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...     

Learning from hint for the conservative motion of the constrained industrial redundant manipulators

A neural network (NN)-based kinematic inversion of industrial redundant arms is developed in this paper to conserve the joint configuration in cyclic trajectories. In the developed approach, the Widrow–Hoff NN with an online adaptive learning algorithm derived by applying Lyapunov approach is introduced. Since this kinematic inversion has an infinite number of joint angle vectors, a fuzzy neural network system is designed to provide an approximate value for that vector. Feeding this vector as an additional hint input vector to the NN limits and guides the output of the NN within the self-motion of the manipulator. The derivation of the candidate Lyapunov function, which is designed to achieve the joint configurations conservation in addition to the joint limits avoidance, leads to a computationally efficient online learning algorithm of the NN. Simulations are conducted for the PA-10 redundant manipulator to bear out the efficacy of the developed approach for tracking closed trajectories.     

具有2DOF铰接式躯干的仿猎豹四足奔跑机器人

为了探索脊柱运动对腿运动的增强机理,设计了具有2自由度铰接式躯干的仿猎豹四足奔跑机器人。对带腾空相的跳跃（bound）步态奔跑运动的力学过程进行描述,采用阻尼型弹性负载倒立摆（D-SLIP）模型建立了四足机器人动力学模型。依据猎豹的奔跑运动模式,对四足机器人脊柱关节与腿关节的耦合运动进行了轨迹规划。提出一种改进的粒子群优化（PSO）算法,解决了机器人脊柱关节驱动机构尺寸和运动轨迹控制参数之间目标互斥的嵌套优化问题。对四足机器人跳跃奔跑运动进行动力学仿真,结果表明：脊柱与腿的协调运动可以增大奔跑步幅,使机器人产生腾空相,从而提高机器人的奔跑速度。     

Walking algorithm for a robotic transfemoral prosthesis capable of walking pattern recognition and posture stabilization

This paper describes a walking pattern generation algorithm for a robotic transfemoral prosthesis that is synchronized with the walking motion of a transfemoral amputee, and posture stabilization for ground adaptation and maintaining balance on inclined grounds. The developed robotic transfemoral prosthesis in this study has a knee joint and ankle roll/pitch joints for walking on complex slopes. The walking motion data obtained from the motion capture system are used as the standard walking pattern data to accurately imitate the inherent gait of the wearer. Walking intention, percent of gait cycle (PGC), and walking stride are predicted through two inertial sensors attached at both thighs, and the joint angles of the robotic transfemoral prosthesis are then generated in real-time from the PGC and the standard walking pattern data. Additionally, variable impedance control and zero moment point (ZMP) control are carried out with a force/torque sensor for posture stabilization against variable ground slopes, and ground slope compensation and disturbance rejection are also done with the use of inertial sensors at the foot and shank. Consequently, the performance of the walking pattern generation algorithm and posture stabilization control was verified through walking experiments of an author on an inclined treadmill.     

一种人体步态轨迹测量方法

为研究人体的步态运动规律,采用基于拉线传感器检测系统和人体关节角度测量装置,对人在不同路况下行走时踝关节运动轨迹和下肢关节角度进行了测量,根据人体下肢刚体模型和数据融合方法对测量结果进行了分析和处理,得到了人在上楼梯、平地行走和下楼梯时的步态轨迹和3种路况下的支撑相;上楼梯时的支撑相最大,平地行走次之,下楼梯最小.测量结果为了解人体运动规律和康复治疗提供了依据.     

使用运动监视传感网络对行走过程中腿部屈伸角的跟踪

An accelerometry-based gait analysis approach via the platform of sensor network is reported in this paper. The hardware units of the sensor network are wearable accelerometers that are attached at the limbs of human body. For the specific task of gait analysis, flexion angles of the thighs during gait cycles are computed. A Kalman filter is designed to estimate the flexion-extension angle, angular velocity of the thigh using the output of the wearable accelerometers. The proposed approach has been applied to four subjects and the performance is compared with videobased approach. Comparative results indicate that with the proposed Kalman filter, the sensor network is able to track the movement of the thighs during gait cycles with good accuracy and simultaneously detect major gait event of foot contact from the waveform of the angular velocity.     

