截瘫助行外骨骼研究综述:从拟人设计依据到外骨骼研究现状

围绕截瘫助行外骨骼(WAEX)研究现状,结合人体运动机理研究成果,对助行外骨骼研究的局限性、人体下肢仿生基础研究进行了综述,在此基础上对实现平衡稳定、运动功能丰富、穿戴舒适的助行外骨骼提出了拟人设计与研究的建议与展望.首先,分析和总结了现有截瘫助行外骨骼在稳定性、功能性和舒适性等方面的局限性.其次,鉴于拟人设计对外骨骼创新研究的重要性,阐述了人体下肢的神经肌肉骨骼结构、信息感知和神经控制等对下肢运动与平衡的重要贡献.再次,总结了现有截瘫助行外骨骼在结构、驱动、感知和控制等方面的典型设计方案.最后,基于现有助行外骨骼及其期望目标之间的差距,对影响和改善助行外骨骼稳定性、功能性和舒适性的关键技术与研究趋势进行了总结与展望.     

下肢负重外骨骼机器人的初步设计

研究设计了一种能够增强人体负重的下肢外骨骼机器人,该负重外骨骼机器人具有8个自由度,可实现髋关节的外展与内收、屈/伸运动;膝关节的屈/伸运动以及踝关节的弯曲运动;根据人体步态分析研究出各个关节的运动角度范围,结合目标负重进行结构优化设计;对机器人的结构进行简化,建立了外骨骼机器人的连杆模型,根据其几何关系,采用D-H准则对外骨骼机器人进行了数学建模;以计算机、六轴运动控制卡和STM32为核心构建了控制系统,结合ZMP (zero moment point)零力矩点稳定性判据及三次样条插值进行了步态规划,并将此步态规划应用于样机上;样机实验结果表明,此结构能够满足不同体型的人进行穿戴,并能够根据规划的步态轻松行走,验证了其结构和控制系统的合理性.     

基于人机信息交互的助行外骨骼机器人技术进展

外骨骼机器人是集人体信息检测、机器人自动控制、神经工程等多学科知识于一身的高科技成果.本文简要介绍了外骨骼机器人研发技术现状和应用市场前景,分别从外骨骼动力驱动和运动测量技术角度剖析了支撑典型外骨骼机器人实现其运动辅助功能的主要技术基础,重点从神经信息交互角度出发,讨论了构建人机信息交互环路中的技术瓶颈,以及如何更为高效准确地获取人体运动意图.最后展望了其未来技术研发方向.     

基于SMA驱动模块的腕部柔性外骨骼设计

针对外骨骼机器人在仿生性、便携性及人机交互性上的问题,提出了一种基于形状记忆合金(SMA)驱动模块的腕部柔性外骨骼.首先,研究SMA弹簧的最大可回复拉伸长度与相变温度特性,建立SMA驱动模块的SMA弹簧与锰钢片的混合模型.然后,根据人体腕关节运动范围和所需拉力,对该混合模型进行参数优化.最后,提出了一种弹性元件势能、风冷和高低电流切换混合的SMA弹簧冷却方法,设计了基于运动角度和相变温度反馈的控制方法.为了验证腕部柔性外骨骼的实际使用效果,进行穿戴训练和测温实验.实验结果表明,该外骨骼的仿生性和柔顺性符合人体腕关节运动特性,其轻质、便携性可提高日常生活活动辅助效果.     

人体下肢外骨骼机器人的发展及关键技术分析

下肢外骨骼机器人具有增强人体机能和辅助下肢康复训练的功能,在军事和医疗领域具有很好的发展前景,逐渐成为机器人领域的研究热点。本文分别介绍了增强人体机能的下肢外骨骼机器人和医疗下肢外骨骼机器人的国内外最新研究现状,概括并分析了与之相关的关键技术,探讨了下肢外骨骼机器人的今后发展方向。     

偏瘫患者辅助下肢外骨骼设计与仿真分析

针对偏瘫患者外骨骼康复机器人降低外骨骼质量的要求,设计了一种辅助下肢外骨骼机器人,采用柔索驱动的膝关节,具有结构简单,质量轻的特点。同时利用ADAMS建立了外骨骼关节柔索驱动的动力学模型,绳索模块建立了柔索驱动模型,通过Ariel生物运动分析软件,采集髋、膝、踝关节运动数据,运用Spline函数进行了仿真分析。经过仿真分析柔索驱动在上台阶运动过程中的不同拉簧预紧力和拉簧刚度下传动特性和驱动力矩,为进一步研究设计下肢外骨骼提供依据。     

