一种机器人的寻迹算法

针对基于光电传感器组寻迹的自动导引机器人,设计了传感器阵列的布置方式。根据此布置方式,提出了三个处理规则结合而成的轨迹识别算法。即使在导引线复杂的情况下,用此算法也能得出行进方向,控制机器人沿轨迹运动。甚至遇到干扰走错时,机器人也能自动纠错。     

基于多数据融合的智能定位传感器避障算法研究

在智能定位传感器内增加避障算法，可使机器人拥有自动躲避障碍的能力，该文基于多数据融合设计智能定位传感器避障算法。设置激光雷达测距和超声波测距作为多传感器障碍检测的方法，获取机器人当前位置与障碍点坐标的相对几何关系，计算机器人与障碍点位置的距离，定位路面障碍点，对2种传感器收集到的数据进行多元障碍定位信息的加权融合。设置智能机器人避障轨迹目标函数以及约束条件，设计机器人避障算法，得到基于定位传感器的机器人避障方法。实验结果表明，在简单环境及复杂环境下机器人均未与障碍物相撞。在运行轨迹中随机放置障碍物，机器人能够及时完成运行轨迹的变化。由此可见，该避障算法具备较好的应用前景，可应用于各种智能机器人中。     

会走迷宫的机器人——控制系统的设计

走迷宫机器人主要是基于自动导引小车(AGV——auto—guided Vehicle)的原理,实现小车识别路线.判断并自动规避障碍.选择正确的行进路线。导引方式采用与地面颜色有较大差别的导引线.使用反射式光电传感器感知导引线.障碍判断采用机械式传感器。驱动电机采用直流电机.电机控制方式为单向PWM开环控制。控制     

自动清扫机器人

设计思想 日本静甲株式会社的清水工厂开发出一种自动清扫机器人,它可用于各种工厂中的清扫工作。以往的自动导引车辆(AGV)采用了不同的控制方式,例如电磁导引,磁气导引,反射式光学导引等方式,尽管它们的控制方式不同,但都要求地面上要有相应的配套施工。而该机器人则采用了光纤陀螺来自主控制机器人的方向,对地面没有任何要求。     

信息融合在移动机器人轨迹模糊跟踪中的应用

在机器人轨迹跟踪过程中,机器人自动跟踪的精度直接影响跟踪效果;以3自由度移动机器人为研究对象研究了机器人轨迹模糊跟踪系统,且在该系统中,采用多个传感器同时对移动机器人进行跟踪检测,并利用融合算法对其进行融合,将融合后的结果作为模糊控制器的输入;计算机仿真结果表明,在3自由度移动机器人轨迹跟踪中,采用多传感器信息融合是合理的、可行的;且可以减少跟踪过程中由传感器引起的误差对跟踪精度的影响,提高控制精度.     

单片机控制步进电机-自动循迹行驶

本文主要是根据类似于医院里自动送药机器人的运行方式而设计的。预先在地面上画好小车所要行驶的轨迹,通过传感器把轨迹位置传回CPU处理,然后控制小车沿轨迹行驶,系统监督可靠,效率高。可广泛应用于各行业的自动控制系统。     

任务空间实时轨迹规划的旋量方法

：基于旋量提出了一种任务空间实时轨迹规划的新算法．该算法借鉴了机器人制导的设计思想,以 空间机器人目标捕获为应用背景,可以仅依靠视线测量信息实现多种约束条件下的轨迹规划．首先给出了矢 量的旋量表示方法,在此基础上设计了一种类似于比例导引的新型三维制导律．应用新型三维制导律计算出 运动的法向加速度,并根据安全性的要求计算出线加速度;两者结合完成了轨迹生成的实时算法．生成的轨 迹安全可行,且具有一定的优化特性．仿真结果证明了该规划算法的有效性．     

