欠驱动两足步行机器人研究现状

针对欠驱动两足步行机器人的研究现状与发展趋势进行了探讨。首先,总结了被动行走和踝关节欠驱动两足机器人的研究现状,介绍了欠驱动两足步行机器人的基本研究方法,包括问题描述、步态规划、运动控制和稳定性判定等,并对欠驱动两足机器人需要进一步研究的问题和发展方向进行深入研究,最终目标是将欠驱动控制策略应用于两足步行机器人的行走过程控制,以提高其运动性能。     

点接触式两足步行机器人直立姿态稳定控制

针对点接触式两足步行机器人提出了一种直立姿态稳定控制策略。建立了机器人动力学模型,对机器人直立姿态的稳定性和可控性进行分析;并基于T-S模型建模,构造机器人全局模糊模型,基于LQR最优控制理论,设计全局渐近稳定的模糊控制器对机器人直立姿态进行稳定控制。仿真结果表明,该算法具有较宽的稳定范围,可以使机器人在上电以后和从行走切换到站立状态时迅速恢复直立姿态,保持稳定。     

智能假肢感知和约束变增益迭代学习控制

针对膝关节输入力矩为受限的阻尼力矩,步态跟随控制缺乏在线调节问题,提出用离线和在线集成的变增益一阶D型闭环迭代学习方法进行步态跟随控制。建立系统动力学模型,给出基于有限状态机的步态感知和人腿摆动相步速间接感知方法,减小跟随滞后。设计在线变步长指数增益调节算子,并针对输入力矩受限,设计离线的带惩罚因子的指数增益调节算子。利用谱半径约束条件,分析算法的收敛性。在真人比例虚拟样机模型上进行控制仿真实验,给出了固定增益和变增益调节下轨迹跟踪曲线。结果证明,提出的增益调节算子可保证输入在有效范围内,并可提高跟踪速度。     

一种简化的人体步行神经网络模型

本文针对复杂的人体步行的神经 -肌肉 -骨胳系统模型 ,分析了步行中的主要关系 ,对神经网络模型作了较大的简化和修改 ,提出了一个简单的神经网络模型 .该模型在步行状态、神经耦合、感觉信号反馈等方面都作了简化 .计算机仿真表明 ,该模型能产生稳定的步行运动     

Stochastic distribution of the required coefficient of friction for level walking--an in-depth study

This study investigated the stochastic distribution of the required coefficient of friction (RCOF) which is a critical element for estimating slip probability. Fifty participants walked under four walking conditions. The results of the Kolmogorov-Smirnov two-sample test indicate that 76% of the RCOF data showed a difference in distribution between both feet for the same participant under each walking condition; the data from both feet were kept separate. The results of the Kolmogorov-Smirnov goodness-of-fit test indicate that most of the distribution of the RCOF appears to have a good match with the normal (85.5%), log-normal (84.5%) and Weibull distributions (81.5%). However, approximately 7.75% of the cases did not have a match with any of these distributions. It is reasonable to use the normal distribution for representation of the RCOF distribution due to its simplicity and familiarity, but each foot had a different distribution from the other foot in 76% of cases. PRACTITIONER SUMMARY: The stochastic distribution of the required coefficient of friction (RCOF) was investigated for use in a statistical model to improve the estimate of slip probability in risk assessment. The results indicate that 85.5% of the distribution of the RCOF appears to have a good match with the normal distribution.     

ITS智能车辆横向运动模式空间构造算法研究

1． 山东大学控制科学与工程学院，山东济南250061；2． 山东理工大学计算机科学与技术学院，山东淄博255049；3． 天津大学电气与自动化工程学院，天津300072）摘要：针对ITS（intelligent transport system）智能车辆复杂的非线性结构和时变特性，提出了以空间代替时间建立IV（intelligent vehicle）横向运动模式空间的思想．通过改变车辆的各种参数，针对车体的数学模型采集车体在各种环境条件下的模拟运动控制信息．以模糊C均值算法（FCM）为基础，推导出车辆运动模式空间的构造算法．该构造算法能够客观地对测量数据进行有效聚类，聚类清晰，模式子空间划分清楚，从而为车辆运动模式空间的构造提供了一种理论依据．最后对样本数据的聚类和模式空间的构造算法进行了仿真，结果证明了算法的有效性和可行性     

基于循环抑制CPG模型控制的蛇形机器人蜿蜒运动

根据生物蛇和蛇形机器人的结构及运动特点，应用循环抑制CPG建模理论构建了蛇形机器人神经网络模型；利用蛇形机器人模型，仿真验证了CPG模型对蜿蜒运动控制的有效性；提出并仿真验证了实现有目的转弯控制的CPG参数调节方法。最后，给出了今后的研究方向。     

The Serotonergic Agonists Quipazine, CGS-12066A, and α-Methylserotonin Alter Motor Activity and Induce Hindlimb Stepping in the Intact and Spinal Rat Fetus.

The effects of serotonergic agonists were examined in intact and spinal fetuses, using an in vivo fetal rat preparation. On Gestational Day 20, fetuses were prepared with a midthoracic or sham spinal transection. Dose-response curves were obtained for quipazine (nonselective 5-hydroxytryptamine [5-HT] agonist; 1.0-10.0 mg/kg), CGS-12066A (5-HT1B agonist; 1.0-30.0 mg/kg), and α-methylserotonin (α-Me-5-HT; 5-HT? agonist; 0.2-15.0 mg/kg). During a 10-min test, each of the agonists (delivered via intraperitoneal injection) influenced fetal behavior: They increased the occurrence of head movements, mouthing, and hindlimb stepping. Quipazine and α-Me-5-HT also promoted hindlimb activity in spinal fetuses. Thus, stimulation of the fetal 5-HT system modulates motor activity at multiple levels of the developing central nervous system. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Role of Visual and Nonvisual Information in the Control of Locomotion.

During locomotion, retinal flow, gaze angle, and vestibular information can contribute to one's perception of self-motion. Their respective roles were investigated during active steering: Retinal flow and gaze angle were biased by altering the visual information during computer-simulated locomotion, and vestibular information was controlled through use of a motorized chair that rotated the participant around his or her vertical axis. Chair rotation was made appropriate for the steering response of the participant or made inappropriate by rotating a proportion of the veridical amount. Large steering errors resulted from selective manipulation of retinal flow and gaze angle, and the pattern of errors provided strong evidence for an additive model of combination. Vestibular information had little or no effect on steering performance, suggesting that vestibular signals are not integrated with visual information for the control of steering at these speeds. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

一处室内轮式自主移动机器人的导航控制研究

介绍了一种室内移动机器人CASIA-I.对该机器人的运动机构做了较为详细地阐述, 针对该运动机构给出了机器人的运动方程和一种导航控制算法,并根据该算法进行了软件仿真 和实物实验.在软件仿真和实物实验两种环境下,机器人都能够实时避开障碍物奔向目标.仿真 和实验表明:该移动平台具有良好的可靠性,且该导航控制算法是一种有效的导航算法.