学习人类控制策略的双足机器人步态控制研究

针对双足机器人面临的复杂环境下动态行走的适应性难题,提出了一种基于学习人类控制策略的双足机器人步态控制方法。利用三维线性倒立摆模型构造双足行走系统的状态方程,建立学习人类控制策略的参数化模型,设计了基于SVM的学习型控制器。该方法保证了躯干始终处于与地面近似垂直,增强了步态控制的鲁棒性,提高了双足机器人在复杂环境下行走的动态稳定性。实验验证了该方法的有效性。     

伸缩腿双足机器人半被动行走控制研究

研究半被动伸缩腿双足机器人行走控制和周期解的全局稳定性问题.使用杆长可变的倒立摆机器人模型,以支撑腿的伸缩作为行走动力源,采用庞加莱映射方法分析了双足机器人行走的不动点及其稳定性.当脚与地面冲击时,假设两腿间的夹角保持为常数,设计了腿伸缩长度的支撑腿角度反馈控制率.证明了伸缩腿双足机器人行走过程不动点的全局稳定性.仿真结果表明,本文提出的腿伸缩长度反馈控制可以实现伸缩腿双足机器人在水平面上的稳定行走,并且周期步态对执行器干扰和支撑腿初始角速度干扰具有鲁棒性.     

基于测力平台阵列的双足步行机器人实际零力矩点检测

提出一种多维力测力平台阵列系统,通过机器人行走过程中脚部与平台接触力的测量,并根据. vukobratovic关于ZMP的定义,得到机器人行走过程的ZMP实际轨迹信息,为双足步行机器人的稳态行走步态规划提 供参考依据.更进一步,该系统也可以用作双足步行机器人行走过程步态规划的实验平台.     

基于deep Q-network双足机器人非平整地面行走稳定性控制方法

针对双足机器人在非平整地面行走时容易失去运动稳定性的问题,提出一种基于一种基于价值的深度强化学习算法DQN（Deep Q-Network）的步态控制方法。首先通过机器人步态规划得到针对平整地面环境的离线步态,然后将双足机器人视为一个智能体,建立机器人环境空间、状态空间、动作空间及奖惩机制,该过程与传统控制方法相比无需复杂的动力学建模过程,最后经过多回合训练使双足机器人学会在不平整地面进行姿态调整,保证行走稳定性。在V-Rep仿真环境中进行了算法验证,双足机器人在非平整地面行走过程中,通过DQN步态调整学习算法,姿态角度波动范围在3°以内,结果表明双足机器人行走稳定性得到明显改善,实现了机器人的姿态调整行为学习,证明了该方法的有效性。     

基于主—从控制的双足机器人动步态行走

本文给出一种基于主-从控制实现双足机器人动步态行走的控制方法.该方法计算量小,可以在线规划步态及实现双脚支撑期平滑的动态切换.仿真及实验结果验证了方法的有效性及可行性,实现了变步态动态行走.实验所采用的装置为 HLTR—13双足机器人.该机器人重65kg,高1.1m,具有12个自由度.实验结果表明,本文给出的方法能较好地实现变步态动态行走。从而使机器人具有较强的环境适应能力.     

Robust Sliding-mode Control of Nine-link Biped Robot Walking

A nine-link planar biped robot model is considered which, in addition tothe main links (i.e., legs, thighs and trunk), includes a two-segment foot.First, a continuous walking pattern of the biped on a flat terrain issynthesized, and the corresponding desired trajectories of the robot jointsare calculated. Next, the kinematic and dynamic equations that describe itslocomotion during the various walking phases are briefly presented. Finally,a nonlinear robust control approach is followed, motivated by the fact thatthe control which has to guarantee the stability of the biped robot musttake into account its exact nonlinear dynamics. However, an accurate modelof the biped robot is not available in practice, due to the existence ofuncertainties of various kinds such as unmodeled dynamics and parameterinaccuracies. Therefore, under the assumption that the estimation error onthe unknown (probably time-varying) parameters is bounded by a givenfunction, a sliding-mode controller is applied, which provides a successfulway to preserve stability and achieve good performance, despite the presenceof strong modeling imprecisions or uncertainties. The paper includes a setof representative simulation results that demonstrate the very good behaviorof the sliding-mode robust biped controller.     

