Visual feedback control of a robot in an unknown environment (learning control using neural networks)

In this paper, a visual feedback control approach based on neural networks is presented for a robot with a camera installed on its end-effector to trace an object in an unknown environment. First, the one-to-one mapping relations between the image feature domain of the object to the joint angle domain of the robot are derived. Second, a method is proposed to generate a desired trajectory of the robot by measuring the image feature parameters of the object. Third, a multilayer neural network is used for off-line learning of the mapping relations so as to produce on-line the reference inputs for the robot. Fourth, a learning controller based on a multilayer neural network is designed for realizing the visual feedback control of the robot. Last, the effectiveness of the present approach is verified by tracing a curved line using a 6-degrees-of-freedom robot with a CCD camera installed on its end-effector. The present approach does not necessitate the tedious calibration of the CCD camera and the complicated coordinate transformations. This revised version was published online in October 2004 with a correction to the issue number.     

Wall-following control of a two-wheeled mobile robot

Wall-following control problem for a mobile robot is to move it along a wall at a constant speed and keep a specified distance to the wall.This paper proposes wall-following controllers based on Lyapunov function candidate for a two-wheeled mobile robot (MR) to follow an unknown wall. The mobile robot is considered in terms of kinematic model in Cartesian coordinate system. Two wall-following feedback controllers are designed: full state feedback controller and observer-based controller. To design the former controller, the errors of distance and orientation of the mobile robot to the wall are defined, and the feedback controller based on Lyapunov function candidate is designed to guarantee that the errors converge to zero asymptotically. The latter controller is designed based on Busawon’s observer as only the distance error is measured. Additionally, the simulation and experimental results are included to illustrate the effectiveness of the proposed controllers.     

A robust control with a neural network structure for uncertain robot manipulator

A robust position control with the bound function of neural network structure is proposed for uncertain robot manipulators. The uncertain factors come from imperfect knowledge of system parameters, payload change, friction, external disturbance, and etc. Therefore, uncertainties are often nonlinear and time-varying. The neural network structure presents the bound function and does not need the concave property of the bound function. The robust approach is to solve this problem as uncertainties are included in a model and the controller can achieve the desired properties in spite of the imperfect modeling. Simulation is performed to validate this law for four-axis SCARA type robot manipulator.     

机器人在先进制造业应用与研究

柔性制造、计算机集成、精益生产、虚拟制造等是先进制造业的发展趋势,机器人技术在先进制造系统中的地位和作用十分重要,但是目前在机器人向先进制造系统集成方面有一些问题需要突破.     

水火成型机器人Y轴横梁有限元应力计算与分析

对船体外板水火成型机器人的核心部件———Y轴横梁进行了有限元应力计算和分析。首先采用三维十节点四面体结构实体单元计算了各种典型工况下Y轴横梁的应力分布情况 ,然后对其分布规律作了简要分析 ,为后续优化设计和误差补偿提供了理论依据。     

基于无线网络的移动机器人远程控制系统设计与实现

在无线局域网中建立一套以移动机器人为控制对象,上位PC机为主控制端的控制系统。通过可视化的人机交互界面,向远程机器人发送控制指令并接受到反馈信息,采用TCP/IP协议中的Socket(套接字)编程实现。同时为机器人加入视觉和超声波传感器,使之能在人的操控下半自主移动。最终开发成品使之具有操作简单,易于扩展,移植性强等特点。     

6自由度机器人奇异形位分析及仿真

通过对6R机器人奇异形位形成条件的分析，把机器人奇异形位分为结构边界奇异、边界奇异、内部奇异和腕部奇异四种情况。在对边界奇异进行了详细分析的基础上，对每一情况给出了计算机仿真结果，获得了直观的边界奇异表达。     

A QP Artificial Neural Network inverse kinematic solution for accurate robot path control

In recent decades, Artificial Neural Networks (ANNs) have become the focus of considerable attention in many disciplines, including robot control, where they can be used to solve nonlinear control problems. One of these ANNs applications is that of the inverse kinematic problem, which is important in robot path planning. In this paper, a neural network is employed to analyse of inverse kinematics of PUMA 560 type robot. The neural network is designed to find exact kinematics of the robot. The neural network is a feedforward neural network (FNN). The FNN is trained with different types of learning algorithm for designing exact inverse model of the robot. The Unimation PUMA 560 is a robot with six degrees of freedom and rotational joints. Inverse neural network model of the robot is trained with different learning algorithms for finding exact model of the robot. From the simulation results, the proposed neural network has superior performance for modelling complex robot’s kinematics.     

基于运动控制卡的五自由度机器人控制系统的开发

针对一个五自由度的机械手,设计了基于运动控制卡的控制系统.该系统控制采用的"PC+运动控制卡'开放式方案,利用运动PCI1243U运动控制卡产生脉冲和方向信号.通过Microsift VisualB编辑界程序,实现运动学计算,计算出关节坐标,调用控制卡中的运动函数库,实现对机器人运动实现控制.     

