一种仿人机器人跑步状态分析模型

依据仿人机器人跑步的动力学特性,通过对仿人机器人虚拟加速度传感器输出的信号进行分析,建立了仿人机器人跑步相关特征值的概率模型.针对仿人机器人的结构,分析了在整个跑步过程中惯性力和弯矩的作用,对跑步状态的影响,获取虚拟加速度传感器输出的信号,采用小波变换分析动态信号,同时进行快速傅里叶变换,在频域上提取能量特征值.使用马氏距离作为稳定跑步的判定标准,并给出了定量描述,在ADAMS软件中搭建仿人机器人,虚拟加速度传感器设置在质心处,进行跑步仿真实验,仿真实验结果表明,该模型能够反映仿人机器人的跑步特性,仿人机器人能够在跑步状态发生改变时,根据跑步特征及时调整步态,保证其稳定性.     

基于神经网络的仿人跑步机器人步态规划

为了快速生成仿人机器人跑步运动轨迹,研究了一种用于仿人机器人跑步步态生成的步态规划器。采用三维弹簧倒立摆模型描述跑步过程中仿人机器人质心运动规律,奔跑时机器人质心轨迹及落脚点位置可以由四个步态参数来确定,从而将步态规划问题转化成步态参数优化问题,求解了500余种不同运动状态下的步态参数。建立了基于三层BP神经网络的步态规划器,将优化结果作为训练样本训练神经网络。用上述规划器实现了仿人机器人跑步步态规划并对规划结果进行了仿真验证。研究结果表明,基于BP神经网络的步态规划器可以实现步态参数的快速计算,生成的跑步步态逼真;提出的跑步运动步态规划方法可行,为仿人机器人实时轨迹生成提供了一种解决方法。     

基于ZMP的仿人机器人跑步运动模式

采用小车-曲面桌子模型,提出了一种基于零力矩点(zero moment point,ZMP)的仿人机器人跑步运动模式.在单腿支撑阶段和飞行阶段,分别规划了仿人机器人的质心运动轨迹和双脚运动轨迹.在单腿支撑阶段,求解根据小车-曲面桌子模型建立的动力学方程,依据小车的运动轨迹规划出仿人机器人的质心轨迹;在飞行阶段,仿人机器人质心可看作抛物线运动,质心轨迹可通过水平方向上的匀速运动和竖直方向上的自由落体运动轨迹表示.分析了双脚与地面接触时的力及力矩约束.通过改变ZMP调整身体的倾斜角度,保持身体动态平衡.同时根据动力学方程分别求解出踝关节及其他关节的关节力矩.仿真实验结果表明:仿人机器人跑步时各关节角度和关节驱动力矩变化稳定,能够实现稳定的跑步,验证了方法的有效性.     

基于能效优化的仿人机器人跑步步态优化与控制

针对高能耗导致的仿人机器人难以大规模实用化的问题,提出了一种新的仿人机器人参数化跑步步态优化方法。分析了不同跑步步态参数对仿人机器人水平、垂直方向的稳定性及能耗的影响,将机器人步态优化问题转化为对步态参数的多目标寻优问题,根据连杆模型得到机器人跑步过程中水平、垂直方向的稳定裕度及能耗表达式,并构造目标函数,采用基于对位学习的遗传算法对机器人参数化跑步步态进行多目标寻优,在保证机器人俯仰、翻滚和偏摆各方向力矩平衡的前提下降低整体能量消耗;针对传统遗传算法早熟及收敛速度慢的问题,提出基于领域知识的精细化初始成员策略,采取生成种群成员对位点的方式更新种群,以加快收敛速度;为提高轨迹跟踪性能,设计了自适应控制器,并给出了稳定性证明。仿真实验表明:该方法能有效降低能耗并保证其稳定性。     

仿人跑步机器人稳定性与控制策略研究进展

针对仿人跑步机器人稳定性和控制策略的研究现状与发展趋势进行了探讨.首先.分析了仿人跑步模型的固有特性,包括单边约束特性、系统混杂特性和变拓扑结构特性,介绍了基于ZMP(Zero Moment Point)和零化角动量的稳定性判据以及基于庞加莱映射和极限环的稳定性判据,总结了基于轨迹规划的时变控制策略和基于虚拟约束的定常...     

