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.     

下肢外骨骼机器人变步长步态规划方法

为了解决下肢外骨骼机器人连续步态规划问题,基于倒立摆模型提出了一种步态规划算法,并针对传统倒立摆模型无法变步长连续行走的问题提出了新的改进方法。将外骨骼机器人分成支撑腿和摆动腿两部分,分别采用D-H法进行运动学分析;利用倒立摆模型和固定函数法,进行等效质心与摆动腿末端轨迹规划;在相邻单脚支撑期之间插入双脚支撑期,使下肢外骨骼机器人在不断改变步行时,利用双脚支撑期进行位置和速度的切换,实现实时步态规划;将规划算法在SIMULINK中实现,并与ADAMS模型进行联合仿真,下肢外骨骼机器人在仿真环境下行走稳定,证明了算法的有效性。     

神经网络滑模控制下肢外骨骼机器人的轨迹跟踪

针对下肢外骨骼机器人行走稳定性与步态轨迹跟踪控制问题,对下肢外骨骼机器人三连杆模型进行动力学建模与轨迹仿真。通过拉格朗日法建立下肢外骨骼机器人的动力学模型,设计了神经网络自适应滑模控制算法。引入神经网络,对下肢外骨骼机器人步态轨迹跟踪系统的不确定项进行逼近,在控制器中采用了改进的趋近律,使用李雅普诺夫稳定性理论进行了稳定性分析,并通过MATLAB对改进后的控制算法进行了仿真验证。仿真结果表明,采用该算法对具有关节摩擦和外界环境干扰的下肢外骨骼机器人进行轨迹跟踪时,具有较好的跟踪效果;通过改进的趋近律,能削弱系统的抖振。相比于基于计算力矩法的滑模控制,该控制算法有更好的跟踪效果,能应用到下肢外骨骼机器人行走的稳定性和步态轨迹跟踪控制中。     

仿人机器人斜坡行走的步态规划

为了解决双足机器人在复杂路面的行走问题,提出了一种具体的基于几何约束的机器人斜坡行走步态规划方法。通过对机器人行走的起步阶段,单脚支撑期中摆动腿对机器人身体稳定的影响,以及行走步态流程进行了详细的规划,以机器人Nao为研究对象,构建出了机器人的连杆模型,计算出机器人在前向和侧向运动中保证身体稳定的踝关节约束范围,分析了流程图中机器人各行走步态,并计算出各步态中关节角度变化,从而规划出了机器人Nao从起步到结束行走的过程。运用三次多项式插值的方法使得各关节运动平滑稳定,并根据规划中的各个步态运动利用MATIAB仿真,获得机器人在步行过程中X、Y、Z方向上的质心轨迹,并通过x、y方向的轨迹可以看出机器人行走过程重心稳定,从而证明此方法用于机器人斜坡行走的可行性。     

理疗师交互下的下肢康复训练机器人个性化步态规划方法

针对偏瘫患者的个体差异及病况差异,提出了一种理疗师交互下的下肢康复训练机器人步态规划方法,在理疗师-减重悬吊式康复训练机器人-患者三者共存的复杂环境中,理疗师穿戴主控外骨骼直接行走实现步态时空参数规划,并融入理疗师的医学经验及对患者的评估.首先,基于旋量理论建立运动学模型,实现理疗师空间与机器人空间的运动映射;然后,统一规划机器人关节运动轨迹、减重机构重心调整轨迹及跑步机步速.最后,通过理疗师步态参数的实时采集、运动映射实验及机器人轨迹跟踪实验,验证了步态时空规划方法的有效性.结果表明,髋、膝关节规划角度在人体关节活动范围内,速度变化平稳,关节轨迹规划和重心调整规划均符合人体行走的生理特性.理疗师的参与实现了渐进康复训练中的个性化步态规划.     

一种下肢康复机器人步态的生成与调节方法

针对下肢康复外骨骼机器人康复训练参考步态的标准化设计,对平地行走、上楼梯和下楼梯等不同情境下的步态轨迹的设计问题进行研究,提出了由无线惯性传感器采集人体步态,利用基于关键点的步态生成与调节方法,得到了融合过渡过程的参考步态曲线.利用得到的参考步态曲线指导下肢康复外骨骼式机器人辅助人体在平地行走、上楼梯和下楼梯等情景下进...     

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.     