Bionic control of exoskeleton robot based on motion intention for rehabilitation training

As the complexity of obtaining irregular daily motion trajectory during upper limb rehabilitation training, this paper proposes the bionic control method for the presented exoskeleton robot arm based on motion intention. Firstly, the collected motion signal is pre-processing by filtering. Then the motion intention and motion mode of the processed signal are classified by using the hierarchical multi-classification support vector machine. Meanwhile, the adaptive Hopf oscillator network based on dynamic learning is used for offline learning of joint motion trajectory, and the parameters of the reproduced signal in different motion modes are obtained. Finally, the corresponding parameters are transferred according to the user’s intention, and the oscillator network is reconstructed to realize the periodic motion control of rehabilitation training. With experimental verification, the proposed method can follow the human body’s motion intention and reproduce the daily motion trajectory of the upper limb. The results show it can be used to conduct rehabilitation training for the patient.     

基于径向基函数神经网络的红外步态识别

为提高红外步态识别的效果,提出一种基于径向基函数神经网络的多分类器融合算法。对红外步态序列,分别应用基于轮廓线傅立叶描述子特征的模糊分类器和基于下肢关节角度特征的贝叶斯分类器进行识别,再利用径向基函数神经网络的学习和分类功能,对获得的输出信息进行度量层的融合和再识别。仿真实验结果表明,该算法获得更加精确的分类效果。     

An online collision-free trajectory generation algorithm for human–robot collaboration

The premise of human–robot collaboration is that robots have adaptive trajectory planning strategies in hybrid work cell. The aim of this paper is to propose a new online collision avoidance trajectory planning algorithm for moderate dynamic environments to insure human safety when sharing collaborative tasks. The algorithm contains two parts: trajectory generation and local optimization. Firstly, based on empirical Dirichlet Process Gaussian Mixture Model (DPGMM) distribution learning, a neural network trajectory planner called Collaborative Waypoint Planning network (CWP-net) is proposed to generate all key waypoints required for dynamic obstacle avoidance in joint space according to environmental inputs. These points are used to generate quintic spline smooth motion trajectories with velocity and acceleration constraints. Secondly, we present an improved Stochastic Trajectory Optimization for Motion Planning (STOMP) algorithm which locally optimizes the generated trajectories of CWP-net by constraining the trajectory optimization range and direction through the DPGMM model. Simulations and real experiments from an industrial use case of human–robot collaboration in the field of aircraft assembly testing show that the proposed algorithm can smoothly adjust the nominal path online and effectively avoid collisions during the collaboration.     

Self-Learning for a Humanoid Robotic Ping-Pong Player

Imitating the learning process of a human playing ping-pong is extremely complex. This work proposes a suitable learning strategy. First, an inverse kinematics solution is presented to obtain the smooth joint angles of a redundant anthropomorphic robot arm in order to imitate the paddle motion of a human ping-pong player. As humans instinctively determine which posture is suitable for striking a ball, this work proposes two novel processes: (i) estimating ball states and predicting trajectory using a fuzzy adaptive resonance theory network, and (ii) self-learning the behavior for each strike using a self-organizing map-based reinforcement learning network that imitates human learning behavior. Experimental results demonstrate that the proposed algorithms work effectively when applied to an actual humanoid robot playing ping-pong.     

基于足底压力和关节运动信息的步态检测系统设计

作为老年人高发疾病,脑卒中有着较高的几率造成患者行动障碍。及时检测、获取偏瘫患者行走时步态信息,是治疗师为患者制定康复计划的重要环节。目前,较新的步态检测系统大多采用三维分析,三维步态分析系统体积较大、成本高、操作不便,在患者主动康复训练中存在较大的局限。针对现今步态检测系统的局限,设计了一种结合无线传输技术,检测患者关节运动角度及足底压力的运动信息检测系统。系统采用薄膜压力传感器、惯性传感器分别采集足底压力及关节运动信息,实现了对患者的8路足底压力信息及下肢关节运动角度信息的快速、有效采集。实验表明,系统可以有效采集关节运动角度及足底压力信息,在未来的康复训练中有着很好的应用前景。     

