四足机器人运动分析及控制算法研究

为实现一种液压驱动的四足机器人稳定行走,针对JQRI00四足机器人进行了步态规划,并推导出其运动控制算法。根据前期对四足机器人的结构设计,建立其运动学模型,得到各关节运动的逆运动学分析。根据JQRI00的驱动特点,对其运动控制算法进行研究。并利用ADAMS对机器人进行仿真。仿真及控制系统实验的结果准确合理,表明所研究的步态规划正确可行,控制算法实时有效,为机器人后期的复杂动作控制提供参考。     

A generalized control framework of assistive controllers and its application to lower limb exoskeletons

Various control methods have been studied for the natural assistance of human motions by exoskeletal robots, i.e., wearable robots for assisting the human motions. For example, impedance control and compliance control are widely used for controlling interaction forces between a human and a robot. When an accurate measurement of the human muscular force is available (e.g., electromyography), a direct use of the estimated human joint torque is possible in the control of an assistive robot. The human motions in a daily living, however, are so complex that they are constituted by multiple phases, such as walking, sitting, and standing, where the walking can be further categorized into multiple sub-phases. Therefore, a single control method cannot be the best option for all the motion phases; a switch in the control algorithms may be necessary for assisting human movements in multiple motion phases. In this paper, a generalized control framework is proposed to incorporate the various assistive control methods in one general controller structure, which consists of Feedforward Disturbance Compensation Control, Reference Tracking Feedback Control, Reference Tracking Feedforward Control, Model-based Torque Control. The proposed control framework is designed taking into consideration of the linearity of each control algorithm, and thus it enables the continuous and smooth switching of assistive control algorithms, and makes it possible to analyze the stability of the overall control loop. The proposed method is implemented into a lower-limb exoskeleton robot and is verified by experimental results.     

A SVM controller for the stable walking of biped robots based on small sample sizes

Conventional machine learning methods such as neural network (NN) uses empirical risk minimization (ERM) based on infinite samples, which is disadvantageous to the gait learning control based on small sample sizes for biped robots walking in unstructured, uncertain and dynamic environments. Aiming at the stable walking control problem in the dynamic environments for biped robots, this paper puts forward a method of gait control based on support vector machines (SVM), which provides a solution for the learning control issue based on small sample sizes. The SVM is equipped with a mixed kernel function for the gait learning. Using ankle trajectory and hip trajectory as inputs, and the corresponding trunk trajectory as outputs, the SVM is trained based on small sample sizes to learn the dynamic kinematics relationships between the legs and the trunk of the biped robots. Robustness of the gait control is enhanced, which is propitious to realize the stable biped walking, and the proposed method shows superior performance when compared to SVM with radial basis function (RBF) kernels and polynomial kernels, respectively. Simulation results demonstrate the superiority of the proposed methods.     

Female gait patterns: The influence of footwear

The two horizontal orthogonal ground reaction forces, mediolateral (Fx) and antero-posterior (Fy), together with limb segment displacements, were recorded simultaneously during gait from 24 female subjects aged 18–29 years. Recordings were made for each subject barefoot and when wearing each of four different pairs of shoes: soft-soled, rigid-soled, medium- and high-heeled. The results showed a trend for increasing lateral stability with increasing heel height and decreasing ground contact area. This.was reflected in decreasing mean values for defined ground reaction force peaks in the medio-lateral direction at heel strike and at toe off. Limb segments displacements tended to increase with decreasing heel height and increasing shoe-ground contact area, being greatest when subjects were wearing pumps, i.e. flexible-soled, no-heeled shoes.     

Automatic diagnosis of neuro-degenerative diseases using gait dynamics

Here an approach for the diagnosis of neuro-degenerative diseases based on gait dynamics is proposed. The proposed method uses information from a time series of stride intervals, swing intervals, stance intervals and double support intervals of stride-to-stride measures of footfall contact times using force-sensitive resistors. Different features were extracted from these time series and the best of them were selected for the diagnosis. The support vector machines using different kernels were examined for the diagnosis. The radial basis function kernel obtained the best performance for this aim. The results show that features derived from double support intervals are common effective features for the diagnosis of neuro-degenerative diseases using the gait dynamics.     

Falls in the healthy elderly: predisposing causes

One hundred healthy men and women aged 65-85 took part in a prospective study. They were clinically examined and underwent laboratory tests of gait, balance and reaction time at the start of an observation year.

