基于HMCD的仿人机器人单杠运动控制策略

为完成仿人机器人单杠运动,分析了欠驱动单杠机器人Acrobot模型,并根据IHOG技术要求、实物机器人本体结构和自由度配置,提出了基于HMCD的控制策略.通过单杠视频捕捉获取人体运动数据,根据仿人机器人模型分析关键特征点、基本动作的运动数据得到的关键帧的关节角数据,经过运动学约束调整,采用插值方法生成能够应用于仿人机器人的运动轨迹.在MF-1型仿人机器人单杠实物平台上进行控制实验的成功,验证了该方法的有效性.     

仿人机器人复杂动态动作设计及相似性研究

提出了一种基于人体运动的考虑节奏相似性的仿人机器人复杂动态动作设计方法. 首先, 把人体的运动分割成基本动作段, 给出了运动学约束, 讨论了复杂动态动作的稳定性调节方法. 然后, 提出了考虑运动节奏的仿人机器人模仿人体动作的相似性函数, 并给出了满足运动学约束和动力学稳定性、具有高相似性的运动轨迹求解方法. 最后, 通过在仿人机器人 BHR-2 上进行中国功夫``刀术'实验验证了该方法的有效性.     

基于人体运动的仿人型机器人动作的运动学匹配

提出了仿人型机器人模仿人体运动的相似性函数,讨论了运动学约束,并给出了满足运动约束条件且具有与人体动作高相似性的运动求解算法. 在有33个自由度的仿人型机器人实体上完成了模仿人的太极拳动作,验证了方法的有效性.     

仿人机器人逆运动学在线求解方法研究

针对仿人机器人逆运动学在线求解问题,从计算实时性与数值稳定性的角度展开研究。分析了仿人机器人下肢拓扑结构,建立其运动学模型与前向运动学描述,根据当前机器人逆运动学算法研究成果,将几何方法与阻尼最小二乘法相结合,并结合分段阻尼思想,提出一种仿人机器人在线逆运动学求解算法。根据运动规划结果,将所提方法与基于雅可比矩阵的广义逆方法通过仿真算例进行对比,验证了所提方法在奇异位形附近的数值稳定性和在线实施的可行性。     

仿人机器人手臂抓取运动规划广义逆RRT算法

针对高维空间冗余仿人机器人手臂抓取对象运动规划时所面临的目标位形未知、抓取空间受限、位形空间存在大量障碍物等难题,提出了一种广义逆快速探索随机树法(RRT)避碰运动方法.首先,提出分层子维空间运动规划概念模型,建立了仿人机器人上肢手臂运动学和逆运动学模型;其次,提出一种加权最小范数广义逆RRT规划算法;最后,通过计算机三维仿真,采用基于有向包围盒法开发的碰撞检测算法,验证了该算法的有效性.     

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Kinect的仿人机器人控制系统

为实现对具有16个自由度仿人机器人的姿态控制,采用Kinect传感器对人体姿态的坐标数据进行采集,根据坐标信息利用Processing软件开发基于SimpleOpenNI库的上位机软件,建立人体关节模型,并利用空间向量法对仿人机器人的步态规划以及重心控制算法分析,解析各关节的转动角度,经由无线WiFi模块向仿人机器人发送指令以控制舵机的运动,最终实现对机器人的控制,搭建了基于Kinect传感器的测试平台.测试结果表明:仿人机器人上肢在运动范围内无死角,通过对重心的控制,下肢可实现简单的步行,符合预期效果.     

基于运动相似性的仿人机器人双足步行研究

提出了一种基于人体步行运动相似性的仿人机器人双足步行动作设计方法．改进了人体步行轨迹的参 数获取与相似性匹配系统,扩展了相似性函数的适用范围．根据仿人机器人的机械连杆特点定义了步行运动周期中 的关键姿势与子相变换,建立了运动学约束方程,并对行走中出现的动态稳定性问题进行了约束．仿真和实体机器 人实验验证了该方法的有效性．     

基于体感的仿人机器人步态学习与控制

针对现有理想化步态动力学模型规划方法复杂、人为指定参数过多、计算量大的问题,提出一种基于体感数据学习人体步态的仿人机器人步态生成方法。首先,用体感设备收集人体骨骼信息,基于最小二乘拟合方法建立人体关节局部坐标系;其次,搭建人体与机器人映射的运动学模型,根据两者间主要关节映射关系,生成机器人关节转角轨迹,实现机器人对人类行走姿态的学习;然后,基于零力矩点(ZMP)稳定性原则,对机器人脚踝关节转角采用梯度下降算法进行优化控制;最后,在步态稳定性分析上,提出使用安全系数来评价机器人行走稳定程度的方法。实验结果表明,步行过程中安全系数保持在0~0.85,期望为0.4825,ZMP接近于稳定区域中心,机器人实现了仿人姿态的稳定行走,证明了该方法的有效性。     

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.     

