一种微小型嵌入式图像处理平台的研究

微小型自重构移动机器人自主对接方法的研究是一个较新的方向,针对微小型自重构机器人系统的要求,研究了一种基于DSP ADSP-BF533和CMOS数字摄像头VS6524的微小型嵌入式图像采集和处理平台,用于机器人对接引导;并在实验中实现了对自重构微小型机器人对接部件特征的识别,验证了平台的可靠性。     

面向微小型机器人的5.8GHz微波能量传输系统

提出了面向微小型机器人的5.8 GHz 微波能量传输系统,采用Ritz-Galerkin（RG）方法建立了微波能 量传输系统射频整流电路模型,分析了整流二极管的整流性能,得到了输入功率与输出电压、整流效率与负载比之 间的关系．设计了八木振子天线,提高了能量接收的定向性．最后搭建了微小型机器人微波能量传输实验系统,开 展了利用微波传输能量驱动微机器人的实验,验证了利用微波能量传输系统为微小型机器人供能的可行性．     

一种新型管道内微机器人的研究

本文描述了一种微管道机器人的结构与控制,分析 了这种微管道机器人的移动原理,该机器人借用仿生学原理,结构独特、简单、新颖,运行 速度快,能方便地实现前进和后退,可以在各种形状的弯管内运行．具有自学习功能,实现 了智能控制．该机器人的直径为8mm,最小长度为16mm,最高运动速度为40mm/s,牵引力大于 0.5N．该机器人可用于核电站航天飞机等其他特殊领域的微小型管道的检查,维护．     

毫米级全方位移动微型装配机器人设计、运动学分析与控制

介绍了一种用于微型工厂的毫米级移动微装配机器人,其具有独特的全方位运动结构．微机器人由4个直径3 mm的电磁微马达驱动,并装备有一对微型夹钳．通过分析运动学矩阵的秩,证明了微机器人的全方位特性,并建立了微夹钳的运动学方程．设计了基于计算机视觉的微机器人控制系统,给出了微机器人定位和驱动方法．实验证明了微机器人的负载能力、机动性以及控制系统的有效性．     

群体自主微小型移动机器人的合作

单个微小型机器人由于自身能力的限制，因此必须多个机器人联合起来才可以完 成指定的任务，所以机器人之间的协作在微操作领域就显得尤其重要。该文利用增强式的 学 习方法，使得微小型机器人具有一定的学习能力，增强了对不确定环境的适应性，并采 用了 一种基于行为的群体自主式微小移动机器人的协作结构，用于机器人的故障排除，仿 真结果 说明了这种体系结构的有效性。     

一种FPGA的嵌入式可重构机器人控制系统

提出一种基于FPGA的可重构嵌入式微处理器控制系统.在FPGA中嵌入两个Nios II 软核,用VHDL语言编写用户自定义组件.在一个由Nios II软核组成的处理器上实现PWM信号生成、编码器信号处理以及多电机同步伺服运算等,在另一个处理器实现机器人任务管理.该控制系统针对微小型爬壁机器人的控制系统设计,不仅具有良好的实时多任务处理能力,而且具有可重构的特点,因而可应用于一类微小型机器人控制系统以提高其设计的灵活性.     

舵桨联合操纵微小型水下机器人的开发

介绍了一种舵桨联合操纵的微小型水下机器人．该机器人装有左右布置的两个主推进器、可调攻角水平舵和垂直稳定翼．配合所搭载的深度计、磁罗经等传感器和控制计算机，可实现水下定深定向航行，并可由TV和声纳探测水下目标．其航行与探测能力得到了试验验证．     

微型全方位移动机器人的研制

本文介绍了采用2毫米电磁型微马达作为驱动器的移动微型机器人．其整体尺寸为10mm×6mm ×5mm．作为微型机器人的核心部件,微马达采用电磁型轴向磁通结构以获得较大的输出力 矩．该马达的设计创新还在于其控制上可以在不同的阶段采用同步电机和步进电机两种 控制方式．微型机器人的控制器通过超细直径的柔性导线与机械结构相连,创新的结构设计 使得机器人的转弯半径非常小,将导线连接的影响降至极小．通过算法可以控制该机器人前 进、后退、灵活转弯．本文详细论述了该微机器人的设计、制作、结构部件和性能．     