Distributed manufacturing control of printed circuit boards using map

This paper presents a distributed printed-circuit-board (PCB) manufacturing control system using UNIX computers connected to a MAP local-area network. The requirements and methodology for data distribution and control are illustrated in the context of the PCB production environment, and the implementation issues of tool-operator communication, error-recovery techniques and shopfloor real-time data feedback are discussed. The interfacing of the system with a production scheduler and a CAD manufacturing workstation is also investigated.     

电磁诱导式喷雾机器人导航控制设计

利用电磁感应技术对喷雾机器人的导航控制系统进行了设计,提出了一种基于PD控制的四轮独立驱动、实时跟踪诱导的控制算法.该算法能够使喷雾机器人在转弯时自动调整行走速度和路线.运行试验表明,算法程序稳定、可靠,满足了喷雾机器人的功能要求.     

管道横孔去毛刺机器人系统的设计

根据仿生学原理,提出了一种蠕动式爬行的管外机器人.详细介绍了其机械本体结构和工作原理.同时,给出了光电耦合输出信号电路、输入信号放大电路以及SPEC061A型单片机控制系统总体框图和控制程序流程图.     

遗传算法在工业机器人控制中应用研究

在构建工业机器人实验硬件系统的基础上,对机器人控制技术进行深入的研究,忽略机器人关节间的耦合,设计单关节的PID控制器.为获取最优的控制效果,利用遗传算法进行PID参数的自整定.最后,对所设计的PID控制器进行了仿真试验,结果表明,与单纯的PID控制相比,基于遗传算法的PID控制器响应速度快,基本无超调,可满足实际作业的要求.     

Development of a new 5 dof mobile robot arm and its motion control system

In this paper, a new revolute mobile robot arm with five degree of freedom (d.o.f) was developed for autonomous moving robots. As a control system for the robot arm, a distributed control system composed of the main controller and five motor controllers for arm joints was developed. The main controller and the motor controllers were developed using the ARM microprocessor and the TMS320c2407 microprocessor, respectively. A new trajectory tracking algorithm for the motor controllers was devised employing pre-generated off-line trajectory data. Also, a 3-D simulator based on the OpenGL software to simulate the motion of the robot arm was developed. To validate the performance of the robot system, experiments to track a specified trajectory were performed.     

基于神经网络的机器人位置逆解

本文运用神经网络求解机器人运动学位置逆解。通过深入分析基于Ｋｏｈｏｎｅｎ网络原理和Ｗｉｄｒｏｗ－Ｈｏｆ误差修正的Ｍ．Ｒ．Ｓ．自组织神经网络及机器人运动学特性，创新了自组织神经网络训练算法。对ＰＵＭＡ５６０机器人的计算机仿真结果表明：本算法在自组织能力和定位控制精度方面有大幅度提高。     

Design of a high-impact survivable robot

This paper presents the design, construction, and testing of a two-wheeled low-cost mobile robot platform that has high survivability when subjected to large impact forces and general rough handling. The design of the drive transmission system and integrated suspension system is developed, along with general equations of motion describing their dynamics. Analyses were conducted to insure stability of the various subsystems and optimize parameters for the desired vibration characteristics. Equations of motion were also developed to describe the rocking chassis phenomenon inherent to the two-wheeled design. A flywheel compensation scheme which helps eliminate the rocking chassis problem is also outlined. An impact analysis combining theory and empirical data was used to predict the survivability threshold. Finally, three series of experiments were conducted, with the first two followed by design improvements. In contrast to currently available commercial robots, our new design employs a flexible mechanical platform capable of absorbing energy during high load impacts. This design was substantiated during the final tests when the robot survived a third story drop without any damage.     

Neural network controller for robotic motion control

Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations.     

基于模糊自整定PD控制器的机器人计算力矩控制

设计了应用于计算力矩控制方案中的模糊PD控制器(FLC),进一步提出了模糊自整定PD控制器(SFLC).用带有末端执行器的平面3自由度机器人为例进行了动力学仿真,并将采用固定PD控制器、模糊PD控制器和模糊自整定PD控制器的计算力矩控制方案进行了比较.仿真结果表明,采用模糊自整定PD控制器(SFLC)后能有效的克服模型不确定所造成的影响,得到比较小的轨迹跟踪误差,为机器人的实际控制提供了理论基础.     

Development of anthropomorphic robot hand SKK robot hand I

In this paper, a three-fingered anthropomorphic robot hand, called SKK Robot Hand I, is presented. By employing a two-DOF joint mechanism, called Double Active Universal Joint (abbreviated as DAUJ from now on) as its metacarpal joint, the hand makes it possible to mimic humanlike motions. We begin with addressing the motivation of the design and mention how the anthropomorphic feature of a human is realized in the design of SKK Hand I. Also, the mechanism of the hand is explained in detail, and advantages in its modular design are discussed. The proposed hand is developed for use as a testbed for dextrous manipulation. It is expected to resolve the increasing demand for robotic applications in unstructured environments. We describe its hardware construction as well as the controller structure including the preliminary results of experiments.     

一种蠕动式管内机器人的研制

这里对一种采用超越方式行走的蠕动式管内机器人进行了研究,分析了机器人的超越行走原理和自锁条件,介绍了其机构和控制系统。实验结果表明,该机器人具有良好的运行状态和应用价值。