基于质心运动状态的双足机器人欠驱动步行稳定控制

针对双足机器人欠驱动步行稳定控制问题,提出一种基于机器人质心(Co M)运动状态的前馈控制策略.首先,根据步行速度与步行稳定性的关系,提出一种基于步行速度的欠驱动步行稳定性直观表述并给予数学定义:如果机器人步行速度能够始终收敛于一个已被证明可维持步行的速度,则机器人步行处于稳定状态;然后,基于该直观表述的数学定义和人类变速步行时的步态特征,提出一种基于质心运动状态的前馈控制策略,控制策略以机器人质心水平速度作为系统输出,通过控制质心在单个步行周期内的位移,实现对质心水平运动速度的控制,进而实现稳定步行;最后,在混凝土和木板地面上,成功实现了平均步行速度0.178 m/s、步幅为腿长0.31倍的欠驱动步行.试验结果表明:所提出的控制策略能够通过控制质心对理想速度的跟踪,实现欠驱动稳定步行.     

双足机器人动态步行实时步态生成

针对双足机器人动态步行生成关节运动轨迹复杂问题,提出了一种简单直观的实时步态生成方案。建立了平面五杆双足机器人动力学模型,通过模仿人类步行主要运动特征并根据双足机器人动态步行双腿姿态变化的要求,将动态步行复杂任务分解为顺序执行的四个过程,在关节空间相对坐标系下设计了躯干运动模式、摆动腿和支撑腿动作及步行速度调整模式,结合当前步行控制结果反馈实时产生稳定的关节运动轨迹。仿真实验验证了该方法的有效性,简单易实现。     

双足机器人稳定行走的仿人预测控制研究

针对双足机器人的稳定行走,提出了一种新的仿人预测控制在线步行模式生成方法。把期望零力矩点（ZMP）分解成离线规划好的参考ZMP和实时变化的可变ZMP之和,通过预测控制和其逆系统共同作用对质心运动进行控制,从而生成具有自适应性的步行模式。但单一的预测控制系统对诸如矩形齿状扰动的可变ZMP的跟踪存在较大的误差,结合仿人智能控制对误差的强抑制能力,设计了与预测控制相结合的仿人预测控制系统。仿真实验验证对矩形齿状扰动的可变ZMP,仿人预测系统也能实现较好的跟踪。     

两足步行椅机器人机械参数与步行稳定性关系研究

研究了两足步行椅机器人的机械参数对步行ZMP（Zero Moment Point）稳定裕度的影响．采用稳定的参数化步态，在步态不变的情况下，改变机器人各连杆的质量、转动惯量和质心位置，分析其对ZMP稳定裕度的影响．通过仿真实验得到以下结论：合理选择大腿、小腿和上身的机械参数可以显著增加步行ZMP稳定裕度，从而降低伺服控制的难度．     

人体步行规律与仿人机器人步态规划

从仿生学角度分析了人体的步行运动规律,提出了一种基于人体运动规律的仿人机器人步态参数设定方法.首先对人体步行运动数据进行捕捉并分析,得出人体各步态参数间的函数关系,以人体步行相似性作为评价指标,提出仿人机器人步态参数的设定方法.其次,通过分析人体在步行过程中的补偿支撑脚偏航力矩的基本原理,提出了基于双臂及腰关节协调运动的仿人机器人偏航力矩补偿算法,以提高仿人机器人行走的稳定性.最后通过仿真及实验验证了所提出的步态规划方法的正确性及有效性.     

基于腰关节力矩补偿的仿人机器人快速步行模式生成

为了有效地提高仿人机器人动步行能力,利用基于预观控制的ZMP 步态生成模式的优点并引入脚尖 脚后跟与地面间的旋转关节,生成了机器人的质心和踝关节轨迹．同时,为了得到更快的步行速度,提出了侧向质 心摆动幅度递减和腰关节偏摆力矩补偿的方法．最后在虚拟物理环境下,利用动力学仿真软件实现了虚拟3-D 仿人 机器人快速动步行．仿真结果证明了所采用方法的有效性．     

船舶混沌运动的周期脉冲参数微扰控制

针对海上航行船舶非线性运动中存在的混沌现象,为实现对航行船舶的高精度航向控制,本文将脉冲参数微扰方法与横截同宿点理论相结合,提出了一种基于Melnikov方法的简捷船舶混沌运动周期脉冲参数微扰控制方法.该控制方法利用Melnikov函数确定控制脉冲参量关系及取值范围,基于该方法设计的控制器克服了脉冲参量取值难以确定的不足.仿真结果表明,本文所提方法能将混沌系统快速稳定到不同的周期轨道,具有较好的控制效果.     