下肢外骨骼机器人动态建模与步态跟踪LQR控制

针对下肢外骨骼机器人步态轨迹与参考轨迹存在较大跟踪误差的问题,提出一种线性二次型调节器(LQR)控制方法来取代常规PD控制.根据牛顿-欧拉方程和系统参数辨识,建立人机一体化的下肢外骨骼机器人系统的动态模型,并进行LQR控制参数设计和稳定性分析.仿真和实验结果表明,基于LQR控制的下肢外骨骼机器人能很好地跟踪步态参考轨迹,误差比PD控制更小.上述建模方法和控制策略有效,为下肢外骨骼机器人的研究和设计提供参考.     

小型双足步行机器人的步态规划

为了解决双足步行机器人的步态控制,实现机器人稳定步行.为加强机器人的行走稳定性和优化步态过程,通过构造机器人行走过程中应满足的约束条件,规划机器人行走时的基本姿态及重心轨迹.根据规划的行走姿态及轨迹建立运动学方程,求解方程得到机器人各关节的运动轨迹.通过Matlab软件进行对运动轨迹模型的仿真,仿真得到的结果与设想的结果一致,证明步行得到平滑的关节轨迹是平稳的,并验证了方法的可行性.     

基于踝关节处人机位姿误差的外骨骼摆动腿控制

提出了一种外骨骼整机运动控制策略,并重点介绍了外骨骼摆动腿的运动规划与PD(比例-微分)控制.作为外骨骼摆动腿运动规划的依据,踝关节处人机位姿误差检测仅需要在腰部与踝足部进行人机绑定,降低了对穿戴者运动的限制与控制系统的复杂性.通过Adams与Matlab验证了作为外骨骼摆动腿PD控制器设计基础的外骨骼动力学模型的正确性.引入卡尔曼滤波器预测穿戴者摆动腿的运动可以在有限的采样频率下改善控制精度.实验结果表明,该方法能有效识别穿戴者摆动腿的运动意图并驱动外骨骼髋、膝关节实现人机踝关节轨迹的跟踪,可用于下肢外骨骼摆动腿的控制.     

基于图像处理的人体步态信息采集与处理

为了获取健康人体的正常步态信息, 提出了一种快捷有效的获取方法. 通过在下肢关节点处粘贴标记点, 利用摄像机获取正常人行走的图像, 对图像进行二值化处理, 提取出标记点坐标. 经过最小二乘拟合分析可得到人体脚心在一个步态周期内的运动轨迹及运动速度. 最后对下肢康复机器人进行步态规划, 得到下肢康复机器人的步态轨迹及其速度,并对不同年龄人群的步态速度曲线进行了分析. 实验结果表明, 该系统可行性好, 工作稳定, 为下肢康复机器人的运动学分析与控制提供有力的理论依据和验证方法.     

Multi-channel Fusion Based Adaptive Gait Trajectory Generation Using Wearable Sensors

This paper presents a method to regenerate lower limb joint angle trajectories during gait cycle by judging human intention using wearable sensor system. Myoelectric signals from user are used to detect the intention of gait initiation and gait phases. Multi-channel redundant fusion technique is implemented to obtain a robust stride time and gait phase calculation algorithm. Joint trajectories corresponding to particular gait events and phases are regenerated using a Radial basis neural network. The network is trained with joint angle data measured by Inertial Measurement Unit (IMU) from users with varying anthropomorphic features. Generated trajectory is adaptive to anthropomorphic as well as gait velocity variation. Contribution of this paper is in development of a wearable sensor system, multi-channel redundant fusion to calculate stride time and an adaptive gait trajectory generation algorithm. The proposed method of trajectory generation is used to regenerate lower limb joint motion in sagittal plane for wearable robotic devices like prosthesis and active lower limb exoskeleton.     

