CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor程龙
Degree Grantor中国科学院大学
Place of Conferral北京
Keyword手部康复 便携式外骨骼 Adrc + Ilc 镜像训练
Other Abstract 目前脑卒中已成为成年人致残的最重要原因之一。脑卒中患者通常伴随有认知、感觉和运动障碍等问题。在这类脑卒中患者中,大约有2/3 的人面临着手或者手臂的瘫痪问题。人手作为人类最重要的肢体,在日常生活中难以替代,因此恢复手部功能是至关重要的。手部康复机器人的应用能够解决当前脑卒中患者众多与康复训练师不足的矛盾。因此,本文主要围绕可穿戴外骨骼手部康复机器人的系统研制与镜像训练开展,包含外骨骼机器人的机械结构设计、运动学建模及其工作空间分析、信息感知系统、控制算法以及镜像训练等。论文的主要工作和创新点归纳如下。 
  1. 研制了一种新型的背包型便携式手部康复机器人。目前,手部外骨骼康复机器人可分为底座式和便携式两种。底座式康复机器人具有精确有效,患者手臂负重较小的优点,但是此类机器人往往质量较大,不便携带。便携式康复机器人便于携带,但是往往将驱动系统置于患者手臂上,增加了患者手臂的负担。针对此问题,本文研制了一种新型的背包型便携式手部康复机器人。该机器人采用便携式的外骨骼结构,便于穿戴;采用钢丝绳进行远距离动力传输,增加了机器人的柔顺性;所有的控制系统都后置于背包中,降低了患者手臂上的负重。该机器人能够实现5个手指9个关节的独立控制,能够辅助患者做屈伸运动以及分离运动。该康复机器人具有安全、便携、绳驱、模块化设计以及轻量化的优点。此外,从机械设计上来看,康复机器人机构应遵从人类手部解剖学数据,不应该限制患者手部的活动范围。因此本文对所设计的外骨骼机器人进行了工作空间分析。通过分析可知,所设计的手部康复机器人的机械结构能够遵从人体手部解剖学数据。而后本文进行了手部康复机器人的功能验证,实验结果表明所设计的康复机器人能够辅助患者实现屈伸运动、分离运动等康复运动训练。 
  2. 提出了一种新型的“ADRC + ILC”控制方法实现外骨骼机器人的有效控制。针对PID 控制参数比较难调且较难取得优良的抗扰动效果等问题,本文提出了“ADRC + ILC”的新型控制方法用于实现手部外骨骼机器人的轨迹跟踪控制。首先确定传感器的类型,利用神经网络对该传感器进行了标定。借助传感器的反馈信息,实现了闭环的控制系统。相较于文献中常见的PID 控制方法以及基于机器人精确数学模型的控制方法,本文提出的方法无需求解机器人的数学模型,且具有较好的抗扰动性能。实验结果表明所提出的“ADRC + ILC”控制方法比PID控制器具有更优良的控制效果。 
  3. 实现了基于表面肌电信号(sEMG)的人-机器人镜像训练。针对被动康复训练难以调动患者的积极性、增加患者的参与程度等问题,本文实现了基于sEMG信号的人-机器人镜像训练。首先采用神经网络实现了基于sEMG 信号的线下手势识别,针对8 类Fugl-Meyer 量表手势的分类准确率达到了97.71%。在此基础上,实现了基于sEMG 的在线动态手势识别,将识别结果作为信号源进行康复机器人的有效控制,使康复机器人能够跟随人体手势产生相应的动作,实现了人-机器人镜像训练,达到了良好的人机交互。;   In recent years, stroke has become one of the most common causes of adult disabilities. Stroke often results in a combination of cognitive, sensory and motor impairments. Hand function impairment is one of the most serious issues among these impairments, about two thirds of stroke survivors suffer from partial paralysis at the level of the arm and hand. However, the hand is the most important limb of mankind, which is irreplaceable in daily life. Therefore, regaining hand function is identified as the most urgent demand for people with paralyzed limbs. Unfortunately, the number of rehabilitation therapists is insufficient to meet the huge need in the rehabilitation market. The application of hand rehabilitation robot can solve this issue to some extent. Therefore, this paper mainly focuses on the system design and mirror-training of the self-designed hand rehabilitation robot, which mainly includes the mechanical structure design, kinematics modeling and workspace analysis, information perception system, control algorithm, and human-robot interaction interface (mirror training) of the exoskeleton robot. The main contents of the thesis are as follows. 
  1. A novel backpack-type portable hand rehabilitation robot is implemented in this thesis. There are two design methodologies for exoskeleton robots: the fixed-frame platform and the portable device. These fixed-frame platforms are precise and useful, while they are extremely heavy. Portable rehabilitation robots are portable, however, they often place the drive system on the patient's arm. In address this problem, this paper has developed a new backpack-type portable hand rehabilitation robot. This robot has nine degrees of freedom for the independent control of patient's fingers, which can be used to assist patients to do rehabilitation training such as the flexion and extension movements and the separation movements of fingers. In addition, this robot has a portable exoskeleton structure and uses the cable-driven approach to achieve the long-distance power transmission. The entire control system is placed in the backpack to reduce the weight on the patient's arm. The distinguished features of this robots are: portable, cable-driven, modularized structured, safe and lightweight. Compliance with the human hand anatomy is one premise for the hand rehabilitation robot. And the robot should not limit the finger's movement space. Therefore, it is necessary to analyze the robot's kinematics model. By the rehabilitation robot's kinematics model, the workspace can be calculated. The result shows that the proposed rehabilitation robot can meet the workspace requirement. Besides, an experiment is employed to verify that the proposed robot's mechanical structure can satisfy the rehabilitation requirement. 
  2. A new control method of "ADRC + ILC" is proposed for the effective control of the exoskeleton robot. Firstly, the type of the sensor is determined and the sensor is calibrated by neural networks. With the sensor information, the closed-loop control system can be realized. The parameter setting of the PID controller is very complicated and is depended on experiments. Furthermore, the PID controller cannot have a good performance on the disturbance rejection. Therefore, this thesis proposes a new control method, namely "ADRC + ILC", for trajectory tracking of robots. Finally, some experiments are used to demonstrate that the proposed "ADRC + ILC" method can track the trajectory well. 
  3. Human-robot mirror training based on surface electromyography (sEMG) is achieved. Because the passive rehabilitation control is difficult to stimulate the patient's enthusiasm and increase the patient's participation, this thesis realizes a human-robot mirror training based on sEMG. Firstly, the neural network is used to realize the gesture recognition based on sEMG. The classification accuracy of eight gestures in Fugl-Meyer scale can reach 97.71%. On this basis, the online gesture recognition based on sEMG is realized. The recognition result is used as a signal source to realize the effective control of the rehabilitation robot, so that the rehabilitation robot can follow the human's hand gesture to generate the corresponding action.
Document Type学位论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
陈妙. 手部外骨骼康复机器人的系统研制与镜像训练研究[D]. 北京. 中国科学院大学,2018.
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