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。     

基于软PLC的烟草拆包机器人运动控制系统设计

在机器人运动学分析的基础上,阐述了用软PLC和SERCOS总线开发机器人控制系统中运动控制的方法,并主要阐述了机器人语言与NC代码之间的转换,并实现对机器人的高速、高精度运动控制的硬件和软件结构的设计.实际应用中,能将机器人语言转换为G代码,通过SERCOS接口实现了对机器人的高速、高精度运动控制.从而为机器人的控制系统设计提供了一个新的可行的方案.     

国内外两足步行机器人研究的历史、现状及发展趋势

本文共分6个部分,详细介绍了研究两足步行机器人的原因和目的,两足步行机器人的特点及其应用前景,国内外两足步行机器人研究的历史、现状及发展趋势.     

七连杆双足机器人建模和控制系统仿真

研究双足机器人稳定性控制问题,步行机器人作为一种载运工具适应地况能力强,结构复杂,运动控制难.实现类人型机器人动态行走,针对行走的稳定性,必须对机器人进行动力学建模、步态设计和稳定姿态控制算法设计.研究了一种七连杆双足机器人的动力学建模和控制系统仿真方法.建立两足步行机器人腿的可参数化仿真模型,对七平面双足机器人的运动情况和控制输入输出进行仿真,得出试验结果.并对影响步行机器人稳定性能的参数进行分析,为后面机器人样机的研制提供理论及数据依据.     

水火弯板智能机器人控制器的设计

针对造船技术中水火弯板工艺的自动化，本文介绍了一种基于SERCOS总线的特种工业机器人 ．在描述水火弯板工艺过程的基础上，文章对机器人的总体结构及其技术特点做了简单介 绍．本文着重分析了基于SERCOS总线的机器人控制器的特点，介绍了层次化、模块化软件 系统的设计．最后，针对实际应用背景，本文提出了测量引导加工的加工方法．     

开放式控制系统中SERCOS总线的接口设计与实现

目前,市场对适合中小批量加工、具有良好柔性和多功能性的制造系统的需求已逐步超过对大型单一功能的制造系统的需求;这一趋势促成了模块化、可重构、可扩充的开放式控制系统的产生;为了推动国内数控系统的开放性进程,对一种开放式运动控制总线一SERCOS总线进行了研究,并通过设计出嵌入到伺服驱动器中的SERCOS接口卡,实现了完整的主从环形通讯;该接口卡软、硬件的成功设计证明了国内有能力研发出具有SERCOS接口的数控产品,将有利于开放式控制系统在中国的发展。     

日本拟人型两足步行机器人研发状况及我见

本文概括地介绍了日本拟人型两足步行机器人的研发状况，五个主要研发小组的成果 和特点，简述了步行机器人研究涉及的八项关键技术．指出智能机器人玩具市场是服务机器 人的一个值得大力开拓的方向．提出了对我国拟人型步行机器人发展战略的建议．     

两足步行椅机器人的机构设计

主要介绍了两足步行椅机器人的整体机构设计．在踝关节和髋关节处，采用了一种新型多自由度正交关节设计，使得机器人结构紧凑，提高了步态规划的精度．利用两足步行椅机器人动力学模型，分析了各关节的驱动力矩，以此确定了驱动电机的功率．在髋关节和座椅之间设计了减震系统，并建立了减震系统模型，分析了减震系统对于步态稳定性的作用．最后，为保证乘坐者的安全，从机构的角度设计了机械式保护装置．     

蛇形机器人控制系统的设计与实现

以蛇为模型的蛇形机器人的研究，扩大了机器人的应用领域．本文基于CAN总线技术完成了蛇形机器人控制系统的设计及研制，有效地实现了机器人的运动控制．在此基础上完成了蛇形机器人的集中式控制和分布式控制方式．     

仿人机器人稳定步行控制研究

介绍了仿人机器人运动控制研究现状,通过对步行机器人稳定性判据ZMP分析,提出通过控制踝关节转动角度来调节ZMP的位置,以保证机器人行走的稳定性．根据模糊控制理论,设计出步行机器人踝关节二维模糊控制系统及模糊控制器．仿真结果表明步行机器人能够通过控制踝侧向关节的相对转动调节ZMP点的位置,实现机器人的稳定步行．     

