基于移动机器人的多超声波传感器信息融合

智能化是未来机动机器人的发展方向,而智能化的基础是传感器技术的发展,机器人信息融合技术则弥补了使用单传感器带来的缺陷。本文利用多超声波传感器系统荻得移动机器人的环境信息,并使用人工神经网络对多传感器信息进行融合,从而达到对被测物体影像比较准确的认识,试验表明效果良好,具有很好的使用价值。     

Grid-based localization of a mobile robot using sonar sensors

This paper presents a technique for localization of a mobile robot using sonar sensors. Localization is the continual provision of knowledges of position that are deduced from its a priori position estimation. The environment of a robot is modeled by a two-dimensional grid map. We define a physically based sonar sensor model and employ an extended Kalman filter to estimate positions of the robot. Since the approach does not rely on an exact geometric model of an object, it is very simple and offers sufficient generality such that integration with concurrent mapping and localization can be achieved without major modifications. The performance and simplicity of the approach are demonstrated with the results produced by sets of experiments using a mobile robot equipped with sonar sensors.     

基于单目视觉的轮式机器人同步定位与地图构建

如何降低计算复杂度是视觉机器人同步定位与地图(SLAM)构建的热点问题.提出一种基于单目视觉的低计算复杂度的轮式机器人同步定位与地图构建算法.该算法在观测步通过图像处理与分析,识别特征点并进行定位,将轮式机器人的视觉投影与空间物体的几何关系转换为计算机器人相对特征点的距离和角度.整体算法步骤按照预测、观测、数据关联、更新、地图构建的递推算法进行同步定位与地图构建.提出的算法可识别环境目标,并进行平滑运动.在滤波观测步只处理单帧图像数据,和Active Vision和立体视觉方法相比,降低了算法的计算复杂度.     

Accurate calibration of kinematic parameters for two wheel differential mobile robots

Odometry using wheel encoders provides fundamental pose estimates for wheeled mobile robots. Systematic errors of odometry can be reduced by the calibration of kinematic parameters. The UMBmark method is one of the widely used calibration schemes for two wheel differential mobile robot. In this paper, an accurate calibration scheme of kinematic parameters is proposed by extending the conventional UMBmark. The contributions of this paper can be summarized as two issues. The first contribution is to present new calibration equations that remarkably reduce the systematic error of odometry. The new equations were derived to overcome the limitation of the conventional schemes. The second contribution is to propose the design guideline of the test track for calibration experiments. The calibration performance can be significantly improved by appropriate design of the test track. The numerical simulations and experimental results show that the odometry accuracy can be improved by the proposed calibration schemes.     

管道机器人无线电磁自适应定位技术

针对金属管道机器人跟踪定位问题,提出了一种改进的自适应无线电磁定位方法,在未知管道几何尺寸和周围环境电磁参数的情况下实现了管道内机器人的三维定位.首先,通过磁偶极子模型分析了管道外极低频电磁场分布规律.其次,建立了六传感器接收天线的自适应定位模型,设计了电磁发射和接收电路,基于该模型推导出了含有6个未知数的非线性方程组,并分别采用3种算法对其进行了仿真分析.最后,分别对不同壁厚及不同埋藏深度管道进行了试验研究.试验结果表明,在管道壁厚为5.74 mm,埋藏深度为0.8～2 m的情况下,总位置误差的平均值小于18.7 cm,即比原模型减少了3.4 cm,满足机器人实际工作中的定位需要.     

Development of an articulated mine-detecting manipulator system for mobile robots

This paper presents a servo motor selection technique for a mobile mine detection system which is operated in the field condition. As a first step, mine detection sensor should follow the earth profile while maintaining a constant distance to the earth surface. In order to acquire a reasonable manipulator trajectory and its required torque, a kind of modeling technique is devised to remove the unwanted fluctuations in the earth data. Then, Straete et al.’s normalization method is adopted and is improved to select the adequate actuator for each joint of manipulator. The improvements are as follows: First, an equivalent output power lines are plotted at the same load curve, which gives another selection criterion. Second, a temperature based criterion is proposed for the continuous limit, which estimates the steady state winding temperature of a motor, and can be used to determine a suitable motor and gear ratio. This formulation gives not only the selection of a motor and gear ratio, but also gives the estimated operating temperature. Finally, realized system is verified through the basic performance test.     

Robust and efficient vision system for group of cooperating mobile robots with application to soccer robots

In this paper a global vision scheme for estimation of positions and orientations of mobile robots is presented. It is applied to robot soccer application which is a fast dynamic game and therefore needs an efficient and robust vision system implemented. General applicability of the vision system can be found in other robot applications such as mobile transport robots in production, warehouses, attendant robots, fast vision tracking of targets of interest and entertainment robotics. Basic operation of the vision system is divided into two steps. In the first, the incoming image is scanned and pixels are classified into a finite number of classes. At the same time, a segmentation algorithm is used to find corresponding regions belonging to one of the classes. In the second step, all the regions are examined. Selection of the ones that are a part of the observed object is made by means of simple logic procedures. The novelty is focused on optimization of the processing time needed to finish the estimation of possible object positions. Better results of the vision system are achieved by implementing camera calibration and shading correction algorithm. The former corrects camera lens distortion, while the latter increases robustness to irregular illumination conditions.     