基于支持向量机的外骨骼机器人灵敏度放大控制

为了准确控制外骨骼机器人跟随人体运动,需要建立其动态、精确的数学模型;人体下肢外骨骼是一个多自由度、强耦合以及非线性的多连杆系统,难以建立准确的运动学和动力学模型;文章使用三维运动捕捉与空间定位系统,获取实际人体运动参数(运动学与动力学),应用支持向量机(SVM)学习人体下肢外骨骼的数学模型;基于该模型构造基于支持向量机模型的灵敏度放大控制方法;文章使用MATLAB和LIBSVM建立外骨骼下肢机器人的数学模型,并进行仿真分析;仿真结果表明基于SVM的模型学习方法,能够准确计算出人体下肢外骨骼的动力学模型,并简化建模过程;基于SVM的灵敏度放大控制,能够有效计算出人体下肢外骨骼各关节(髋关节、膝关节、踝关节)的输出力矩,并控制外骨骼机器人跟随人体运动。     

下肢外骨骼机器人控制的脑电感知方法研究

将外骨骼机器人技术与BCI系统结合起来,使人体具有了外骨骼机器人的一系列优良特性,同时使外骨骼机器人具备了人体的智能;首先,对外骨骼机器人技术与BCI技术的融合进行了可行性分析,说明了该方法的可行性;其次,通过实验采集了6种想象运动的脑电信号,选取了C3、C4通道的脑电信号,并对其进行了去噪处理;然后,对经过预处理的六种想象运动的脑电信号通过小波变换进行了分解,提取了包括小波分解系数和能量系数的脑电信号小波特征;最后,针对所提取的小波特征,采用了最小二乘支持向量机对这6种想象运动模式进行分类处理。     

一种下肢外骨骼机器人随动控制算法的研究

随动控制算法对下肢外骨骼机器人的助力效果起着决定性的作用。根据零力控制算法及下肢外骨骼机器人的运动特点,提出了一种末端随动控制算法,并给出了算法推导过程。最后通过Matlab仿真验证了该算法的有效性。     

基于实时意图识别的外骨骼机器人步态切换

根据外骨骼穿戴者的实时运动意图,切换外骨骼机器人的行走步态,是外骨骼机器人研究热点问题之一.针对外骨骼机器人能够根据穿戴者的运动意图控制步态切换问题,提出采集人体表面肌电信号进行解码,通过BP神经网络识别穿戴者的运动意图,实现外骨骼进行步态规划,利用关节角度与足底压力的相关性以及重心轨迹,对所规划的步态的稳定性进行分析,依据不同的运动意图完成步态进行实时切换,验证了基于实时意图识别控制的外骨骼步态切换的可行性.     

Minimizing human-exoskeleton interaction force by using global fast sliding mode control

A critical issue in the model-based control of performance-augmenting exoskeleton systems is the unknown nonlinear dynamic properties of the systems or the uncertainties. An improper estimation of the system dynamics can cause instabilities in the system and generate considerable human-exoskeleton interaction forces during human motions. Thus, the controller of such exoskeleton systems needs to add robustness to stabilize it against the uncertainties. In this paper, we propose a global fast sliding mode control algorithm integrated in a hybrid controller for each exoskeleton leg to minimize human-exoskeleton interaction forces. By doing so, the proposed algorithm does not require an exact estimation of the dynamic properties of the exoskeleton system, but still minimizes the physical human-exoskeleton interaction (pHEI) forces. Finally, the performance of the proposed algorithm is verified by experiments on our lower exoskeleton system, which is used for human power augmentation and called “PRMI” exoskeleton. Our experimental results show that the proposed control algorithm provides a good control quality for the PRMI exoskeleton. The PRMI exoskeleton can support a wearer carrying heavy load while tracking the rapid movements of the wearer without obstructing them.     

Minimizing Human-Exoskeleton Interaction Force Using Compensation for Dynamic Uncertainty Error with Adaptive RBF Network

A critical issue in the control of exoskeleton systems is unknown nonlinear dynamic properties of the system. The improper estimation of those unknown properties can cause considerable human-exoskeleton interaction force during human’s movements. It is really challenging to exactly estimate the parameters of dynamic models. In this paper, we propose a novel exoskeleton control algorithm to both compensate for the dynamic uncertainty error and minimize the human-exoskeleton interaction force. We have built a virtual torque controller based on dynamic models of a lower exoskeleton and have used an approximation of a Radial Basis Function (RBF) neural network to compensate for the dynamic uncertainty error. By doing so, we avoid using complicated force sensors installed on the human-exoskeleton interface and minimize the physical Human-Robot Interaction (pHRI) force. Moreover, we introduce the prototype of our exoskeleton system, called ‘PRMI’ exoskeleton system. Finally, we validated the proposed algorithm on this system, and the experimental results show that the proposed control algorithm provides a good control quality for the ‘PRMI’ exoskeleton system by compensating for dynamic uncertainty error.     

一种新的康复与代步外骨骼机器人研究

针对老年人及下肢障碍者康复训练与代步问题,本文提出一种新的康复与代步外骨骼机器人.本文首先详细介绍了机器人各部分组成及机构设计方案,通过下肢外骨骼与轮椅的有机结合,有效保持或恢复老年人、脑卒中患者下肢运动能力的同时,为患者提供一种方便的代步工具;运用脚蹬车运动制订康复训练策略,可保持下肢康复训练轨迹固定,保证患者安全;提出主从式操作方法及多模态康复训练控制流程提高使用者参与感.最后通过仿真与实验验证了所提康复系统的可行性与设计的正确性.     