一类磁性刚体航天器的混沌控制研究

采用基于误差线性系统稳定性准则的混沌控制方法,控制具有结构内阻尼的磁性刚体航天器在重力场与磁场共同作用下在圆形轨道的混沌姿态运动.讨论了航天器姿态运动方程中部分参数的取值对于运动姿态的影响,给出了这些参数通过倍周期分岔或逆倍周期分岔通往混沌的途径.当参数使系统做混沌姿态运动时,采用上述方法将混沌运动控制至周期-4轨道,并实现周期-1、2、4轨道之间转换的灵活控制.此外,分析了控制参数的变化对于控制效果的影响,并分别给出了控制至不同轨道时的输入扰动范围及控制参数范围.仿真结果表明,该方法能够实现混沌姿态运动在预定周期轨道间的灵活控制,且输入扰动量小、控制速度快、具有高精度,从而验证了该方法在航天器混沌姿态运动控制方面的有效性.     

基于自学习中枢模式发生器的仿人机器人适应性行走控制

为了克服传统中枢模式发生器(Central pattern generator, CPG)关节空间控制方法的复杂性和局限性, 本文基于自学习中枢模式发生器模型, 提出了一套在线调制和融合多传感器信息的仿人机器人环境自适应行走控制方法.算法难点在于如何在机器人的工作空间将自学习CPG用于工作空间轨迹生成, 并使CPG参数直接和步态模式相关联.本文提出了利用自学习CPG来学习和实时生成机器人质心轨迹和脚掌轨迹的方法, 在线调节机器人步长、抬腿高度和步行速度等关键参数.参考生物反射行为, 利用传感反馈信息激发CPG以产生具有环境适应性的工作空间轨迹, 提升行走质量. 控制系统的参数通过优化算法来进一步改善行走性能.相比于传统的CPG关节空间法, 本文所采用的自学习CPG工作空间法不仅极大简化了CPG网络结构而且提高了仿人机器人行走的适应性.最后, 通过仿人机器人坡面适应性行走的仿真和实验, 验证了所提出控制策略的可行性和有效性.     

三维线性倒立摆模型在双足机器人系统中的应用

机器人稳定的步行行走模式在双足机器人的控制中占有很重要的地位,提出了一种基于三维线性倒立摆的机器人行走模型。通过机器人的三维倒立摆模型得到机器人质心的位置和速度,再结合机器人的逆运动学,求得机器人各关节的关节角度,驱动机器人关节运动。从而得到机器人完整的运动轨迹。     

RoboCup3D仿真机器人步态优化研究

在RoboCup3D比赛中,拥有一个快速灵活、稳定的步态模式是赢得机器人足球比赛的关键之一。为了获得这样的步态模式,提出一种双足机器人垂直质心高度可变的机器学习训练方法。首先,通过规划双足机器人垂直质心高度的轨迹、利用倒立摆模型和数值化方法控制零力矩点,实现双足机器人的类人行走。然后,采用自适应协方差矩阵进化算法对步态参数优化,为了获得快速稳定的步行,采用累积分层的学习方法在之前优化的基础上进一步优化。最后,采用蜂拥编队壁障算法验证多机器人环境下优化步态的稳定性、灵活性。实验和竞赛结果均表明本文提出算法的有效性。     

Adaptive dynamic walking of a quadruped robot using a neural system model

We are trying to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model. In this paper, we integrate several reflexes, such as a stretch reflex, a vestibulospinal reflex and extensor/flexor reflexes, into a central pattern generator (CPG). We try to realize adaptive walking up and down a slope of 12°, walking over an obstacle 3 cm in height, and walking on terrain undulation consisting of bumps 3 cm in height with fixed parameters of CPGs and reflexes. The success in walking on such irregular terrain in spite of stumbling and landing on obstacles shows that the control method using a neural system model proposed in this study has the ability for autonomous adaptation to unknown irregular terrain. In order to clarify the role of a CPG, we investigate the relation between parameters of a CPG and the mechanical system by simulations and experiments. CPGs can generate stable walking suitable for the mechanical system by receiving inhibitory input as sensory feedback and generate adaptive walking on irregular terrain by receiving excitatory input as sensory feedback. MPEG footage of these experiments can be seen at: http://www.kimura.is.uec.ac.jp.     

下肢助行外骨骼步态规划与仿真研究

针对目前大多数下肢助行外骨骼自身不能维持稳定行走与步态规划方法存在缺陷的现状，设计一种10自由度主动驱动下肢外骨骼；其步态规划基于静稳定性判据，采用重心投影法，利用构造函数规划重心及踝关节轨迹，进一步通过运动学计算获得完整步态周期内所有关节的运动轨迹；运用ADAMS构建人-外骨骼耦合运动虚拟样机模型，进行平地步行仿真，仿真得到关键点的空间轨迹与动力学参数，通过与规划的重心和踝关节轨迹对比，外骨骼能够实现预期运动并稳定行走，仿真结果表明所设计的步态规划方法合理有效。     

Development and motion control of a biped walking robot based on passive walking theory

This research aims to develop the biped walking robot that can walk on the horizontal ground and improve walking efficiency by utilizing the theory of the passive walking robot, namely the pendulum principle. For that, two motors were installed on the hip of the robot to generate the control torques to perform a walking motion. The computer simulations with dynamic model were carried out to investigate the walking capability of the system. Experimental robot was developed considering the calculated results. The proportional control law was used in walking experiment. The robot can walk on the horizontal ground with the proposed method.     

