基于ADAMS的双足机器人拟人行走动态仿真

在双足机器人HEUBR_1的设计中,下肢采用了一种新的串并混联的仿人结构,并在足部增加了足趾关节.为验证该仿人结构设计的合理性及拟人步态规划的可行性,在ADAMS虚拟环境中建立了双足机器人HEUSR_1的仿真模型.通过拟人步态规划生成了运动仿真数据,在ADAMS虚拟环境中实现了具有足趾运动的拟人稳定行走,经仿真分析,获得了双足机器人HEUBR_1拟人行走步态下的运动学和动力学特性,仿真结果表明:双足机器人HEUBR_1的串并混联的仿人结构设计能够满足行走要求,且拟人步态规划方法可行,有足趾运动的拟人行走具有运动平稳、能耗低、足底冲击力小的特点.稳定行走的仿真步态数据可为下一步双足机器人HEUBR_1样机行走实验提供参考数据.     

复杂环境下仿人机器人有效支撑区域的感知

当主流的仿人机器人都采ZMP(zero moment point)理论作为稳定行走的判据.实时ZMP点落在支撑足与地面接触形成的多边形支撑区域内是仿人机器人实现稳定步行的必要条件.因此实现仿人机器人在复杂现实环境中稳定行走,必须要求机器人足部感知系统提供足够丰富的地面环境信息,从而可以准确获取支撑区域的形状以实现基于实时ZMP点的稳定控制.文中将柔性阵列力传感器应用于仿人机器人足部感知系统,提出了获取仿人机器人支撑区域形状的方法,而且通过实验验证了其可行性.     

仿人足底肌电特征的机器人行走规划

模仿人类行走规律是规划双足机器人运动的基础.以往模仿人类步态主要通过视觉方法或惯性模块测量(Inertia measurement unit, IMU)方法捕捉人体特征点轨迹.这些方法不考虑零力矩点(Zero moment point, ZMP)的相似性.为解决该问题,本文提出了一种基于足底肌电信号(Electromyography, EMG)和惯性模块测量信号的混合运动规划方法.该方法通过测量足底肌电信号计算出足底压力中心的位置以及踝关节扭矩,结合惯性模块所测量的人体躯干和双足轨迹,来规划双足机器人的步态.首先,用肌电仪测量足底肌电信号,用惯性测量模块测量人体各肢体部分的姿态轨迹,经数据标定后作为仿人机器人的运动参考; 然后,通过预观控制输出稳定的步态.为确保仿人行走的效果,基于人体相似性对运动数据进行了步态优化.实验验证和分析表明, EMG信号超前ZMP约160ms,利用这个特性实现了对压力点位置的有效预测,提高了机器人在线模仿人类行走的稳定性.     

基于多传感器集成的仿人机器人足部感知系统

足部是仿人机器人本体支撑的基础,也是唯一与地面接触并发生相互作用的主要部件,其各种地面信息获取能力是机器人实现仿人的自然性稳定行走控制的关键.基于六维力传感器、惯量测量单元和柔性触觉阵列传感器,设计了一种新型仿人机器人集成化足部感知系统(IPFS).具备对各种地面环境识别和足部姿态获取、足底与外界接触位置的实时感知和估...     

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．     

基于虚拟黏弹性肌肉模型的仿人机器人不平整地面稳定行走

复杂未知环境下,仿人机器人在行走过程中难以获取精确的地面信息,导致规划的落脚点与实际落脚位置之间存在误差,这会对机器人的平衡造成严重的干扰.针对该问题,提出了模拟人体肌肉黏弹性的虚拟肌肉模型,并基于该模型设计了仿人机器人在不平整地面上的稳定行走控制方法.首先,从仿生角度出发,扩展传统肌肉模型,构建了具有伸缩功能的虚拟肌肉模型,并对其黏弹性进行了分析.然后,基于该模型,采用LQR(线性二次型调节器)方法设计了虚拟肌肉伸缩长度与伸缩力的控制方法.最后,基于足部力传感器信息反馈,将该模型应用于仿人机器人行走过程中抬脚高度的调节,使仿人机器人能够适应未知复杂环境中地面高度的突变,或在机器人传感器系统获取的地面信息与实际情况相差很大的情况下实现稳定行走.结果表明,该算法可以使仿人机器人在高度差为6 cm以内的不平整地面上实现1.8 km/h的稳定行走.基于BHR-6P平台的行走仿真验证了该算法的有效性.     