Walking Control Algorithm of Biped Humanoid Robot on Uneven and Inclined Floor

This paper describes walking control algorithm for the stable walking of a biped humanoid robot on an uneven and inclined floor. Many walking control techniques have been developed based on the assumption that the walking surface is perfectly flat with no inclination. Accordingly, most biped humanoid robots have performed dynamic walking on well designed flat floors. In reality, however, a typical room floor that appears to be flat has local and global inclinations of about 2°. It is important to note that even slight unevenness of a floor can cause serious instability in biped walking robots. In this paper, the authors propose an online control algorithm that considers local and global inclinations of the floor by which a biped humanoid robot can adapt to the floor conditions. For walking motions, a suitable walking pattern was designed first. Online controllers were then developed and activated in suitable periods during a walking cycle. The walking control algorithm was successfully tested and proved through walking experiments on an uneven and inclined floor using KHR-2 (KAIST Humanoid robot-2), a test robot platform of our biped humanoid robot, HUBO.     

Control of a Biped Walking Robot during the Double Support Phase

This paper discusses the control problem of a biped walking robotduring the double-support phase. Motion of a biped robot during thedouble-support phase can be formulated as motion of robotmanipulators under holonomic constraints. Based on the formulation,the walking gait is generated by controlling the position of thetrunk of the robot to track a desired trajectory, referenced in theworld frame. Constrained forces at both feet were controlled suchthat firm contact is preserved between the feet and ground by using asimplified model of the double-support phase. The control scheme wasevaluated experimentally.     

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。     

Development of a Leg Part of a Humanoid Robot—Development of a Biped Walking Robot Adapting to the Humans' Normal Living Floor

The authors are engaged in studies of biped walking robots from thefollowing two viewpoints. One is a viewpoint as a human science. Theother is a viewpoint towards the development of humanoid robots.In this paper, the authors introduce an anthropomorphic dynamic bipedwalking robot adapting to the humans' living floor. The robot has tworemarkable systems: (1) a special foot system to obtain the positionrelative to the landing surface and the gradient of the surfaceduring its dynamic walking; (2) an adaptive walking control system toadapt to the path surfaces with unknown shapes by utilizing theinformation of the landing surface, obtained by the foot system. Twounits of the foot system WAF-3 were produced, a biped walking robotWL-12RVII that had the foot system and the adaptive walking controlsystem installed inside it was developed, and a walking experimentwith WL-12RVII was performed. As a result, dynamic biped walkingadapting to humans' floors with unknown shapes was realized. Themaximum walking speed was 1.28 s/step with a 0.3 m step length, andthe adaptable deviation range was from -16 to+16 mm/step in the vertical direction, and from-3 to +3° in the tilt angle.     

基于Fuzzy–CPG的双足机器人适应性行走控制

为提高双足机器人的环境适应性, 本文提出了一种基于模糊控制与中枢模式发生器(CPG)的混合控制策 略, 称之为Fuzzy–CPG算法. 高层控制中枢串联模糊控制系统, 将环境反馈信息映射为行走步态信息和CPG幅值参 数. 低层控制中枢CPG根据高层输出命令产生节律性信号, 作为机器人的关节控制信号. 通过机器人运动, 获取环境 信息并反馈给高层控制中枢, 产生下一步的运动命令. 在坡度和凹凸程度可变的仿真环境中进行混合控制策略的 实验验证, 结果表明, 本文提出的Fuzzy–CPG控制方法可以使机器人根据环境的变化产生适应的行走步态, 提高了 双足机器人的环境适应性行走能力.     

动态步行双足机器人THR-I的设计与实现

为了既能验证动态步行的理论结果,又能满足经济性的要求,本文设计并实现了一种基于总线型伺服电机的平面无脚双足机器人THR-I．首先介绍了该机器人的系统构成,包括机械结构部分和控制系统部分;在建立步行动力学方程时,考虑了中心约束系统的耦合影响．然后,利用虚拟约束原理,设计了步行约束,并成功实现了步行周期为0     

双足机器人的鲁棒控制

利用拉格朗日法建立了双足机器人的动力学模型．在双脚支撑地时，运动学方程的约束造成双足机器人自由度的冗余，本文引入拉格朗日因子消除了双足机器人的冗余自由度．采用鲁棒控制法对双足机器人的轨迹跟踪进行控制，仿真实验结果证明, 鲁棒控制法对模型不精确或外部干扰对双足机器人产生的影响有很好的抑制作用，对双足机器人轨迹跟踪控制是有效的．     