Experimental realization of dynamic walking of the biped humanoid robot KHR-2 using zero moment point feedback and inertial measurement

This paper describes a novel control algorithm for dynamic walking of biped humanoid robots. For the test platform, we developed KHR-2 (KAIST Humanoid Robot-2) according to our design philosophy. KHR-2 has many sensory devices analogous to human sensory organs which are particularly useful for biped walking control. First, for the biped walking motion, the motion control architecture is built and then an appropriate standard walking pattern is designed for the humanoid robots by observing the human walking process. Second, we define walking stages by dividing the walking cycle according to the characteristics of motions. Third, as a walking control strategy, three kinds of control schemes are established. The first scheme is a walking pattern control that modifies the walking pattern periodically based on the sensory information during each walking cycle. The second scheme is a real-time balance control using the sensory feedback. The third scheme is a predicted motion control based on a fast decision from the previous experimental data. In each control scheme, we design online controllers that are capable of maintaining the walking stability with the control objective by using force/torque sensors and an inertial sensor. Finally, we plan the application schedule of online controllers during a walking cycle according to the walking stages, accomplish the walking control algorithm and prove its effectiveness through experiments with KHR-2.     

Inverse kinematics and inverse dynamics for control of a biped walking machine

Analytical techniques are presented for the motion planning and control of a 12 degree-of-freedom biped walking machine. From the Newton-Euler equations, joint torques are obtained in terms of joint trajectories, and the inverse dynamics are developed for both the single-support and double-support cases. Physical admissibility of the biped trajectory is characterized in terms of the equivalent force-moment and zero-moment point. This methodology has been used to obtain reference inputs and implement the feedforward control of walking robots. A simulation example illustrates the application of the techniques to plan the forward-walking trajectory of the biped robot. The implementation of a prototype mechanism and controller is also described.     

Asymptotically stable walking for biped robots: analysis viasystems with impulse effects

Biped robots form a subclass of legged or walking robots. The study of mechanical legged motion has been motivated by its potential use as a means of locomotion in rough terrain, as well as its potential benefits to prothesis development and testing. The paper concentrates on issues related to the automatic control of biped robots. More precisely, its primary goal is to contribute a means to prove asymptotically-stable walking in planar, underactuated biped robot models. Since normal walking can be viewed as a periodic solution of the robot model, the method of Poincare sections is the natural means to study asymptotic stability of a walking cycle. However, due to the complexity of the associated dynamic models, this approach has had limited success. The principal contribution of the present work is to show that the control strategy can be designed in a way that greatly simplifies the application of the method of Poincare to a class of biped models, and, in fact, to reduce the stability assessment problem to the calculation of a continuous map from a subinterval of R to itself. The mapping in question is directly computable from a simulation model. The stability analysis is based on a careful formulation of the robot model as a system with impulse effects and the extension of the method of Poincare sections to this class of models     

Simultaneous design of optimal gait pattern and controller for a bipedal robot

Nowadays, biped robotics becomes an interesting topic for many control researchers. The biped robot is more adaptable than the other mobile robots in a varied environment and can have more diverse possibilities in planning the motion. However, it falls down easily and its control for stable walking is difficult. Therefore, generation of a desired walking pattern for the biped robot in the presence of some model uncertainties is an important problem. The proposed walking pattern should be also achievable by the designed controller. To achieve this aim and to reach the best control performance, the walking pattern and controller should be designed simultaneously rather than separately. In the present study, an optimal walking pattern is proposed to be tracked by a designed sliding mode controller. In this respect, a genetic algorithm (GA) is utilized to determine the walking pattern parameters and controller coefficients simultaneously. Here, high stability, minimum energy consumption, good mobility properties, and actuator limitations are considered as the important indexes in optimization. Simulation results indicate the efficiency of the proposed scheme in walking the understudy biped robot.     

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.     