基于体感的仿人机器人步态学习与控制

针对现有理想化步态动力学模型规划方法复杂、人为指定参数过多、计算量大的问题,提出一种基于体感数据学习人体步态的仿人机器人步态生成方法。首先,用体感设备收集人体骨骼信息,基于最小二乘拟合方法建立人体关节局部坐标系;其次,搭建人体与机器人映射的运动学模型,根据两者间主要关节映射关系,生成机器人关节转角轨迹,实现机器人对人类行走姿态的学习;然后,基于零力矩点(ZMP)稳定性原则,对机器人脚踝关节转角采用梯度下降算法进行优化控制;最后,在步态稳定性分析上,提出使用安全系数来评价机器人行走稳定程度的方法。实验结果表明,步行过程中安全系数保持在0~0.85,期望为0.4825,ZMP接近于稳定区域中心,机器人实现了仿人姿态的稳定行走,证明了该方法的有效性。     

A real-time optimal energy-saving walking pattern generator based on gradient descent method and linear quadratic control

Many recent approaches have successfully generated a stable walking pattern for biped robots, but discussions about its optimization are relatively few. In this paper, a Center of Gravity (COG) trajectory optimization method is proposed to minimize the cost function of joint torque, joint limit, and joint speed limit. The linear quadratic control-based inverted pendulum controller optimizes the COG trajectories in sagittal and lateral directions with the COG height trajectory. The COG height trajectory is optimized by finding the derivative of the cost function with respect to the COG height offline. Then the proposed walking pattern generator builds the COG height trajectory database of different walking steps for online connection of a walking pattern. The walking pattern generator is verified by experiments and simulations of different step cycles with our humanoid robot, NINO, and it can clearly reduce the required joint torque of the robot while walking. In addition, compared with the fixed COG height trajectory, the energy consumption is reduced by 14% from the experimental results. Thus, the method succeeds in generating a more energy-saving walking pattern.     

Physical human-robot interaction estimation based control scheme for a hydraulically actuated exoskeleton designed for power amplification

We proposed a lower extremity exoskeleton for power amplification that perceives intended human motion via humanexoskeleton interaction signals measured by biomedical or mechanical sensors, and estimates human gait trajectories to implement corresponding actions quickly and accurately. In this study, torque sensors mounted on the exoskeleton links are proposed for obtaining physical human-robot interaction (pHRI) torque information directly. A Kalman smoother is adopted for eliminating noise and smoothing the signal data. Simultaneously, the mapping from the pHRI torque to the human gait trajectory is defined. The mapping is derived from the real-time state of the robotic exoskeleton during movement. The walking phase is identified by the threshold approach using ground reaction force. Based on phase identification, the human gait can be estimated by applying the proposed algorithm, and then the gait is regarded as the reference input for the controller. A proportional-integral-derivative control strategy is constructed to drive the robotic exoskeleton to follow the human gait trajectory. Experiments were performed on a human subject who walked on the floor at a natural speed wearing the robotic exoskeleton. Experimental results show the effectiveness of the proposed strategy.     

Gait Pattern Adaptation for an Active Lower-Limb Orthosis Based on Neural Networks

This work deals with neural network (NN)-based gait pattern adaptation algorithms for an active lower-limb orthosis. Stable trajectories with different walking speeds are generated during an optimization process considering the zero-moment point (ZMP) criterion and the inverse dynamic of the orthosis–patient model. Additionally, a set of NNs is used to decrease the time-consuming analytical computation of the model and ZMP. The first NN approximates the inverse dynamics including the ZMP computation, while the second NN works in the optimization procedure, giving an adapted desired trajectory according to orthosis–patient interaction. This trajectory adaptation is added directly to the trajectory generator, also reproduced by a set of NNs. With this strategy, it is possible to adapt the trajectory during the walking cycle in an on-line procedure, instead of changing the trajectory parameter after each step. The dynamic model of the actual exoskeleton, with interaction forces included, is used to generate simulation results. Also, an experimental test is performed with an active ankle–foot orthosis, where the dynamic variables of this joint are replaced in the simulator by actual values provided by the device. It is shown that the final adapted trajectory follows the patient intention of increasing the walking speed, so changing the gait pattern.     

Object carrying of hexapod robots with integrated mechanism of leg and arm

Hexapod robots are easy to realize walking in complicated environments and have the characteristic of high redundancy. It is well worth taking advantage of hexapod robots’ versatility, such as using legs to do manipulation or carry objects. In this paper, several methods are proposed to deal with issues of carrying objects by transforming one or two legs as arms while walking on other legs for hexapod robots. Firstly, practical gaits for one-legged carrying and two-legged carrying are presented respectively. Secondly, problems existing in gait planning, for instance how to estimate the mass of object and how to calculate joint motions according to desired center of gravity (COG) trajectory, are solved by dynamic analysis and a kinematic method based on COG Jacobian. Finally, the effectiveness of our methods to implement carrying objects is demonstrated by two successful experiments of carrying a bottle of water and a box.