微小型自主水下机器人研究现状

综述了国内外微小型自主水下机器人（AUV）的研究现状。首先，对微小型AUV的定义进行了梳理。其次，基于微小型AUV的平台结构特征，对微小型AUV的应用背景和特征进行了归纳和总结。最后，对微小型AUV的发展趋势进行了分析和探讨。为微小型AUV的设计提供了参考，对微小型AUV的应用发展具有借鉴意义。     

基于万向式关节的模块化自重构机器人

提出了一种基于万向式关节的模块化自重构机器人——UBot．该机器人模块结构紧凑、刚性好、运动灵活,具有运动、 重构和处理任务的能力．它由许多标准模块组成,模块均采用万向式结构的正立方体外形,具有4个可以与相邻模块连接\/断开的连接面． 设计了钩爪式连接机构,它可以快速可靠地与相邻模块连接/断开,该连接机构连接后具有自锁功能,节省能量．设计了 模块电气系统．最后进行了连接机构和机器人运动实验,证明了UBot系统的可靠性和运动灵活性．     

Fuzzy-neuro position/force control for robotic manipulators with uncertainties

The performance of a controller for robot force tracking is affected by the uncertainties in both the robot dynamic model and the environmental stiffness. This paper aims to improve the controller’s robustness by applying the neural network to compensate for the uncertainties of the robot model at the input trajectory level rather than at the joint torque level. A self-adaptive fuzzy controller is introduced for robotic manipulator position/force control. Simulation results based on a two-degrees of freedom robot show that highly robust position/force tracking can be achieved, despite the existence of large uncertainties in the robot model.     

A learning particle swarm optimization algorithm for odor source localization

This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.     

基于多传感器融合的全地形机器人系统设计

针对因强降雨、堤防溃决、暴雨增水等因素导致的水位突然上升而泛滥和山洪暴发,形成复杂多变灾后的地形环境。设计了以FPGA为控制器的多传感器融合机器人,提高灾后救援效率。该机器人通过GPS为机器人作业划定区域,生命特征仪、力矩仪和空气质量仪等传感器采集环境数据,搭建非线性全地形机器人动态模型,利用六轴陀螺仪和霍尔传感器获取机器人状态,数据经过扩展卡尔曼滤波算法融合以及航迹算法推算后,获得机器人在灾后环境中的实际信息,使得机器人能够按要求进行搜救作业。实验结果表面,多传感器融合的机器人系统,能够在灾后环境完成信息采集与传输,具有较高的稳定性及准确性。     

Design of a teaching pendant program for a mobile shipbuilding welding robot using a PDA

Teaching pendant is a handheld device by which a human can control a robot. The main functions of a teaching pendant are moving the robot, teaching it about the locations, running robot programs, and jogging the axes. A teaching pendant is usually connected to the robot by a cable. The cable connection and the size of the teaching pendant generally do not pose a problem when the robot controller is separate from the robot. However, a large teaching pendant connected by a cable is not suitable for a self-propelled mobile robot with an internal controller. This paper describes the communication network of a personal data assistant (PDA) as a wireless teaching pendant for a mobile shipbuilding welding robot with embedded controller system that welds and moves autonomously inside the double hull structure of a ship. A double hull is a closed structure that has only a few access holes. It is very difficult and dangerous to weld components inside a double hull structure because of fumes, poisonous gas, and high temperatures. Using a wireless teaching pendant has the following advantages: (1) there are no limits to the welding activities that can take place, (2) the safety level increases because no workers are in close proximity to the robot, (3) workers are far away from the dangerous environmental conditions, (4) it is possible to reduce the weight of the cable connected to the robot, and (5) it is possible to reduce the weight of the robot because of the reduced load of the teaching pendant and the cable. We demonstrate the functionality and performance capabilities of our wireless teaching pendant through field-testing experiments.     

A subsumptive, hierarchical, and distributed vision-based architecture for smart robotics

We present a distributed vision-based architecture for smart robotics that is composed of multiple control loops, each with a specialized level of competence. Our architecture is subsumptive and hierarchical, in the sense that each control loop can add to the competence level of the loops below, and in the sense that the loops can present a coarse-to-fine gradation with respect to vision sensing. At the coarsest level, the processing of sensory information enables a robot to become aware of the approximate location of an object in its field of view. On the other hand, at the finest end, the processing of stereo information enables a robot to determine more precisely the position and orientation of an object in the coordinate frame of the robot. The processing in each module of the control loops is completely independent and it can be performed at its own rate. A control Arbitrator ranks the results of each loop according to certain confidence indices, which are derived solely from the sensory information. This architecture has clear advantages regarding overall performance of the system, which is not affected by the "slowest link," and regarding fault tolerance, since faults in one module does not affect the other modules. At this time we are able to demonstrate the utility of the architecture for stereoscopic visual servoing. The architecture has also been applied to mobile robot navigation and can easily be extended to tasks such as "assembly-on-the-fly."     