双足机器人跨越动态障碍物在线控制系统设计

在双足机器人跨越动态障碍物的在线控制问题中,脚步规划和步态控制的学习时间是关键问题;提出了一种将机器人的步态控制和脚步规划分别独立设计的控制策略;步态控制目的是产生关节点轨迹并控制对理想轨迹的跟踪,考虑到双足机器人关节点轨迹的不连续性,应用小脑模型连接控制CMAC记忆特征步态的关节点轨迹;脚步规划的控制目标是通过对环境的视觉感知预测机器人的运动路径,算法是基于无需对动态环境精确建模的模糊Q学习算法;仿真结果表明该控制策略的可行性,并且可以有效缩短在线学习时间。     

下肢康复机器人的自适应人机交互控制策略

针对康复机器人运动过程中的人机交互性问题,提出一种下肢康复机器人自适应人机交互控制策略.提取伸屈运动中下肢表面肌电信号（Surface electromyography,sEMG）和足底压力特征,分别用于表征下肢运动意图和人机交互力（Interaction force,IF）信息,建立基于sEMG-IF的人机交互信息融合模型,实现下肢康复机器人运动轨迹的在线规划;考虑主动康复运动过程中的人机交互作用,建立具有时变动态特性的人机系统动力学模型,设计间接模糊自适应控制器对期望轨迹进行跟踪控制,实现下肢康复机器人自适应人机交互控制.通过对5名被试者进行下肢康复机器人运动控制实验研究,验证所提方法的可行性和有效性.     

Neural computing for walking gait pattern identification based on multi-sensor data fusion of lower limb muscles

The use of neural computing for gait analysis widely known as computational intelligent gait analysis is addressed recently. This research work reports multilayer feed-forward neural networks for walking gait pattern identification using multi-sensor data fusion; electromyography (EMG) signals and soft tissue deformation analysis using successive frames of video sequence extracted from lower limb muscles according to each gait phase within the considered gait cycle. Neural computing framework for walking gait pattern identification consists of system hardware and intelligent system software. System hardware comprises a wireless surface EMG sensor unit and two video cameras for measuring the neuromuscular activity of lower limb muscles, and a custom-developed artificial neural network for classifying the gait patterns of subjects during walking. The system uses root mean square and soft tissue deformation parameter as the input features. Multilayer feed-forward back propagation neural networks (FFBPNNs) with different network training functions were designed, and their classification results were compared. The intelligent gait analysis system validation has been carried out for a group of healthy and injured subjects. The results demonstrated that the overall accuracy of 98 % prediction is achieved for gait patterns classification established by multi-sensor data fusion of lower limb muscles using FFBPNN with Levenberg–Marquardt training function resulting better performance over FFBPNN with other training functions.

     

一种基于非线性振荡器的步态轨迹自适应算法

步态训练轨迹是影响康复训练效果的一项重要因素,而自适应性对于下肢康复机器人的临床应用具有重要的意义.振荡器可通过在线调节参数而输出不同波形的周期信号,常用于康复机器人步态轨迹的生成.本文在高斯核函数非线性振荡器的基础上提出了一种下肢康复机器人步态轨迹自适应算法.该算法通过轨迹偏差实现对参考轨迹波形的调节,并且用相位偏差曲线面积实现参考轨迹周期的自适应.本文首先介绍了用于生成步态参考轨迹的非线性振荡器的数学模型;其次,详细描述了基于该模型的参考轨迹波形和周期自适应算法;最后,以悬挂减重式下肢康复机器人为研究对象,建立机器人与人体下肢仿真模型,对所提出的步态参考轨迹自适应算法进行仿真实验,并验证了该算法的可行性.     

Kinematic control of a redundant manipulator using an inverse-forward adaptive scheme with a KSOM based hint generator

