动力学在线更新的人和机器人握手智能控制器

为实现人和机器人握手运动的同步, 本文提出了一种基于动力学在线更新的智能控制器. 首先, 建立了人和机器人握手的仿真模型; 然后对智能控制器中有任意吸引子的动力学系统进行多项式近似设计和在线更新设计,使其具备自振动特性和输入输出同步特性; 同时通过忘却系数和负荷系数, 输入输出同步程度能实现参数可调; 最后, 将该智能控制器用于人和机器人握手的控制中, 仿真结果表明了该智能控制器设计的有效性.     

冗余驱动仿下颌运动机器人的机构设计及轨迹规划

为实现人体下颌的运动,针对下颌系统的冗余特性和颞下颌关节运动的独特性,提出一种新型的冗余驱动的仿下颌运动机器人.首先,根据人体下颌运动机理以及仿生设计参数对仿下颌运动机器人进行样机设计.然后,基于虚拟仿真软件,对冗余驱动的仿下颌运动机器人进行轨迹规划.最后,在样机上进行下颌功能运动实验,分别模拟下颌的开闭、前后和侧方运动.结果证明该仿下颌运动机器人能够实现人体下颌的运动,特别是颞下颌关节的运动.     

基于矢量场在线更新的人和机器人身体交流控制

为实现人和机器人身体相互交流时的运动同步,提出基于矢量场在线更新的控制方法,并将此方法应 用于人和机器人握手的研究中．首先,对该方法中任意吸引子的矢量场进行多项式近似设计,使其具备自振动特性; 其次,通过在线更新设计,控制系统具有输入输出同步特性,并且可以通过调节系统中的忘却系数和负荷系数来改 变其同步程度;最后,基于7 自由度机器人臂,将该方法用于人和机器人握手实验中,结果表明了此方法的有效性．     

六足步行机器人的多关节协调控制

根据六足步行机器人的机械结构和关节运动的协调性、准确性的控制要求，确定了混合闭环的伺服结构控制方案。针对机器人步态控制算法给出的数据特点，采用了先对脉冲总数最大的那个关节进行插补，然后按照关节间脉冲总数的比例关系再对其它关节插补的插补算法，实现了机器人各个关节的指令在每一个位控周期内的协调。最后介绍了伺服参数的调整，使各个关节的位置增益一致，保证了各个关节响应时的协调。试验表明，采用该控制方案，通过该插补算法，对伺服系统的参数调整以后的控制系统能够满足机器人关节运动的协调性和准确性的控制要求。     

一处室内轮式自主移动机器人的导航控制研究

介绍了一种室内移动机器人CASIA-I.对该机器人的运动机构做了较为详细地阐述, 针对该运动机构给出了机器人的运动方程和一种导航控制算法,并根据该算法进行了软件仿真 和实物实验.在软件仿真和实物实验两种环境下,机器人都能够实时避开障碍物奔向目标.仿真 和实验表明:该移动平台具有良好的可靠性,且该导航控制算法是一种有效的导航算法.     

机器人的离散自适应控制算法

本文给出了一种适用于多关节液压伺服机器人的递推离散自适应控制算法,该算法易于 用微机实时在线实现.实验表明,其控制作用能显著改善机器人的运动性能.     

七自由度机器人振动抑制的新方法

针对七自由度工业机器人刚性较差,运动过程中容易产生振动且抑制困难的问题,提出了一种控制算法,将时滞整形滤波器与动力学前馈补偿组合在一起,对机器人两个关节采用时滞滤波,并同时采用基于简化动力学模型的前馈补偿来实现机器人振动抑制.     

仿壁虎机器人足端工作空间分析及其实现协调运动的步态规划

基于对壁虎爬行运动的研究,提出一种四足仿壁虎爬壁机器人.对其机械结构、运动学、足端工作空间和越障能力进行了分析,规划了两种爬行步态,并针对实验中出现的过驱动问题进行了分析,设计了一种多关节协调控制算法.实验结果表明,使用该控制算法的机器人运动是协调稳定的,验证了分析结果的正确性和控制算法的有效性.     