仿人型跑步机器人矢状面跑步运动的实现

基于“虚拟腿”的概念,提出了一种新的方法实现仿人型跑步机器人在矢状面内的跑步运动．首先,规划机器人质心的轨迹; 在起跳阶段通过求解“虚拟腿”的二自由度动力学方程,规划机器人质心的轨迹;在飞行阶段机器人质心做自由落体运动．然后,对机器人双脚的运动和上臂的运动进行规划,采用牛顿—拉斐逊法求解非线性方程组得到机器人在每个时刻的运动学参数．最后,根据动力学方程求出各个关节的驱动力矩．仿真实验结果表明：机器人跑步时各个关节角度和关节驱动力矩变化平稳,运动稳定裕度大,机器人可以实现1.2m/s的跑步速度,因此机器人的跑步动作设计合理．     

仿人机器人步态规划反馈控制研究综述

仿人机器人步行稳定性是机器人领域重要研究内容之一。介绍了仿人机器人常用的步态规划方法,划分为非反馈式和反馈式的两种步态规划算法。总结了反馈式步态规划主要研究的内容,并以世界著名Asimo、HRP、KHR和Darmstad仿人机器人为例,描述仿人机器人具体反馈控制方法和过程。探讨了仿人机器人步态反馈控制中有待研究的内容。     

《机器人》2006年度（第28卷）分类总目次

论文与报告涂胶机器人视觉系统的应用研究………………………………唐德威宗德祥邓宗全李明章(1)仿人型跑步机器人矢状面跑步运动的实现…………………………………………魏航信刘明治(5)并联刨床刚度分析及实验研究…………………………………刘红军姜春英房立金赵明场(10)超小型旋翼机系统悬停状态的辨识仿真研究……………………………陈丽刘亮龚振邦(14)仿蚯蚓蠕动微机器人牵引与运动控制………………………………………………王坤东颜国正(19)水下机器人传感器故障诊断…………………………………………………………王丽荣徐玉如(25)基…     

仿人机器人的研究历史、现状及展望

仿人机器人是当前机器人研究领域最活跃的研究方向之一，引起了许多科研工作者的 注意．本文介绍了仿人机器人与其他移动机器人相比的主要优点，对国内外仿人机器人的研 制工作作了综述，并对将来的研究方向和工作重点作出了展望．     

仿人型机器人动态步行控制方法

本文介绍了仿人型机器人动态步行的一些基本问题和相关概念．从信息和控制的 角度对近年来仿人型机器人动态步行研究中出现的步态规划和姿态控制方法进行了分析,并 指出了它们的特点．提出了先进仿人型机器人实现过程中值得进一步研究的问题．     

仿人机器人跑步稳定性准则

A humanoid robot has high mobility but possibly risks of tipping over. Until now, one main topic on humanoid robots is to study the walking stability; the issue of the running stability has rarely been investigated. The running is different from the walking, and is more difficult to maintain its dynamic stability. The objective of this paper is to study the stability criterion for humanoid running based on the whole dynamics. First, the cycle and the dynamics of running are analyzed. Then, the stability criterion of humanoid running is presented. Finally, the effectiveness of the proposed stability criterion is illustrated by a dynamic simulation example using a dynamic analysis and design system (DADS).     

不同个性的情感机器人表情研究

在情感机器人研究中,不同个性的面部表情是情感机器人增强真实感的重要基础。为实现情感机器人更加丰富细腻的表情,将人类的个性特征引入情感机器人,分析个性理论和情感模型理论,得知不同个性机器人的情感强度。结合面部动作编码系统中面部表情与机器人控制点之间的映射关系,得到情感机器人不同个性的基本表情实现方法。利用Solidworks建立情感机器人脸部模型,在ANSYS工程软件中将SHFR-Ⅲ情感机器人脸部模型设置为弹性体,通过有限元仿真计算方法,对表情的有限元仿真方法进行了探究,得到实现SHFR-Ⅲ不同个性基本表情的控制区域载荷大小和仿真结果。最后,根据仿真结果,进行SHFR-Ⅲ情感机器人不同个性的表情动作实验。实验结果表明,有限元表情仿真可以指导SHFR-Ⅲ情感机器人实现近似人类的不同个性的基本面部表情。     

Symmetry position/force hybrid control for cooperative object transportation using multiple humanoid robots

A symmetry position/force hybrid control framework for cooperative object transportation tasks with multiple humanoid robots is proposed in this paper. In a leader-follower type cooperation, follower robots plan their biped gaits based on the forces generated at their hands after a leader robot moves. Therefore, if the leader robot moves fast (rapidly pulls or pushes the carried object), some of the follower humanoid robots may lose their balance and fall down. The symmetry type cooperation discussed in this paper solves this problem because it enables all humanoid robots to move synchronously. The proposed framework is verified by dynamic simulations.     

