (2) The user-defined procedures (commands) can be called by specifying the command name. (3) It is possible to describe robot motion (a sequence of the position and orientation of a robot hand) and force magnitude in the task-oriented Cartesian coordinate system (task coordinate system) suitable for robot tasks. Furthermore, it is possible to describe the translation and rotation of the coordinate system without syntactic distinction. (4) As the teaching data can be easily embedded in the language and can be played back in the force control mode, complex task programming becomes easy. In IML, as the user-defined command and the teaching data can be used just as the builtin system command; the system can be extended easily and naturally. In this article these features are described, and it is shown that these functions can be realized with the unified representation by introducing the concept of a functional programming language. The effectiveness of IML was confirmed by actually making a robot perform some tasks programmed in IML.     

A 3D-printed Robot Hand with Three Linkage-driven Underactuated Fingers

In this study, we improved an underactuated finger mechanism by using Solidworks to simulate the grasp operation of a finger in some different situations. In addition, a robot palm is designed for the three-finger robot hand with the designed underactuated fingers. A Solidworks simulation was used to verify the rationality of the design. Some parts of the hand were modified to fit for 3D printing, and a prototype of the hand was produced by 3D printing, which could reduce the cost of the production process, as well as provide design flexibility and other advantages. Finally, some grasping experiments were made with the prototype. The results showed that the robot could grasp objects with different sizes, and further verified the rationality of the design and feasibility of fabricating the robot hand using 3D printing.     

基于CoDeSys平台的六自由度工业机器人运动控制器设计

针对ER50六自由度工业机器人,基于CoDeSys软件平台开发了一款机器人运动控制器。采用ARM+CoDeSys架构和PLCopen规范进行六自由度工业机器人控制系统软件开发以及人机交互界面设计。首先根据D-H参数对ER50机器人进行运动学建模,并在此基础上封装ER50机器人正逆运动学功能块;然后对控制器的示教模块、点动模块以及在线编程模块进行软件开发并设计各个模块的人机交互界面。在ER50机器人上实现了运动控制器在线示教、点动以及在线编程等功能;最后通过直线和圆弧轨迹跟踪实验验证了运动控制器设计的有效性。     

Incremental motion learning through kinesthetic teachings and new motion production from learned motions by a humanoid robot

This work presents a new incremental motion learning algorithm through kinesthetic teachings and a new motion production algorithm by combining learned motions in a humanoid robot. The proposed algorithms are useful for improving the motions that a humanoid robot can produce. The learning algorithm consists of data encoding, time alignment, dimensional reduction, parameter estimation in the Gaussian mixture model (GMM) of motions, GMM refinement, and motion generation steps. The overall procedure is built to be incremental. No historic data memorization is required in any step, and model parameters are enough information to generate motions. The motion production algorithm allows a robot to extract new motions simply from learned motions without requiring teaching sessions. A series of experiments with a humanoid robot serves to validate the performance of the proposed algorithms.