柔性关节机器人抓取末端振动自动化控制方法

为了提高柔性关节机器人抓取末端振动控制精度,该文提出柔性关节机器人抓取末端振动自动化控制方法。对柔性关节机器人展开系统动力学分析,构建奇异摄动模型定义柔性关节机器人系统的运动方程。设计柔性关节机器人的优化控制器,构建控制误差代价函数。利用拉格朗日法获取函数最优解,实现柔性关节机器人抓取末端振动自动化控制。实验结果表明,该方法能够有效抑制柔性关节机器人末端振动信号,相位控制误差均在0.02°以内,抑振措施的上升时间低于7.66 s,末端控制精准度高、效率高、性能好。     

微创血管介入手术机器人控制系统与零位定位装置设计

为实现微创血管介入手术机器人对手术工具的精确定位和稳定把持,设计了其控制系统及相应零位定位装置.首先介绍了机器人系统组成和基于PMAC(可编程多轴控制器)的上下位机控制系统构架.同时,设计了5次插值运动规划算法和关节运动的三环PID控制算法,提高了机器人响应速度和稳定性.为了确定机器人运动初始位置,提出了以霍尔传感器为基础,结合电机运动信号的零位找寻方法和装置.实验结果表明,该零位定位装置定位稳定且准确.     

Kick it with elasticity: Safety and performance in human–robot soccer

The RoboCup community has one definite goal [H. Kitano, M. Asada, RoboCup humanoid challenge: That’s one small step for a robot, one giant leap for mankind, in: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS1998, Victoria, pp. 419–424, 1998]: winning against the human world soccer champion team by the year 2050. This implies real tackles and fouls between humans and robots, rising safety concerns for the robots and even more important for the human players. Nowadays, similar questions are discussed in the field of physical human–robot interaction (pHRI), but mainly in the context of industrial and service robotics applications.The first part of our paper is an attempt for a pHRI view on human–robot soccer. We take scenes from real soccer matches and discuss what could have happened if one of the teams consisted of robots instead of humans. The most important result is that elastic joints are needed to reduce the impact during collisions. The second and third part consider conversely, how the robot can handle the impact of kicking the ball and how it can reach the velocity of human-level soccer. Again joint elasticity is the key point.Overall, the paper analyzes a vision far ahead. However, all our conclusions are based on concrete simulations, experiments, derivations, or findings from sports science, forensics, and pHRI.     

An approach to enlarge learning space coverage for robot learningcontrol

In robot learning control, the learning space for executing general motions of multijoint robot manipulators is quite large. Consequently, for most learning schemes, the learning controllers are used as subordinates to conventional controllers or the learning process needs to be repeated each time a new trajectory is encountered, although learning controllers are considered to be capable of generalization. In this paper, we propose an approach for larger learning space coverage in robot learning control. In this approach, a new structure for learning control is proposed to organize information storage via effective memory management. The proposed structure is motivated by the concept of human motor program and consists mainly of a fuzzy system and a cerebellar model articulation controller (CMAC)-type neural network. The fuzzy system is used for governing a number of sampled motions in a class of motions. The CMAC-type neural network is used to generalize the parameters of the fuzzy system, which are appropriate for the governing of the sampled motions, to deal with the whole class of motions. Under this design, in some sense the qualitative fuzzy rules in the fuzzy system are generalized by the CMAC-type neural network and then a larger learning space can be covered. Therefore, the learning effort is dramatically reduced in dealing with a wide range of robot motions, while the learning process is performed only once. Simulations emulating ball carrying under various conditions are presented to demonstrate the effectiveness of the proposed approach     

Experience-based imitation using RNNPB

Robot imitation is a useful and promising alternative to robot programming. Robot imitation involves two crucial issues. The first is how a robot can imitate a human whose physical structure and properties differ greatly from its own. The second is how the robot can generate various motions from finite programmable patterns (generalization). This paper describes a novel approach to robot imitation based on its own physical experiences. We considered the target task of moving an object on a table. For imitation, we focused on an active sensing process in which the robot acquires the relation between the object's motion and its own arm motion. For generalization, we applied the RNNPB (recurrent neural network with parametric bias) model to enable recognition/generation of imitation motions. The robot associates the arm motion which reproduces the observed object's motion presented by a human operator. Experimental results proved the generalization capability of our method, which enables the robot to imitate not only motion it has experienced, but also unknown motion through nonlinear combination of the experienced motions.     

A generalized control framework of assistive controllers and its application to lower limb exoskeletons

Various control methods have been studied for the natural assistance of human motions by exoskeletal robots, i.e., wearable robots for assisting the human motions. For example, impedance control and compliance control are widely used for controlling interaction forces between a human and a robot. When an accurate measurement of the human muscular force is available (e.g., electromyography), a direct use of the estimated human joint torque is possible in the control of an assistive robot. The human motions in a daily living, however, are so complex that they are constituted by multiple phases, such as walking, sitting, and standing, where the walking can be further categorized into multiple sub-phases. Therefore, a single control method cannot be the best option for all the motion phases; a switch in the control algorithms may be necessary for assisting human movements in multiple motion phases. In this paper, a generalized control framework is proposed to incorporate the various assistive control methods in one general controller structure, which consists of Feedforward Disturbance Compensation Control, Reference Tracking Feedback Control, Reference Tracking Feedforward Control, Model-based Torque Control. The proposed control framework is designed taking into consideration of the linearity of each control algorithm, and thus it enables the continuous and smooth switching of assistive control algorithms, and makes it possible to analyze the stability of the overall control loop. The proposed method is implemented into a lower-limb exoskeleton robot and is verified by experimental results.     