Constrained Analytical Trajectory Filter for stabilizing humanoid robot motions

Mimicking human motion with a humanoid robot is essential for allowing humanoid robots to be used in service applications. Simply creating motions without considerations for balance and stability or directly copying motion from a human using motion capture and implementing it on a humanoid robot may not be successful because of the difference in physical properties between the human and the humanoid robot, which may cause instability and make it fall. Using the Zero Moment Point as the stability criteria, this work proposes a Constrained Analytical Trajectory Filter as part of an Analytical Motion Filter, which stabilizes a reference motion that can come from human motion capture data, kinematic synthesis, or animation software. The resulting solutions used in the Constrained Analytical Trajectory Filter provide insight into the complex interactions of motion and stability. The solutions were verified in simulation and with hardware, showing that the analytical filter can be successfully applied for stabilizing reference motions for humanoid robots which may be unstable otherwise.     

Learning to Walk by Imitation in Low-Dimensional Subspaces

In this paper, we provide the first demonstration that a humanoid robot can learn to walk directly by imitating a human gait obtained from motion capture (mocap) data without any prior information of its dynamics model. Programming a humanoid robot to perform an action (such as walking) that takes into account the robot's complex dynamics is a challenging problem. Traditional approaches typically require highly accurate prior knowledge of the robot's dynamics and environment in order to devise complex (and often brittle) control algorithms for generating a stable dynamic motion. Training using human mocap is an intuitive and flexible approach to programming a robot, but direct usage of mocap data usually results in dynamically unstable motion. Furthermore, optimization using high-dimensional mocap data in the humanoid full-body joint space is typically intractable. We propose a new approach to tractable imitation-based learning in humanoids without a robot's dynamic model. We represent kinematic information from human mocap in a low-dimensional subspace and map motor commands in this low-dimensional space to sensory feedback to learn a predictive dynamic model. This model is used within an optimization framework to estimate optimal motor commands that satisfy the initial kinematic constraints as best as possible while generating dynamically stable motion. We demonstrate the viability of our approach by providing examples of dynamically stable walking learned from mocap data using both a simulator and a real humanoid robot.     

遥操作护理机器人系统的操作者姿态解算方法研究

设计了一种遥操作护理机器人系统,为实现从端同构式机器人的随动运动控制,对主端操作者人体姿态解算方法进行了研究.首先,构建由惯性传感单元构成的动作捕捉系统,对用作从端机器人动作指令的操作者人体姿态信息进行采集,采用四元数法对人体运动原始数据进行初步求解.其次,将四元数法得到的姿态数据解算成依据仿人结构设计的护理机器人各关节运动的目标姿态角,实现人体姿态到机器人动作的同构性映射.最后,为验证本文所提姿态解算方法的性能,设计了操作者控制护理机器人完成递送和拿取药瓶动作的实验.结果表明,本文姿态解算方法的解算性能与参考系统基本相同;在操作者动作姿态快速变化的时间段,系统仍可获得较高精度的目标姿态数据,其误差在动态条件下依旧能保持在2%以下;护理机器人可较好地实时复现操作者的人体动作.本文方法能满足机器人进行一般护理作业时对人体姿态数据处理的快速性和准确性要求.     