基于指尖力传感器的HIT 机器人灵巧手笛卡尔阻抗控制

介绍了基于5维指尖力／力矩传感器的HIT机器人灵巧手的笛卡尔阻抗控制．当手指与障碍物接触时，呈现出2阶机械阻抗特性．在阻抗控制算法中，不需直接计算加速度，从而避免了因大加速度误差给控制带来的不利因素．结合期望轨迹和实际位置及速度，产生一个参考轨迹，手指跟踪此参考轨迹即可自动获得期望阻抗特性．     

基于WSAN的AGVS控制系统模型研究

给出了一种基于无线传感反应网络的自动导引搬运车系统的结构,研究了该系统的控制模型,提出了把无线传感器网络作为控制系统观测器的方法,在此模型基础上讨论了传感器节点模型和定位算法,最后制定了分布式动态路径规划策略。     

红外线传感器及其导引技术在自动导向车中的应用研究

在保证自动导向车导引精度的前提下，如何构筑一种性能优异、成本低廉的传感器测控系统是自动导向车技术的研究热点和难点。现以普通红外线传感器作为自动导向车信号采集的核心器件，并采用优化阵列式分布，构成了一种功能突出、性能稳定、结构简单、导引准确、价格低廉的光电眼，进而通过系统微处理器的分析和处理，排除了环境干扰，强化了测控功能，使自动导向车获得良好的导引效果，可确保基于此技术的自动导向车具有优越的性价比和实用性。     

基于FPGA+ARM的视觉导航轮式机器人

根据自动运输的需求,以FPGA+ARM为核心,设计一种基于视觉导航的自主式轮式机器人,使用FPGA控制图像的实时采集、存储和显示,用ARM实现路径识别、通信并控制执行机构和传感器。路径识别的基本思想是采样二值化并去噪,再检测出场景中的路径,由路径跟踪模块进行导航计算。实验结果表明,该机器人的控制准确可靠,能正确地跟踪预先设置的引导轨线。     

复杂路线下机器人的三点三轮寻迹系统

针对寻迹时的复杂路线,设计一种简单的机器人。该机器人以AT89C52单片机为控制芯片,使用3个自制的红外光电传感器来辨别路线,具有设计简单、成本较低等特点。测试表明,该机器人能顺利地完成复杂路线下的寻迹,其双级转弯的设计使得寻迹更为准确。     

Integrated tracking and accident avoidance system for mobile robots

In the intelligent transportation field, various accident avoidance techniques have been applied. One of the most common issues with these is the collision, which remains an unsolved problem. To this end, we developed a Collision Warning and Avoidance System (CWAS), which was implemented in the wheeled mobile robot. Path planning is crucial for a mobile robot to perform a given task correctly. Here, a tracking system for mobile robots that follow an object is presented. Thus, we implemented an integrated tracking system and CWAS in a mobile robot. Both systems can be activated independently. Using the CWAS, the robot is controlled through a remotely controlled device, and collision warning and avoidance functions are performed. Using the tracking system, the robot performs tasks autonomously and maintains a constant distance from the followed object. Information on the surroundings is obtained through range sensors, and the control functions are performed through the microcontroller. The front, left, and right sensors are activated to track the object, and all the sensors are used for the CWAS. The proposed system was tested using the binary logic controller and the Fuzzy Logic Controller (FLC). The efficiency of the robot was improved by increasing the smoothness of motion via the FLC, achieving accuracy in tracking and increasing the safety of the CWAS. Finally, simulations and experimental outcomes have shown the usefulness of the system.     

基于决策树的羽流追踪机器人自主决策方法研究

针对无法获得可靠羽流流向信息不利于实现羽流追踪的问题，提出了一种基于决策树的羽流追踪移动机器人自主决策方法。该方法通过移动机器人两侧的浓度传感器采集到的浓度信息，利用追踪的行为规则建立决策树模型，获得行为决策信息，使机器人高效地追踪到羽流并精确地定位。由于浓度变化关系蕴含了羽流的流向及流速信息，从而取代了传统方法中流向及流速传感器。在扩散环境下，通过移动机器人羽流追踪实验，实现了良好的源定位效果。     