双足机器人预观控制的ZMP补偿步行模式研究

针对双足机器人的稳定行走,提出了一种预观控制的零力距点（ZMP）补偿步行模式在线生成方法。利用实际ZMP与目标ZMP之间的未来误差信息,基于预观控制计算机器人行走过程中质心的补偿量,事先调整质心轨迹来改变步态。最终使实际ZMP更好地跟踪目标值。12自由度的双足机器人动力学仿真验证了所提出方法的有效性,而且机器人能在一定程度不平整地面上实现稳定行走。     

基于六维力/力矩传感器和关节力矩的仿人机器人步态补偿算法

为实现仿人机器人的稳定行走,提出一种根据其足底六维力/力矩传感器信息、针对关节力矩的步态补偿算法．利用直流伺服电机的过载能力,来改善仿人机器人关节在大负载扰动下的动态性能．行走实验证明了该算法在离线实施过程中的有效性．     

双足步行机器人的ZMP-CoP检测及研究

ZMP(零力矩点）和CoP(压力中心)是评价双足步行机构行走稳定性的重要参数．本文在研究了ZMP和CoP两者关系的基础上，根据THBIP-I仿人机器人基于ZMP理论的姿态调整要求和六维力/力矩传感器的安装位置，推导了适用于双足机器人的CoP计算公式，建立了采用六维力/力矩传感器的CoP检测系统．进行了THBIP-I仿人机器人行走过程的实际CoP检测实验，并对实验结果进行了讨论．实验证明了该系统的准确性.     

脉冲推力作用下半被动双足机器人的行走动力学分析

为了提高半被动双足机器人在水平地面上行走的稳定性,研究一种脉冲推力作用下半被动双足机器人的行走动力学行为.以最简单的特殊行走模型为动力学模型,采用支撑腿脚后跟脉冲推力作为双足机器人行走动力源.鉴于系统模型的高度非线性,将连续阶段的非线性微分方程线性化;利用角动量守恒和脉冲推力构造一个二维离散映射;采用离散映射的不动点及其特征值分析系统周期步态的存在性和稳定性;接着讨论系统的倍周期分岔.在理论分析的基础上,通过Matlab软件对半被动双足机器人的行走动力学进行仿真实验. 仿真结果表明,在水平地面上行走的半被动双足机器人具有稳定的周期-1步态和周期-2步态.     

Position-based impedance control of a biped humanoid robot

This paper describes position-based impedance control for biped humanoid robot locomotion. The impedance parameters of the biped leg are adjusted in real-time according to the gait phase. In order to reduce the impact/contact forces generated between the contacting foot and the ground, the damping coefficient of the impedance of the landing foot is increased largely during the first half double support phase. In the last half double support phase, the walking pattern of the leg changed by the impedance control is returned to the desired walking pattern by using a polynomial. Also, the large stiffness of the landing leg is given to increase the momentum reduced by the viscosity of the landing leg in the first half single support phase. For the stability of the biped humanoid robot, a balance control that compensates for moments generated by the biped locomotion is employed during a whole walking cycle. For the confirmation of the impedance and balance control, we have developed a life-sized humanoid robot, WABIAN-RIII, which has 43 mechanical d.o.f. Through dynamic walking experiments, the validity of the proposed controls is verified.     

仿人预测控制在双足机器人步行控制中的应用

基于零力矩点(ZMP)的预测控制是目前双足机器人步行控制中最先进的方法,但是预测控制需要比较精确的预测模型,在环境扰动导致模型失配时,预测控制的性能下降较快。为了解决这个问题,利用仿人智能控制对环境误差具有较强抑制的特点改进预测控制。探讨了在步行控制中引入仿人智能控制的必要性和仿人智能控制改进预测控制的可行性,并设计了仿人预测控制器。最后通过仿真实验验证了新的控制器对双足机器人步行控制的有效性。     

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.     