Motion teaching method for complex robot links using motor current

Conventional robot motion teaching methods use a teaching pendant or a motion capture device and are not the most convenient or intuitive ways to teach a robot sophisticated and fluid movements such as martial arts motions. Ideally, a robot could be set up as if it were a clothing mannequin that has light limbs and flexible yet frictional joints which can be positioned at desirable shape and hold all the positions. To do the same with a robot, an operator could pull or push the links with minor forces until the desired robot posture is attained. For this, a robot should measure the applied external force by using torque sensors at the robot joints. However, torque sensors are bulky and expensive to install in every DOF joints while keeping a compact design, which is essential to humanoid robots. In this paper, we use only motor current readings to acquire joint torques. The equations used to compensate for the effect of gravity on the joint torques and the self-calibration method to earn link parameters are presented. Additionally, kinematic restrictions can be imposed on the robot’s arms to simplify the motion teaching. Here, we teach the Kendo training robot with this method and the robot’s learnt martial art motions are demonstrated.     

Motion planning for humanoid robots based on virtual constraints extracted from recorded human movements

In the field of robotics there is a great interest in developing strategies and algorithms to reproduce human-like behavior. In this paper, we consider motion planning for humanoid robots based on the concept of virtual holonomic constraints. At first, recorded kinematic data of particular human motions are analyzed in order to extract consistent geometric relations among various joint angles defining the instantaneous postures. Second, a simplified human body representation leads to dynamics of an underactuated mechanical system with parameters based on anthropometric data. Motion planning for humanoid robots of similar structure can be carried out by considering solutions of reduced dynamics obtained by imposing the virtual holonomic constraints that are found in human movements. The relevance of such a reduced mathematical model in accordance with the real human motions under study is shown. Since the virtual constraints must be imposed on the robot dynamics by feedback control, the design procedure for a suitable controller is briefly discussed.     

Fuzzy sliding mode control of a finger of a humanoid robot hand

Abstract: The motion control problem for the finger of a humanoid robot hand is investigated. First, the index finger of the human hand is dynamically modelled as a kinematic chain of cylindrical links. During construction of the model, special attention is given to determining bone dimensions and masses that are similar to the real human hand. After the kinematic and dynamic analysis of the model, in order to ensure that the finger model tracks its desired trajectory during a closing motion, a fuzzy sliding mode controller is applied to the finger model. In this controller, a fuzzy logic algorithm is used in order to tune the control gain of the sliding mode controller; thus, an adaptive controller is obtained. Finally, numerical results, which include a performance comparison of the proposed fuzzy sliding mode controller and a conventional sliding mode controller, are presented. The results demonstrate that the proposed control method can be used to perform the desired motion task for humanoid robot hands efficiently.     

A Study on Acquiring Underlying Behavioral Criteria for Manipulator Motion by Focusing on Learning Efficiency

Conventional humanoid robotic behaviors are directly programmed depending on the programmer's personal experience. With this method, the behaviors usually appear unnatural. It is believed that a humanoid robot can acquire new adaptive behaviors from a human, if the robot has the criteria underlying such behaviors. The aim of this paper is to establish a method of acquiring human behavioral criteria. The advantage of acquiring behavioral criteria is that the humanoid robots can then autonomously produce behaviors for similar tasks with the same behavioral criteria but without transforming data obtained from morphologically different humans every time for every task. In this paper, a manipulator robot learns a model behavior, and another robot is created to perform the model behavior instead of being performed by a person. The model robot is presented some behavioral criteria, but the learning manipulator robot does not know them and tries to infer them. In addition, because of the difference between human and robot bodies, the body sizes of the learning robot and the model robot are also made different. The method of obtaining behavioral criteria is realized by comparing the efficiencies with which the learning robot learns the model behaviors. Results from the simulation have demonstrated that the proposed method is effective for obtaining behavioral criteria. The proposed method, the details regarding the simulation, and the results are presented in this paper.     

Planning 3-D Collision-Free Dynamic Robotic Motion Through Iterative Reshaping

We propose a general and practical planning framework for generating 3-D collision-free motions that take complex robot dynamics into account. The framework consists of two stages that are applied iteratively. In the first stage, a collision-free path is obtained through efficient geometric and kinematic sampling-based motion planning. In the second stage, the path is transformed into dynamically executable robot trajectories by dedicated dynamic motion generators. In the proposed iterative method, those dynamic trajectories are sent back again to the first stage to check for collisions. Depending on the application, temporal or spatial reshaping methods are used to treat detected collisions. Temporal reshaping adjusts the velocity, whereas spatial reshaping deforms the path itself. We demonstrate the effectiveness of the proposed method through examples of a space manipulator with highly nonlinear dynamics and a humanoid robot executing dynamic manipulation and locomotion at the same time.