All falls occurring in that year were analysed in detail. Three-quarters of them were trips or slips; however, ten personal factors were identified which, if present, increased the likelihood of a fall occurring. These were: disturbance of gait following a rest period accompanied by a lighting change (in persons aged 70 and over); an absent or abnormal plantar reflex; failure to wear prescribed spectacles; the presence of anxiety/depression, of a foot problem, or of two or more self-perceived limitations on mobility; a history of former wearing of high heels; a sustained drop in pulse pressure 5 min after cessation of a rest period; restricted neck movements; and the presence of an inverse Romberg ratio.

The number of circumstantial and personal factors contributing to the falls varied between three and twelve.     

Control of a robotic orthosis for gait rehabilitation

Robot assisted gait training may help in producing rapid improvements in functional gait parameters. This paper presents new experimental results with an intrinsically compliant robotic gait training orthosis. The newly developed robotic orthosis has 6 degrees of freedom (DOFs). A trajectory tracking controller based on the boundary layer augmented sliding control (BASMC) law was implemented to guide the subject’s limbs on physiological gait trajectories. The compliance of the robotic orthosis sagittal plane hip and knee joints was also controlled, independently of the trajectory tracking control. The robotic orthosis and the control scheme were evaluated on three neurologically intact subjects walking on a treadmill. A maximum trajectory tracking error of 10° was recorded at the hip and knee sagittal plane joints. The results showed that subjects can walk in the robotic orthosis with comfort and the BASMC law was able to guide the subject’s limbs on reference physiological trajectories.     

Effects of different loads and carrying systems on selected biomechanical parameters describing walking gait

The effects of two different systems on selected biomechanical parameters of walking gait, while carrying loads of varying magnitude, were investigated.

Ten healthy males who were not regularly engaged in carrying tasks walked a distance of 20 m for ten trials for each of the following five conditions: (i) normal walking without any external load; (ii) 20% and (iii) 40% body weight carried using a backpack system; and (iv) 20% and (v) 40% body weight carried using a doublepack system which distributed the load equally between the front and back of the subjects. The experimental set-up consisted of a Kistler force platform interfaced to a Tektronix 4051 Graphic Calculator, two super 8 mm movie cameras and a photoelectric timing system. Force data (417 Hz) were obtained for ten trials along with side- and rear-view film data (100 fps) for three of the trials for each of the subject conditions. In addition, selected aspects of foot-position data were acquired from a minimum of six footprints from one trial for each subject condition. Walking speed was controlled at 4·5 ± 0·3km/h. Parameters describing the temporal relationship of the gait pattern and values describing the spatial relationship of foot position were evaluated. Selected variables describing the components of the ground-reaction-force-time curves were also examined. Finally, selected kinematic and kinetic parameters were evaluated for four functional subphases of the support period.

Comparisons using a one-way ANOVA with repeated measures were conducted to examine differences between parameters describing the load-carrying conditions and normal gait. Results from the analysis revealed that both the light and heavy loads substantially modified the normal walking gait pattern. Interactions between the load conditions and carrying systems were tested using separate two-way ANOVA with repeated measures. Significant ordinal interactions as well as significant main effects were found between the two carrying systems for some parameters, suggesting that the doublepack system was more effective than the conventional backpack system, especially for carrying the heavy load.     

Gait recognition based on joint distribution of motion angles

Gait as a biometric trait has the ability to be recognized in remote monitoring. In this article, a method based on joint distribution of motion angles is proposed for gait recognition. The new feature of the motion angles of lower limbs is defined and extracted from either 2D video database or 3D motion capture database, and the corresponding angles of right leg and left leg are joined together to work out the joint distribution spectrums. Based on the joint distribution of these angles, we build a feature histogram individually. In the stage of distance measurement, three types of distance vector are defined and utilized to measure the similarity between the histograms, and then a classifier is built to implement the classification. Experiments has been carried out both on CASIA Gait Database and CMU motion capture database, which show that our method can achieve a good recognition performance.     

热释电红外传感器在生物特征识别领域中的研究进展

步态识别作为生物特征识别技术的一个新兴子领域,旨在根据人的行走姿势实现对个体身份的识别。人行走过程中身体各部分的红外热辐射信号具有某种个体特征,该特征信息能够被热释电红外传感嚣有效检测。通过分析传感器的输出信号,就可以提取出人体运动的特征性数据,实现对不同人、不同运动状态的识别。本文对热释电红外传感器应用于生物特征识别领域的研究进展进行论述,从传感器的探测原理、在步态识别中的应用以及热释电信息的特征提取与分类算法进行总结,并对当前该研究方向上亟待解决的问题作简要的分析。