一种小样本支持向量机控制器在两足机器人步态控制的研究

神经网络等传统的机器学习方法是基于样本数目无穷大的经验风险最小化原则,这对非确定环境下有限样本的步态学习控制非常不利．针对两足机器人面临的非确定环境适应性难题,提出了一种基于支持向量机（SVM）的两足机器人步态控制方法,解决了小样本条件下的步态学习控制问题．提出了一种基于混合核的步态回归方法,仿真研究表明了这种方法比全局核和局部核分别单独用于步态学习时有优越性．SVM以踝关节及髋关节的轨迹作为输入,相应的满足ZMP判据的上体轨迹作为输出,利用有限的理想步态样本对机器人上体轨迹与腿部轨迹之间的动态运动关系进行学习,然后将训练好的SVM置入机器人控制系统,从而增强了步态控制的鲁棒性,有利于实现两足机器人在非结构环境下的稳定步行．仿真结果表明了所提方法的优越性．     

Development of a leg part of a humanoid robot-design of a biped walking robot having antagonistic driven joints using a nonlinear spring mechanism

The authors are engaged in studies of biped walking robots from the following two viewpoints. One is a viewpoint as a human science. The other is a viewpoint towards the development of humanoid robots. In the current research concerning a biped walking robot, there is no developed example of a life-size biped walking robot with antagonistically driven joints by which the human musculo-skeletal system is imitated in the lower limbs. Humans are considered to exhibit walking behavior which is both efficient and capable of flexibly coping with contact with the outside environment. However, developed biped walking robots cannot realize human walking. The human joint is driven by two or more antagonistic muscle groups. Humans can vary the joint stiffness, using nonlinear spring characteristics possessed by the muscles themselves. The function is an indispensable function for a humanoid. Therefore, the authors designed and built an anthropomorphic biped walking robot having antagonistic driven joints. In this paper, the authors introduce the design method of the robot. The authors performed walking experiments with the robot. As a result, a quasi-dynamic biped walking using antagonist driven joint was realized. The walking speed was 7.68 s per step with a 0.1 m step length.     

A brief review of dynamics and control of underactuated biped robots

Humans as bipeds enjoy certain advantages over other terrestrial systems, which motivate us to study and develop biped robots. Underactuated biped robots adopt the energy efficient gait of the biological counterparts and passive walkers. However, the control design for such robots is challenging due to lesser controllable joints, non-linear hybrid system dynamics and the goal of utilizing the natural dynamics. This paper summarizes various designs, models and control strategies used to enable stable walking and running for the underactuated biped robots. It gives a brief about how the mechanism of such bipeds evolved to incorporate the design variations which significantly improved the system performance. The few basic mathematical models which are used to simulate, analyze and predict the system dynamics and test control designs, are described, highlighting the difference in walking and running models. An introduction to the various stability criteria and control methods, successful in enabling stable walking for the robots on flat or uneven terrains, is provided. This paper gives a brief of the significant achievements in this field and ends with the highlights of the abilities inherent to humans but lacking in underactuated bipeds, and adopting or improving which should be the focus of the future research.     

动态双足机器人的控制与优化研究进展

对动态双足机器人的可控周期步态的稳定性、鲁棒性和优化控制策略的国内外研究现状与发展趋势进行了探讨.首先,介绍动态双足机器人的动力学数学模型,进一步,提出动态双足机器人运动步态和控制系统原理;其次,讨论动态双足机器人可控周期步态稳定性现有的研究方法,分析这些方法中存在的缺点与不足;再次,研究动态双足机器人的可控周期步态优化控制策略,阐明各种策略的优缺点;最后,给出动态双足机器人研究领域的难点问题和未来工作,展望动态双足机器人可控周期步态与鲁棒稳定性及其应用的研究思路.     

A SVM controller for the stable walking of biped robots based on small sample sizes

Conventional machine learning methods such as neural network (NN) uses empirical risk minimization (ERM) based on infinite samples, which is disadvantageous to the gait learning control based on small sample sizes for biped robots walking in unstructured, uncertain and dynamic environments. Aiming at the stable walking control problem in the dynamic environments for biped robots, this paper puts forward a method of gait control based on support vector machines (SVM), which provides a solution for the learning control issue based on small sample sizes. The SVM is equipped with a mixed kernel function for the gait learning. Using ankle trajectory and hip trajectory as inputs, and the corresponding trunk trajectory as outputs, the SVM is trained based on small sample sizes to learn the dynamic kinematics relationships between the legs and the trunk of the biped robots. Robustness of the gait control is enhanced, which is propitious to realize the stable biped walking, and the proposed method shows superior performance when compared to SVM with radial basis function (RBF) kernels and polynomial kernels, respectively. Simulation results demonstrate the superiority of the proposed methods.