3D stable biped walking control and implementation on real robot

A combination of walking control methods was proposed and implemented on a biped robot. The LIPM-based model predictive control (MPC) was adopted to generate a basic stable walking pattern. The stability of pitch and yaw rotation was improved through pitch and yaw momentum control as a supplementation of MPC. It is found that biped robot walking tends to deviate from the planned walking direction if not considering the rotation friction torque in yaw axis under the support foot. There are basically two methods to control yaw momentum, waist and swing arms rotation control. However, the upper body is often needed to accomplish other tasks. Therefore, a yaw momentum control method based on swing leg dynamics was proposed. This idea does not depend on upper body’s motion and is highlighted in this paper. Through experiments, the feasibility of the combination of the control methods proved to be practical in keeping biped robot walking stable both in linear and rotation motion. The pros and cons of the yaw momentum control method were also tested and discussed through comparison experiments, such as walking on flat and uneven terrain, walking with different payloads.     

Reinforcement learning based compliance control of a robotic walk assist device

ABSTRACT

Millions of people around the globe have to deal with walking disability. Robotic walk assist devices can help people with walking disabilities, especially those with weak legs. However, safety, cost, efficiency and user friendliness are some of the key challenges. For robotic walk assist devices, light weight structure and energy efficient design as well as optimal control are vitally important. In addition, compliance control can help to improve the safety of such devices as well as contribute to their user friendliness. In this paper, an optimal adaptive compliance control is proposed for a Robotic walk assist device. The suggested scheme is based on bio-inspired reinforcement learning. It is completely dynamic-model-free scheme and employs joint position and velocity feedback as well as sensed joint torque (applied by user during walk) for compliance control. The efficiency of the controller is tested in simulation on a robotic walk assisting device model.     

Trajectory generation and control for a biped robot walking upstairs

This paper presents an effective and systematic trajectory generation method, together with a control method for enabling a biped robot to walk upstairs. The COG (center of gravity) trajectory is generated by the VHIPM (virtual height inverted pendulum mode) for the horizontal motion and by a 6th order polynomial for the vertical motion; an ankle compliance control (ACC) is also added into the robot control. The proposed methods are evaluated by simulations as well as being implemented in a robot for the performance verification. The results show that the proposed methods can generate stable motions when walking upstairs, and these can significantly reduce the zero moment point (ZMP) errors compared with other methods, enabling the robot to walk up steeper stairs.     

A Biologically Inspired Biped Locomotion Strategy for Humanoid Robots: Modulation of Sinusoidal Patterns by a Coupled Oscillator Model

Biological systems seem to have a simpler but more robust locomotion strategy than that of the existing biped walking controllers for humanoid robots. We show that a humanoid robot can step and walk using simple sinusoidal desired joint trajectories with their phase adjusted by a coupled oscillator model. We use the center-of-pressure location and velocity to detect the phase of the lateral robot dynamics. This phase information is used to modulate the desired joint trajectories. We do not explicitly use dynamical parameters of the humanoid robot. We hypothesize that a similar mechanism may exist in biological systems. We applied the proposed biologically inspired control strategy to our newly developed human-sized humanoid robot computational brain (CB) and a small size humanoid robot, enabling them to generate successful stepping and walking patterns.     

Stealth walking with reduced double-limb support phase and its extension to careful legged locomotion

Stealth walking is an underactuated walking movement completing in one step for a stable and cautious walk on irregular terrains. The generated gait generally consists of the single- and double-limb support phases; during the former, the leg angles are strictly controlled to follow the desired trajectories to make the forefoot land on the ground stealthily, whereas during the latter, the upper body is controlled to return to the initial state while keeping the vertical ground reaction forces acting on both feet positive. This causes, however, the increase of the step period and deterioration of the energy efficiency. To solve this problem, this paper discusses some methods for achieving high-speed stealth walking based on a reduction of the double-limb support phase. First, a model of an underactuated rimless wheel with an upper body is introduced for analysis. Second, a method for generating a stealth walking gait of the linearized model is proposed, and the instability inherent in the gait is mathematically investigated. Third, two methods for extending the obtained results to the nonlinear model are discussed. Fourth, the method is also extended to generate a careful walking gait on the frictionless road surface; the importance and significance of this study are discussed through investigation of a strict stealth walking.     

Predictive simulation of human walking transitions using an optimization formulation

A general optimization formulation for transition walking prediction using 3D skeletal model is presented. The formulation is based on a previously presented one-step walking formulation (Xiang et al., Int J Numer Methods Eng 79:667–695, 2009b). Two basic transitions are studied: walk-to-stand and slow-to-fast walk. The slow-to-fast transition is used to connect slow walk to fast walk by using a step-to-step transition formulation. In addition, the speed effects on the walk-to-stand motion are investigated. The joint torques and ground reaction forces (GRF) are recovered and analyzed from the simulation. For slow-to-fast walk transition, the predicted ground reaction forces in step transition is even larger than that of the fast walk. The model shows good correlation with the experimental data for the lower extremities except for the standing ankle profile. The optimal solution of transition simulation is obtained in a few minutes by using predictive dynamics method.