Emergent synthesis of motion patterns for locomotion robots

Emergence of stable gaits in locomotion robots is studied in this paper. A classifier system, implementing an instance-based reinforcement-learning scheme, is used for the sensory-motor control of an eight-legged mobile robot and for the synthesis of the robot gaits. The robot does not have a priori knowledge of the environment and its own internal model. It is only assumed that the robot can acquire stable gaits by learning how to reach a goal area. During the learning process the control system is self-organized by reinforcement signals. Reaching the goal area defines a global reward. Forward motion gets a local reward, while stepping back and falling down get a local punishment. As learning progresses, the number of the action rules in the classifier systems is stabilized to a certain level, corresponding to the acquired gait patterns. Feasibility of the proposed self-organized system is tested under simulation and experiment. A minimal simulation model that does not require sophisticated computational schemes is constructed and used in simulations. The simulation data, evolved on the minimal model of the robot, is downloaded to the control system of the real robot. Overall, of 10 simulation data seven are successful in running the real robot.     

Reactive robot system using a haptic interface: an active interaction to transfer skills from the robot to unskilled persons

This paper is concerned with the reactive robot system (RRS) which has been introduced as a novel way of approaching human–robot interactions by exploiting the capabilities of haptic interfaces to transfer skills (from the robot to unskilled persons). The RRS was implemented based on two levels of interaction. The first level, which implements the first two stages of the learning process, represents the conventional control way of interchanging a set of forces in response to a static read of the contact position of some pre-defined dynamic rules (passive interaction). The second level, which implements the last stage of the learning process, represents an enhanced way of interaction between haptic interfaces and humans. This level adds to robotic system a degree of intelligence which enables the robot to dynamically adapt its behavior depending on user wishes (active interaction). In particular, in this paper, the implementation of the second level of the RRS is described in detail. A set of experiments was performed, applied to Japanese handwriting, to verify if second level of the RRS can interact with humans during the autonomous stage of the learning process. The results demonstrated that our system can still provide assistance to users on the autonomous stage while mostly respecting their intentions without significantly affecting their performance.     

油罐检测爬壁机器人结构与控制系统设计

研究智能爬壁机器人检测技术及其系统 ,提出了机器人在大型垂直罐壁移动作业的路径规划方法 ,并分析了抗倾覆机构的作用 ,设计给出了机器人总体结构和控制系统 .系统以磁吸附爬壁机器人为运动载体 ,采用非接触式无损检测技术 ,并配备多种传感器 ,具备较高的智能化水平 .现场实验表明 ,该系统自动化程度高、运动稳定可靠、定位精度高 ,大幅度提高了检测效率 .     

弧焊机器人焊缝跟踪神经网络控制器

介绍了一种能提高高弧焊机器人焊缝跟踪精度的神经网络控制器,通过神经网络的补偿作用,弥补了由于无法知道机器人精确模型所造成的控制上的误差,不同于机器人控制中传统的网络控制器,本文提出并应用了基于笛卡尔空间轨迹控制的机器人焊缝跟踪神经网络,大大简化了控制算法,计算机模拟及实验表明,该控制器非常适用于只的实际焊接,对于现有机器人,无须改变其控制器内部结构,即可应用该技术,与常用的机器人关节力矩控制法相比     

External force control of an industrial puma 560 robot

Deformations occurring in a robot working in contact with a rigid environment is a real problem in the industrial world. This problem can be solved by taking into account forces undergone by the robot at the end-effector level. The first part of this article is aimed at determining a force control scheme that satisfies this constraint and that can be implemented on a non-modified industrial robot controller. Various existing force control schemes are investigated and the reasons for discarding them are given. Then, the emphasis is put on a so-called external force control scheme, which seems to be a solution to our problem. The control law appearing in such a scheme is determined by means of a realistic robot simulator developed on a SUN workstation. A simple integral term on the force error gives acceptable results in various robot configurations. This is illustrated in the form of graphs. In a second part, the implementation of this external control scheme in a real PUMA 560 robot's UNIMATE controller is presented. Certain software issues and implementation solutions are pointed out. Several experiments are described. Once again, graphs are used to show the experimental results. © 1994 John Wiley & Sons, Inc.