混沌神经动力学行为在多自由度机器人上的应用研究

把高维的混沌神经动力学行为应用到多但有限自由度的机器人以实现适应性控制是一个困难的问题;借鉴大量神经元控制少数肌肉的生物事实,一种简单的神经编码方法被用来使高维的神经网络模式转化成了低维的运动参数;虽然只在神经网络中嵌入了3种简单的姿势动作,但是在混沌神经动力学行为出现时,机器人手臂呈现出复杂的组合运动;利用这一点,提出了一个简单的控制算法用来解决病态问题(不一定有解或者确定的解无法保证的问题);实装实验进一步表明,尽管只有粗略甚至不确定的光源信息,利用提出的控制算法,机器人手臂可以成功的寻找到光源。     

Real time implementation of CTRNN and BPTT algorithm to learn on-line biped robot balance: Experiments on the standing posture

This paper describes experimental results regarding the real time implementation of continuous time recurrent neural networks (CTRNN) and the dynamic back-propagation through time (BPTT) algorithm for the on-line learning control laws. Experiments are carried out to control the balance of a biped robot prototype in its standing posture. The neural controller is trained to compensate for external perturbations by controlling the torso’s joint motions. Algorithms are embedded in the real time electronic unit of the robot. On-line learning implementations are presented in detail. The results on learning behavior and control performance demonstrate the strength and the efficiency of the proposed approach.     

Minimizing Human-Exoskeleton Interaction Force Using Compensation for Dynamic Uncertainty Error with Adaptive RBF Network

A critical issue in the control of exoskeleton systems is unknown nonlinear dynamic properties of the system. The improper estimation of those unknown properties can cause considerable human-exoskeleton interaction force during human’s movements. It is really challenging to exactly estimate the parameters of dynamic models. In this paper, we propose a novel exoskeleton control algorithm to both compensate for the dynamic uncertainty error and minimize the human-exoskeleton interaction force. We have built a virtual torque controller based on dynamic models of a lower exoskeleton and have used an approximation of a Radial Basis Function (RBF) neural network to compensate for the dynamic uncertainty error. By doing so, we avoid using complicated force sensors installed on the human-exoskeleton interface and minimize the physical Human-Robot Interaction (pHRI) force. Moreover, we introduce the prototype of our exoskeleton system, called ‘PRMI’ exoskeleton system. Finally, we validated the proposed algorithm on this system, and the experimental results show that the proposed control algorithm provides a good control quality for the ‘PRMI’ exoskeleton system by compensating for dynamic uncertainty error.     

一种新型人工神经元振荡器的设计与应用

神经元振荡器在动物神经系统感知、运动、记忆等方面发挥重要作用，设计人工神经元振荡器不但可用研究生物神经元振荡器机理，而且可用于设计仿生机器人的仿生神经网络控制系统。本文首先提出了一种新型人工神经元振荡器，该振荡器由两个神经元构成，神经元自身存在非线性反馈联接，两者之间为线性突触联接。然后证明了其存在稳定的、近似圆形的极限环，振荡的收敛速度、幅度和频率分别由动力学方程中的三个参数独立控制。最后介绍了采用这种振荡器设计的鱼形机器人新型仿生神经网络控制系统，该控制系统可以实现对包括启停、稳定游动等动作的良好控制。     

Physical human-robot interaction estimation based control scheme for a hydraulically actuated exoskeleton designed for power amplification

We proposed a lower extremity exoskeleton for power amplification that perceives intended human motion via humanexoskeleton interaction signals measured by biomedical or mechanical sensors, and estimates human gait trajectories to implement corresponding actions quickly and accurately. In this study, torque sensors mounted on the exoskeleton links are proposed for obtaining physical human-robot interaction (pHRI) torque information directly. A Kalman smoother is adopted for eliminating noise and smoothing the signal data. Simultaneously, the mapping from the pHRI torque to the human gait trajectory is defined. The mapping is derived from the real-time state of the robotic exoskeleton during movement. The walking phase is identified by the threshold approach using ground reaction force. Based on phase identification, the human gait can be estimated by applying the proposed algorithm, and then the gait is regarded as the reference input for the controller. A proportional-integral-derivative control strategy is constructed to drive the robotic exoskeleton to follow the human gait trajectory. Experiments were performed on a human subject who walked on the floor at a natural speed wearing the robotic exoskeleton. Experimental results show the effectiveness of the proposed strategy.