Human hip joint center analysis for biomechanical design of a hip joint exoskeleton

We propose a new method for the customized design of hip exoskeletons based on the optimization of the humanmachine physical interface to improve user comfort. The approach is based on mechanisms designed to follow the natural trajectories of the human hip as the flexion angle varies during motion. The motions of the hip joint center with variation of the flexion angle were measured and the resulting trajectory was modeled. An exoskeleton mechanism capable to follow the hip center’s movement was designed to cover the full motion ranges of flexion and abduction angles, and was adopted in a lower extremity assistive exoskeleton. The resulting design can reduce human-machine interaction forces by 24.1% and 76.0% during hip flexion and abduction, respectively, leading to a more ergonomic and comfortable-to-wear exoskeleton system. The human-exoskeleton model was analyzed to further validate the decrease of the hip joint internal force during hip joint flexion or abduction by applying the resulting design.     

Simultaneous bipedal locomotion based on haptics for teleoperation

ABSTRACT

This paper proposes a novel, simultaneous bipedal locomotion method using haptics for remote operation of biped robots. In general, traditional biped walking methods require very high computational power and advanced controllers to perform the required task. However, in this proposed method, a master exoskeleton attached to the human’s lower body is used to obtain the trajectory and haptic information to generate the trajectory of the slave biped robot in real time. Lateral motion of the center of mass of the biped is constrained in this experiment. Also, it is considered that no communication delay is presented in between the two systems in this experiment, and they are not discussed in this paper. Since a direct motion transmission is used in the proposed method, this method is quite straight forward and a simultaneous walking can be realized at the same time with high performance. Also, it does not require an exact dynamic model of the biped or specific method to plan the trajectory. The gait pattern of the biped is directly determined by that of the human. Also, the operator can feel the remote environment through the exoskeleton robot. Results obtained from the experiments validate the proposed method.     

Model Identification and Human-robot Coupling Control of Lower Limb Exoskeleton with Biogeography-based Learning Particle Swarm Optimization

International Journal of Control, Automation and Systems - Lower limb exoskeleton is a typical wearable robot to assist human motion and improve physiological power. However, the control...     

下肢外骨骼康复机器人人因工程研究进展

下肢外骨骼康复机器人是专门针对下肢运动障碍及行动不便者,提供康复锻炼的智能化装备, 其发展对于中国康复事业具有重大的理论意义与实际应用价值。人因工程研究可有效提高下肢康复机器人 HRI 人机交互安全性与交互效率。因此,深入探讨国内外下肢外骨骼康复机器人人因工程研究进展,对其研究现状、 发展趋势进行总结归纳。通过文献检索方法对相关文献进行分析,发现人机交互、步态行为、结构设计、安全 问题等是下肢外骨骼康复机器人人因工程领域的研究热点,并从人因出发,系统梳理国内外下肢外骨骼康复机 器人人因工程研究进展和发展动态,提出我国在下肢外骨骼康复机器人人因工程领域所面临的挑战,研究重点 和未来发展趋势。     

Robo-Erectus: a low-cost autonomous humanoid soccer robot

An exoskeleton robot can replace the wearer's motion function by operating the human's body. The purpose of this study is to propose a power assist method of walking, standing up and going up stairs based on autonomous motion of the exoskeleton robot suit, HAL (Hybrid assistive Limb), and verify the effectiveness of this method by experiment. In order to realize power assist of tasks (walking, standing up and going up stairs) autonomically, we used the Phase Sequence control which generates a task by transiting some simple basic motions called Phases. A task was divided into some Phases on the basis of the task performed by a normal person. The joint moving modes were categorized into active, passive and free modes according to the characteristic of the muscle force conditions. The autonomous motions which HAL generates in each Phase were designed corresponding to one of the categorized modes. The power assist experiments were performed by using the autonomous motion with a focus on the active mode. The experimental results showed that the wearer's muscle activation levels in each Phase were significantly reduced. With this, we confirmed the effectiveness of the proposed assist method.     

基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法

设计了一种基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法.在机器人与人体上肢接触面安装力传感器采集人机交互力矩信息作为量化的主动运动意图,设计了一种无模型自适应滤波算法使交互力矩变得平滑而连贯;以人机交互力矩为输入,综合考虑机器人末端点与参考轨迹的相对位置和补偿力的信息,设计了人机交互阻抗控制器,用于调节各关节的给定目标速度;设计了将无模型自适应与离散滑模趋近律相结合的速度控制器完成机器人各关节对目标速度的跟踪.仿真结果表明,该控制方法可以实现外骨骼式上肢康复机器人辅助患者完成主动交互训练的功能.通过调节人机交互阻抗控制器的相应参数,机器人可以按照患者的运动意图完成不同的主动交互训练任务,并在运动出现偏差时予以矫正.控制器在设计实现过程中不要求复杂准确的动力学建模和参数识别,并有一定的抗干扰性和通用性.