This paper proposes an online inverse-forward adaptive scheme with a KSOM based hint generator for solving the inverse kinematic problem of a redundant manipulator. In this approach, a feed-forward network such as a radial basis function (RBF) network is used to learn the forward kinematic map of the redundant manipulator. This network is inverted using an inverse-forward adaptive scheme until the network inversion solution guides the manipulator end-effector to reach a given target position with a specified accuracy. The positioning accuracy, attainable by a conventional network inversion scheme, depends on the approximation error present in the forward model. But, an accurate forward map would require a very large size of training data as well as network architecture. The proposed inverse-forward adaptive scheme effectively approximates the forward map around the joint angle vector provided by a hint generator. Thus the inverse kinematic solution obtained using the network inversion approach can take the end-effector to the target position within any arbitrary accuracy.In order to satisfy the joint angle constraints, it is necessary to provide the network inversion algorithm with an initial hint for the joint angle vector. Since a redundant manipulator can reach a given target end-effector position through several joint angle vectors, it is desirable that the hint generator is capable of providing multiple hints. This problem has been addressed by using a Kohonen self organizing map based sub-clustering (KSOM-SC) network architecture. The redundancy resolution process involves selecting a suitable joint angle configuration based on different task related criteria.The simulations and experiments are carried out on a 7 DOF PowerCube? manipulator. It is shown that one can obtain a positioning accuracy of 1 mm without violating joint angle constraints even when the forward approximation error is as large as 4 cm. An obstacle avoidance problem has also been solved to demonstrate the redundancy resolution process with the proposed scheme.     

Multi-body sensor data fusion to evaluate the hippotherapy for motor ability improvement in children with cerebral palsy

Hippotherapy is a new rehabilitation therapy for children with cerebral palsy (CP). Although it has been proved to be effective in clinical research, a quantitative evaluation of such results is still lacking in previous studies. In this research, one method for evaluating the effectiveness of hippotherapy based on body sensor network (BSN) is proposed. The method adopts distributed magnetic, angular rate, and gravity (MARG) sensors to evaluate the gross motor function of CP children by multi-sensor data fusion algorithm, the comparison results with the golden standard optical capture system demonstrate the robustness of sensor fusion algorithm. Moreover, via tracking one-year’s hippotherapy projects, a pilot study was conducted for measuring and evaluating the motor coordination function and gait parameters of CP children. Finally, the rehabilitation of gross motor function for the chosen subjects under different treatment periods was evaluated through kinematic analysis. The results of our method show that some children’s symptoms, such as limb stiffness, poor joint range of motion, scissors gait, knee flexion gait, have been relieved. Thus, it provides an empirical basis for hippotherapy in the rehabilitation of motor function corresponding to CP children.     

Implementation of omnidirectional crawl for a quadruped robot

As a reptile animal crawls in a cluttered environment, so a quadruped robot should be able to crawl on an irregular ground profile with its static stability by adopting the straightgoing and standstill-turning free gaits. The generalized and explicit formulations for the automatic generation of straight-going gaits and various standstill-turning gaits are presented in this paper. The maximized stride for the straight-going gait and the maximum turning angle for the turning gait of a quadruped robot named TITAN-VIII in a gait cycle are discussed by considering the robot's mechanism constraints and the irregularities of the ground profile. The control algorithm, including control of the joint positions of the robot, is described to implement the desired walking path of the quadruped robot. The effectiveness of the proposed method is demonstrated through experimental result.     

动态数据融合算法改进仿真研究

为了有效滤除传感器网络动态数据携带的冗余信息,提高网络数据准确性与网络节点生命周期,提出了基于时间间隔与数据间隔双重增量的自适应加权动态数据融合算法。获取一段时间内的数据,结合数据间隔得出基准数据,利用其它数据与基准数据的偏差进行数据融合处理,有利于减少网络的负载压力;并根据递推估计将同一类型数据采取多次融合计算,引入自适应理论,利用相对方差对各个数据加权做相应调节,同时对数据修正后的估计权值做融合处理,得到最终的二次加权融合结果,进而提高动态数据融合的精度。通过仿真结果,验证了双重增量自适应加权算法在网络动态数据融合方面的有效性,显著降低了数据冗余程度,提高了数据准确性。     

一种无线传感器网络的混合路由算法

针对无线传感器网络具有严格的能量限制和有效能量的分散、过量的低比率数据和多节点到单一节点的数据流等特点,提出一种适合于无线传感器网络的混合路由算法(HRA).该算法综合应用以节点为中心路由方式的负载平衡算法、位置为中心路由方式的轨道路由和数据为中心的数据融合等,可改善无线传感器网络在路由、发现和查询方面等通信的性能.经性能分析,该算法能很好地增长网络生命期、减少数据传输量、降低能耗.