Soccer playing humanoid robots: Processing architecture,gait generation and vision system

Research on humanoid robotics in Mechatronics and Automation (MA) Laboratory, Electrical and Computer Engineering (ECE), National University of Singapore (NUS) was started at the beginning of this decade. Various research prototypes for humanoid robots have been designed and are going through evolution over these years. These humanoids have been successfully participating in various robotic soccer competitions. In this paper, three major research and development aspects of the above humanoid research are discussed. The paper focuses on various practical and theoretical considerations involved in processing architecture, gait generation and vision systems.     

Balancing of humanoid robot using contact force/moment control by task-oriented whole body control framework

Balancing control of humanoid robots is of great importance since it is a necessary functionality not only for maintaining a certain position without falling, but also for walking and running. For position controlled robots, the for-ce/torque sensors at each foot are utilized to measure the contact forces and moments, and these values are used to compute the joint angles to be commanded for balancing. The proposed approach in this paper is to maintain balance of torque-controlled robots by controlling contact force and moment using whole-body control framework with hierarchical structure. The control of contact force and moment is achieved by exploiting the full dynamics of the robot and the null-space motion in this control framework. This control approach enables compliant balancing behavior. In addition, in the case of double support phase, required contact force and moment are controlled using the redundancy in the contact force and moment space. These algorithms are implemented on a humanoid legged robot and the experimental results demonstrate the effectiveness of them.     

类人足球机器人决策系统的设计

类人机器人足球比赛是机器人足球比赛的最高赛事.类人足球机器人的决策系统是基于独立视觉的自主决策系统,很大程度上决定着比赛的胜败.介绍了自主研发的类人足球机器人决策系统的架构及实现方法,并在此基础上运用有限状态机理论,对单个机器人的自主进攻策略进行了详细分析和研究,真实环境中的实验及比赛结果证明了其有效性.该决策系统的设计及研究工作对基于自主决策的多智能体协作以及服务性机器人决策系统的研究都具有重要的价值.     

Cooperative transportation system for humanoid robots using simulation-based learning

In this paper, an approach to the behavior acquisition required for humanoid robots to carry out a cooperative transportation task is proposed. In the case of object transportation involving two humanoid robots, mutual position shifts may occur due to the body swinging of the robots. Therefore, it is necessary to correct the position in real-time. Developing the position shift correction system requires a great deal of effort. Solution to the problem of learning the required behaviors is obtained by using the Classifier System and Q-Learning. The successful cooperation of two HOAP-1 humanoid robots in the transportation task has been confirmed by several experimental results.     

任务导向的仿人机器人体系结构设计*

从仿人机器人任务实现过程出发,提出了一种基于任务的分层混合式仿人机器人体系结构。在该体系结构下,多个agent分别被用来模拟人的各主要器官的功能,并模仿人类的行为方式实现了任务实现过程中的三个主要部分:人机交互、导航和对象操作。该体系结构对于高智能水平的仿人机器人的研究和开发具有重要意义。     

Statistical Learning for Humanoid Robots

The complexity of the kinematic and dynamic structure of humanoid robots make conventional analytical approaches to control increasingly unsuitable for such systems. Learning techniques offer a possible way to aid controller design if insufficient analytical knowledge is available, and learning approaches seem mandatory when humanoid systems are supposed to become completely autonomous. While recent research in neural networks and statistical learning has focused mostly on learning from finite data sets without stringent constraints on computational efficiency, learning for humanoid robots requires a different setting, characterized by the need for real-time learning performance from an essentially infinite stream of incrementally arriving data. This paper demonstrates how even high-dimensional learning problems of this kind can successfully be dealt with by techniques from nonparametric regression and locally weighted learning. As an example, we describe the application of one of the most advanced of such algorithms, Locally Weighted Projection Regression (LWPR), to the on-line learning of three problems in humanoid motor control: the learning of inverse dynamics models for model-based control, the learning of inverse kinematics of redundant manipulators, and the learning of oculomotor reflexes. All these examples demonstrate fast, i.e., within seconds or minutes, learning convergence with highly accurate final peformance. We conclude that real-time learning for complex motor system like humanoid robots is possible with appropriately tailored algorithms, such that increasingly autonomous robots with massive learning abilities should be achievable in the near future.