Path Planning for a Statically Stable Biped Robot Using PRM and Reinforcement Learning

In this paper path planning and obstacle avoidance for a statically stable biped robot using PRM and reinforcement learning is discussed. The main objective of the paper is to compare these two methods of path planning for applications involving a biped robot. The statically stable biped robot under consideration is a 4-degree of freedom walking robot that can follow any given trajectory on flat ground and has a fixed step length of 200 mm. It is proved that the path generated by the first method produces the shortest smooth path but it also increases the computational burden on the controller, as the robot has to turn at almost all steps. However the second method produces paths that are composed of straight-line segments and hence requires less computation for trajectory following. Experiments were also conducted to prove the effectiveness of the reinforcement learning based path planning method.     

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。     

双足机器人自适应常值驱动与传感反馈结合的仿生行走控制

为了将双足机器人的混沌步态控制收敛到稳定的周期步态,提出一种控制策略。首先用庞卡莱截面法研究斜坡倾角变化对步态的影响,结果表明,坡度增大会导致倍周期步态到混沌步态的产生;然后以人类步行的生物力学为仿生依据,根据延迟反馈控制的基本思路,设计了自适应常值驱动与传感反馈相结合的仿生行走控制策略,并依据当前步和前两步初始状态对控制器参数进行逐步调节,最终将混沌步态控制收敛到周期步态。仿真结果表明了所提出算法的有效性。     

Biped Locomotion by Reduced Ankle Power

Power reduction in the ankle joints of a biped robot is considered inthis paper. Ankles of human beings have small torque and are veryflexible within a certain range of motion (very stiff near and beyondthis range). This characteristic makes foot landing soft and gives agood contact between its sole and the ground. This feature can beimplemented in a biped robot by using a small actuator for the anklejoints. A small actuator consumes less energy and reduces the weightof the leg. With less power in the ankle joints, robot walkingbecomes more difficult to control. This problem can be solved byproviding a feedback control mechanism as presented in this paper. Thecontrol mechanism uses the motion of the body and the swinging leg toeliminate instability caused by the weak ankle. Two locomotionexamples, standing and walking, were investigated respectively toshow the validity of the proposed control scheme. In standing, thecontrol input is the displacement of the ankle joint of thesupporting leg. The control mechanism decides the bending angle ofthe body and the position of the swinging leg. For walking, only thebending angle of the body is used to avoid the discontinuity of thecontrol input. Experimental results are presented to show theeffectiveness of the control mechanism.     

基于六维力／力矩传感器的拟人机器人实际ZMP检测

ZMP（Zero Moment Point)作为双足双行机器人动态稳定行走的判据,已应用于世界上银多著名的步行机器人系统。目前国外步行机器人大多采用力／力矩传感器进行ZMP的实际检测计算,但采用六维力／力矩传感器的却不多,而且其安装位置也各不同,国内机器人还都还都处于离线步态规划阶段,只进行理论了ZMP的计算,并没有进行实时检测。本文根据清华大学985重点项目“拟人机器人技术及其系统研究”的研究要求,确定基于六维力／力矩传感系统的实际ZMP检测方案,确这了传感器安装的最佳位置,推导了单脚支撑期,双脚支撑期的实际ZMP计算公式提出了基于ZMP理论的姿态调整方法,以期在实际应用中进行在线步态规划。     

双足机器人HEUBR-1 样机研制与实验研究

模拟人的肌肉驱动方式,为双足机器人HEUBR-1 设计了二自由度的空间并联机构,并将其应用于双 足机器人HEUBR-1 下肢关节,实现了一种新的串并混联的仿人下肢结构．在HEUBR-1 的足部增加了足趾关节,使 机器人能够模拟人的行走方式,实现真正的拟人步态行走．阐述了双足机器人HEUBR-1 稳定拟人行走的关键性技 术,提出了综合稳定性判据,分析了拟人的多种步态．通过拟人行走步态实验分析,验证了双足机器人HEUBR-1 串 并混联的仿人结构的设计合理性及拟人步态分析的准确性．     

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。