双足机器人自然ZMP轨迹生成方法研究

为了实现双足机器人类人行走,提出了一种基于自然ZMP轨迹的双足机器人步行模式生成方法。在单腿支撑相,根据基于三维线性倒立摆模型,在设定从脚跟到脚趾移动的自然ZMP轨迹后,得到质心轨迹方程;在双腿支撑相采用线性摆模型生成质心轨迹方程。同时给出了在统一坐标系中的多步规划质心轨迹方程。在RoboCup 3D仿真平台实现了采用自然ZMP轨迹的双足机器人类人稳定步行,实验和竞赛结果都验证了该方法的有效性。     

Two walking gaits for a planar bipedal robot equipped with a four-bar mechanism for the knee joint

The design of a knee joint is a key issue in robotics and biomechanics to improve the compatibility between prosthesis and human movements, and to improve the bipedal robot performances. We propose a novel design for the knee joint of a planar bipedal robot, based on a four-bar linkage. The dynamic model of the planar bipedal robot is calculated. Two kinds of cyclic walking gaits are considered. The first gait is composed of successive single support phases with stance flat-foot on the ground separated by impacts. The second gait is a succession of finite time double support phases, single support phases, and impacts. During the double support phase, both feet rotate. This phase is ended by an impact of the toe of the forward foot, while the rear foot is taking off. The single support phase is ended by an impact of the swing foot heel, the other foot keeping contact with the ground through its toe. For both gaits, the reference trajectories of the rotational joints are prescribed by cubic spline functions in time. A parametric optimization problem is presented for the determination of the parameters corresponding to the optimal cyclic walking gaits. The main contribution of this paper is the design of a dynamical stable walking gait with double support phases with feet rotation, impacts, and single support phases for this bipedal robot.     

Design of a walking cyclic gait with single support phases and impacts for the locomotor system of a thirteen-link 3D biped using the parametric optimization

The development of an algorithm of parametric optimization to achieve optimal cyclic gaits in space for a thirteen-link 3D bipedal robot with twelve actuated joints is proposed. The cyclic walking gait is composed of successive single support phases and impulsive impacts with full contact between the sole of the feet and the ground. The evolution of the joints are chosen as spline functions. The parameters to define the spline functions are determined using an optimization under constraints on the dynamic balance, on the ground reactions, on the validity of impact, on the torques, and on the joints velocities. The cost functional considered is represented by the integral of the torques norm. The torques and the constraints are computed at sampling times during one step to evaluate the cost functional for a feasible walking gait. To improve the convergence of the optimization algorithm the explicit analytical gradient of the cost functional with respect to the optimization parameters is calculated using the recursive computation of torques. The algorithm is tested for a bipedal robot whose numerical walking results are presented.     

Human like trajectory generation for a biped robot with a four-bar linkage for the knees

The design of a knee joint is a key issue in robotics to improve the locomotion and the performances of the bipedal robots. We study a design for the knee joints of a planar bipedal robot, based on a four-bar linkage. We design walking reference trajectories composed of double support phases, single support phases and impacts. The single support phases are divided in two sub-phases. During the first sub-phase the stance foot has a flat contact with the ground. During the second sub-phase the stance foot rotates on its toes. In the double support phase, both stance feet rotate. This phase is ended by an impact on the ground of the toe of the forward foot, the rear foot taking off. The single support phase is ended by an impact of the heel of the swing foot, the other foot keeping contact with the ground through its toes. A parametric optimization problem is presented for the determination of the parameters corresponding to the optimal cyclic walking gaits. In the optimization process this novel bipedal robot is successively, overactuated (double support with rotation of both stance feet), fully actuated (single support sub-phase with a flat foot contact), and underactuated (single support sub-phase with a rotation of the stance foot). A comparison of the performances with respect to a sthenic criterion is proposed between a biped equipped with four-bar knees and another with revolute joints. Our numerical results show that the performances with a four-bar linkage are bad for the smaller velocities and better for the higher velocities. These numerical results allows us to think that the four-bar linkage could be a good technological way to increase the speed of the future bipedal robots.     

采用DDPG的双足机器人自学习步态规划方法

为解决多自由度双足机器人步行控制中高维非线性规划难题,挖掘不确定环境下双足机器人自主运动潜力,提出了一种改进的基于深度确定性策略梯度算法（DDPG）的双足机器人步态规划方案。把双足机器人多关节自由度控制问题转化为非线性函数的多目标优化求解问题,采用DDPG算法来求解。为解决全局逼近网络求解过程收敛慢的问题,采用径向基（RBF）神经网络进行非线性函数值的计算,并采用梯度下降算法更新神经网络权值,采用SumTree来筛选优质样本。通过ROS、Gazebo、Tensorflow的联合仿真平台对双足机器人进行了模拟学习训练。经数据仿真验证,改进后的DDPG算法平均达到最大累积奖励的时间提前了45.7%,成功率也提升了8.9%,且经训练后的关节姿态角度具有更好的平滑度。