基于全身协调的仿人机器人步行稳定控制

提出利用机器人质心（CoM）雅克比矩阵,实现全身协调补偿的算法。提出机器人的简化模型;分析基于CoM雅克比矩阵的补偿算法;采用CoM/ZMP（零点矩点）、减振和软着陆控制器实时控制双足步行,实现机器人全身协调的稳定控制;通过仿人机器人AFU09的双足步行实验证明该控制方法的有效性。     

基于ZMP误差校正的仿人机器人步行控制

步行环境不理想、外力扰动等因素导致仿人机器人步行时ZMP出现误差,从而影响机器人的步行稳定性.由于机器人的各关节角度都对ZMP有影响,若只校正支撑腿的踝关节或髋关节等单个关节角度,则难以达到理想的步行控制效果,因此,本文综合考虑各关节角度对ZMP的影响,先通过模糊控制器基于ZMP误差给出机器人的质心位置增量,再利用二次规划方法和质心的雅可比矩阵求解出满足该质心位置增量的各关节角度校正量.仿真实验表明,本文方法较好地跟踪了期望ZMP,提高了步行稳定裕度,使仿人机器人实现了稳定的步行.     

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．     

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．     

仿人机器人动态步行控制综述

综述了仿人机器人动态步行的研究历史和研究现状。归纳了动态步行的特点,分析了动态步行稳定性判定方法,介绍了基于ZMP的姿态稳定判据和基于庞加莱映射（Poincaré Map）的步态稳定判据。总结了仿人机器人学习适应复杂地面环境步行的方法,概述了动态步行控制实现的典型解决方案,指出了动态步行控制中待解决的问题,并探讨了未来的发展方向。     

足球反作用力下仿人机器人姿态控制研究

本文以仿人机器人踢足球为例,以现实机器人为原型,利用Solidworks建立了仿人机器人的三维机械结构模型,利用ADAMS和Simulink联合仿真的方法,仿真得到了机器人踢足球的整个过程,利用ZMP理论实时求得机器人的重心轨迹,计算与理想轨迹的偏差,通过改变机器人姿态以减小偏差,实现了对机器人的闭环控制,提高了机器人在受外力作用下的稳定性,并进行了实物实验,验证了该方法的有效性。     

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．     

仿人机器人的不平整地面落脚控制方法

研究了仿人机器人在不平整地面上的落脚控制方法．首先,给出落脚控制问题的详细描述,对落脚过 程进行分析,将几种典型的足—地接触模式转化成统一的等效单点接触模式．然后,引入人类落脚控制经验,设计 基于主动柔顺控制原理的模糊控制器,实现基于等效单点的接触控制,并结合在线运动规划进行了落脚控制的应用 方法研究．最后,通过“Virtual Blackmann”不平整地面行走仿真实验,验证了所提方法的有效性．     

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.     

基于动量控制的人型机器人实时稳定全身运动规划

本文介绍了一种通过动量控制来生成人型机器人稳定的全身运动生成算法.首先,本文引入了基于浮体坐标系的运动学模型来实现全身运动规划以及工作空间的拓展;并为了解决因此产生的实时运动规划中的稳定性问题,本文定义、分析、求解了机器人运动中的动量因素来作为运动过程中的自平衡器;结合任务空间法同时实现了末端轨迹跟踪和自平衡约束;最后通过仿真和实体机器人实验验证了最终算法的有效性.通过运用本文算法,机器人实现了在屈体向前以及动态跟踪人体运动两个应用场景中的自平衡,并通过对比有无平衡器的质心曲线等数据证明了算法的有效性以及必要性.     

Reflexive stability control framework for humanoid robots

In this paper we propose a general control framework for ensuring stability of humanoid robots, determined through a normalized zero-moment-point (ZMP). The proposed method is based on the modified prioritized kinematic control, which allows smooth and continuous transition between priorities. This, as long as the selected criterion is met, allows arbitrary joint movement of a robot without any regard of the consequential movement of the ZMP. On the other hand, it constrains the movement when the criterion approaches a critical condition. The critical condition thus triggers a reflexive, subconscious behavior, which has a higher priority than the desired, conscious movement. The transition between the two is smooth and reversible. Furthermore, the switching is encapsulated in a single modified prioritized task control equation. We demonstrate the properties of the algorithm on two human-inspired robots developed in our laboratory; a human-inspired leg-robot used for imitating human movement and a skiing robot.     

Whole-body hierarchical motion and force control for humanoid robots

Robots acting in human environments usually need to perform multiple motion and force tasks while respecting a set of constraints. When a physical contact with the environment is established, the newly activated force task or contact constraint may interfere with other tasks. The objective of this paper is to provide a control framework that can achieve real-time control of humanoid robots performing both strict and non strict prioritized motion and force tasks. It is a torque-based quasi-static control framework, which handles a dynamically changing task hierarchy with simultaneous priority transitions as well as activation or deactivation of tasks. A quadratic programming problem is solved to maintain desired task hierarchies, subject to constraints. A generalized projector is used to quantitatively regulate how much a task can influence or be influenced by other tasks through the modulation of a priority matrix. By the smooth variations of the priority matrix, sudden hierarchy rearrangements can be avoided to reduce the risk of instability. The effectiveness of this approach is demonstrated on both a simulated and a real humanoid robot.     