Robust oscillation control of wheeled mobile robots

In cases where a wheeled mobile robot runs fast on a rough surface, the sensors mounted on the robot’s body may be destroyed due to the body acceleration and oscillation. In this article, we propose a new scheme to reduce the body acceleration at any specified location for mobile robots with the actuators set on the wheel axes. To achieve this, a combined ideal robot model is designed. In the combined ideal robot model, the location where the acceleration performance is at its best can easily be moved by setting only two design parameters. Next, a robust model tracking controller is developed so that the behavior of an actual mobile robot can track the combined ideal robot model. The developed controller has the following useful properties. (1) The body acceleration at any specified location can easily be improved. (2) The developed controller has good robustness for uncertainties in robot mass, pitch and roll moment of inertia of the robot’s body, and the position of the center of gravity.     

动态多行人环境中基于最优交互避碰的机器人导航

在动态的多行人环境中,服务机器人仅依赖于自身传感器、以第一人称视角自主导航时. 机器人自主定位的不确定性以及对周围行人运动状态估计的不确定性均增加,这给机器人导航决策带来了困难. 为解决这个问题,提出一种基于最优交互避碰的机器人自主导航法. 本方法采用一种改进的粒子PHD滤波法即NP-PHDF法跟踪多个行人的状态. NP-PHDF法结合了卡尔曼粒子滤波及PHD滤波优点,因此它可以跟踪数目变化的多个目标,能够跟踪突然的加减速以及急转弯运动,并且能够抵抗遮挡. 同时,与基于粒子滤波的机器人自主定位法类似,NP-PHDF法使得行人运动状态的不确定性能够以粒子的分布来度量. 为降低状态估计的不确定性,本文提出一种“圈粒子”的粒子圈存法从粒子的分布中提取机器人和行人的真实状态. 算法的有效性在实际场景实验中得到了验证.     

Multi-Sensor-Based Control Strategy for Initiating and Maintaining Interaction Between a Robot and a Human

This paper presents a multi-sensor-based control strategy allowing a mobile robot to safely navigate with respect to a given human being. Two sensors are embedded in our robot: a vision system that is able to detect and track the person of interest, and a RFID antennas belt that can locate the tag worn by the latter. Thus, our control strategy will be built using image features (when the user is visible) and RFID information (when not). In the first case, a robust visual servoing control will be designed, while in the second case a suitable RFID controller will be proposed. Experimental results demonstrate the efficiency of the proposed control strategy.     

Fuzzy target tracking control of autonomous mobile robots by using infrared sensors

The theme of this paper is to design a real-time fuzzy target tracking control scheme for autonomous mobile robots by using infrared sensors. At first two mobile robots are setup in the target tracking problem, where one is the target mobile robot with infrared transmitters and the other one is the tracker mobile robot with infrared receivers and reflective sensors. The former is designed to drive in a specific trajectory. The latter is designed to track the target mobile robot. Then we address the design of the fuzzy target tracking control unit, which consists of a behavior network and a gate network. The behavior network possesses the fuzzy wall following control (FWFC) mode, fuzzy target tracking control (FTTC) mode, and two fixed control modes to deal with different situations in real applications. Both the FWFC and FTTC are realized by the fuzzy sliding-mode control scheme. A gate network is used to address the fusion of measurements of two infrared sensors and is developed to recognize which situation is belonged to and which action should be executed. Moreover, the target tracking control with obstacle avoidance is also investigated in this paper. Both computer simulations and real-time implementation experiments of autonomous target tracking control demonstrate the effectiveness and feasibility of the proposed control schemes.     

Predictive seam tracking with iteratively learned feedforward compensation for high-precision robotic laser welding

In this paper, a novel architecture for robotic seam tracking using an industrial robot and off-the-shelf sensors is proposed to compensate the residual errors that are commonly observed in high-precision robotic laser welding due to the nonlinearity of a seam and the fast path drifts along a robot path. Our experiments demonstrate that the robot system can track both linear and nonlinear long seams at a high speed of 100 mm/s with TCP offset-error within ±0.1 mm using the proposed method.