Self-modeling in humanoid soccer robots

In this paper we discuss the applicability, potential benefits, open problems and expected contributions that an emerging set of self-modeling techniques might bring on the development of humanoid soccer robots. The idea is that robots might continuously generate, validate and adjust physical models of their sensorimotor interaction with the world. These models are exploited for adapting behavior in simulation, enhancing the learning skills of a robot with the regular transference of controllers developed in simulation to reality. Moreover, these simulations can be used to aid the execution of complex sensorimotor tasks, speed up adaptation and enhance task planning. We present experiments on the generation of behaviors for humanoid soccer robots using the Back-to-Reality algorithm. General motivations are presented, alternative algorithms are discussed and, most importantly, directions of research are proposed.     

Direction-changing fall control of humanoid robots: theory and experiments

Humanoid robots are expected to share human environments in the future and it is important to ensure the safety of their operation. A serious threat to safety is the fall of such robots, which can seriously damage the robot itself as well as objects in its surrounding. Although fall is a rare event in the life of a humanoid robot, the robot must be equipped with a robust fall strategy since the consequences of fall can be catastrophic. In this paper we present a strategy to change the default fall direction of a robot, during the fall. By changing the fall direction the robot may avoid falling on a delicate object or on a person. Our approach is based on the key observation that the toppling motion of a robot necessarily occurs at an edge of its support area. To modify the fall direction the robot needs to change the position and orientation of this edge vis-a-vis the prohibited directions. We achieve this through intelligent stepping as soon as the fall is predicted. We compute the optimal stepping location which results in the safest fall. Additional improvement to the fall controller is achieved through inertia shaping, which is a principled approach aimed at manipulating the robot’s centroidal inertia, thereby indirectly controlling its fall direction. We describe the theory behind this approach and demonstrate our results through simulation and experiments of the Aldebaran NAO H25 robot. To our knowledge, this is the first implementation of a controller that attempts to change the fall direction of a humanoid robot.     

Current-pressure-position triple-loop feedback control of electro-hydrostatic actuators for humanoid robots

To overcome the tradeoff between torque density and response of the backdrivable actuators, actuation by electro-hydrostatic actuators (EHA) is effective. While their backdrivability and energy efficiency was shown in the previous studies, their closed-loop dynamic behavior was not discussed in detail. In this paper, we present the analysis and experimental evaluation of the force control performance of the electro-hydrostatic actuator for the humanoid robot ‘Hydra’. We first present a simplified model of EHA and show that EHA can be simplified as a mass-spring-damper model if all values such as pump torque/velocity and fluid pressure/flow-rate are expressed in the equivalent value seen from the actuator. We also show the comparison between the model and experimentally acquired open-loop dynamic behavior. Then, the evaluation on the force measurement and control performance is shown. The static friction on the rod-seal was 0.46% of the maximum piston force, and with additional strain gauge information, the error can be reduced to 0.28% of the maximum force. We also show that our developed EHA has a pressure control bandwidth of 100?Hz in the fixed piston configuration, which is higher than other state-of-the-art series elastic actuators. In the last of paper, the joint level position and torque control performance of Hydra is examined.     

仿人机器人的机械结构设计与控制系统构建

文章研发了一款适用于机器人教育教学的多功能、多用途、普适性的19自由度的小型仿人机器人,主要完成了该机器人的机械结构设计与控制系统构建工作[1]。所设计的机器人机械结构可靠性高、工艺性好、结构紧凑、样式新颖;所构建的机器人控制系统鲁棒性高、稳定性好、控制准确、反应迅速,圆满地实现了预期的设计任务。通过对优缺点的综合对比,得出组合式构型方案在功能性、实用性和稳定性等方面具有明显优势,有望通过后续软件系统的开发提高其运动效能,真正在青少年机器人教育中发挥重要作用[2]。     

State-incremental optimal control of 3D COG pattern generation for humanoid robots

Many researchers have proposed walking pattern generation methods with zero moment point – center of gravity (ZMP–COG) constraints. Some of the researchers used a neural-networks (NN), a central pattern generator (CPG), or a genetic algorithm (GA) for ZMP–COG pattern generation. However, the parameters used in those methods are too many, and the procedure to learn or to search them costs too much computation time. Other researchers designed controllers or used analytical solution method to generate COG trajectories. These methods generate the ZMP-COG pattern very quickly, but the COG height is limited to a constant to linearize the inverted pendulum model of the robot. Due to this limitation, the robots cannot walk freely on surfaces that change in height. To solve this problem, researchers start to use the original nonlinear inverted pendulum model to make the COG height changeable such as using a numerical method or a feedback controller. In this paper, an optimal control-based pattern generator that can allow COG height change is proposed. It can solve sagittal and lateral COG patterns with arbitrarily assigned COG height and ZMP trajectories in real time. Thus, dynamic walking on height-changing surfaces can be achieved.     

Humans and humanoid social robots in communication contexts

As humanoid social robots are developed rapidly in recent years and experimented in social situations, comparing them to humans provides insights into practical as well as philosophical concerns. This study uses the theoretical framework of communication constraints, derived in human–human communication research, to compare whether people apply social-oriented constraints and task-oriented constraints differently to human targets versus humanoid social robot targets. A total of 230 students from the University of Hawaii at Manoa participated in the study. The participants completed a questionnaire, which determined their concern for the five communication constraints (feelings, non-imposition, disapproval, clarity, and effectiveness) in situations involving humans or robots. The results show people were more concerned with avoiding hurting the human’s feelings, avoiding inconveniencing the human interactive partner, and avoiding being disliked by the human and less concerned with avoiding hurting the robot’s feelings, avoiding inconveniencing the robot partner, and avoiding being disliked by the robot. But people did not differ in their concerns of the two task-oriented constraints (clarity and effectiveness) in response to humans versus humanoid robots. The results of the research suggest that people are more likely to emphasize the social-oriented constraints in communication with humans.