Self-learning navigation algorithm for vision-based mobile robots using machine learning algorithms

Many mobile robot navigation methods use, among others, laser scanners, ultrasonic sensors, vision cameras for detecting obstacles and following paths. However, humans use only visual (e.g. eye) information for navigation. In this paper, we propose a mobile robot control method based on machine learning algorithms which use only camera vision. To efficiently define the state of the robot from raw images, our algorithm uses image-processing and feature selection steps to choose the feature subset for a neural network and uses the output of the neural network learned through supervised learning. The output of the neural network uses the state of a reinforcement learning algorithm to learn obstacle-avoiding and path-following strategies using camera vision image. The algorithm is verified by two experiments, which are line tracking and obstacle avoidance.     

基于红外测距传感器信息的通风管道清扫机器人控制算法研究

为解决通风管道清扫机器人在水平矩形管道中的自主行驶问题,设计了一种基于红外测距传感器信息的控制算法.红外测距传感器检测出机器人与管道壁面的距离,计算出机器人在管道中的姿态;光电编码器检测出机器人的实际行驶速度,利用轮式移动机器人运动学模型估计其状态.通过对这两种信息的融合,得到机器人在管道中的位姿信息.根据位姿信息和设计的控制率,实时调整机器人舵轮方向,使机器人保持与壁面平行行驶,并防止其与壁面碰撞.理论上证明这个控制系统是渐近稳定的,并通过实验对控制算法进行了验证.     

基于图像处理的机器人目标识别与定位系统

为实现移动机器人对目标的导引,提出了一种简单有效的方法。机器人视觉系统的关键技术是对目标实时、准确的提取,而目标提取的实质是图像分割。该文介绍了一种基于色彩空间参照表的适用于机器人视觉系统目标提取的图像分割算法,通过图像处理计算出主动目标图像特征的质点来实现对目标的定位,利用神经网络建立了目标从图像空间到摄像机空间的坐标对应关系,从而对空间运动目标进行了定位。仿真结果也验证了该方法的有效性和可行性。     

A two-layered subgoal based mobile robot navigation algorithm with vision system and IR sensors

The present work describes the real-life implementation of a mobile robot navigation scheme where vision sensing is employed as primary sensor for path planning and IR sensors are employed as secondary sensors for actual navigation of the mobile robot with obstacle avoidance capability in a static or dynamic indoor environment. This two-layer based, goal-driven architecture utilizes a wireless camera in the first layer to acquire image and perform image processing, online, to determine subgoal, employing a shortest path algorithm, online. The subgoal information is then utilized in the second layer to navigate the robot utilizing IR sensors. Once the subgoal is reached, vision based path planning and IR guided navigation is reactivated. This sequential process is continued in an iterative fashion until the robot reaches the goal. The algorithm has been effectively tested for several real-life environments created in our laboratory and the results are found to be satisfactory.     

基于SimMechanics的4自由度机器人的轨迹规划和仿真系统设计

针对1台4自由度教学型机器人的结构特点,利用多项式插值规划关节空间的轨迹,并利用Matlab中的SimMechanics工具箱建立运动学仿真模型.仿真结果表明利用仿真模型可以准确、有效地得到机器人的运动参数和运动轨迹,为机器人分析设计提供了可靠依据.     

Automation of cell production system for cellular phones using dual-arm robots

The demands placed on automation of cell production processes for IT products are becoming increasingly sophisticated. Dual-arm robots are drawing particular attention as a process solution because they offer flexibility and can work in a similar manner to a human operator. In this paper, we propose an automation system for cellular phone packing processes that uses two dual-arm robots. The applied robots are designed with specifications that meet the requirements of the cellular phone packing tasks. In addition, a robotic cell production system is proposed that applies a task allocation method to perform an efficient packing job for cellular phones. In particular, each task is assigned with the intention of reducing the takt time , which is the time taken to finish a single product in the production line and to avoid collisions between the two robots. Finally, we implement some of our results in a demonstration of a packing job that involves filling five unit boxes with seven kinds of accessories.     

Characterization and calibration of MEMS inertial sensors for state and parameter estimation applications

Widening applications of inertial sensors have triggered the search for cost effective sensors and those based on MEMS technology have been gaining popularity and widespread use particularly for lower cost applications. However, inertial sensors are subject to various error sources and characteristics of these should be modelled carefully. Corrective calibration is required for successful use for anything but the most trivial applications, body state estimation and navigation being important application areas. In this paper, we review the deterministic error and random noise sources for these sensors, consider a number of inertial sensor calibration tests to provide models for these errors and derive the calibration parameters for MEMS based strapdown IMUs. We carry out these tests and present the results for a low cost and popular IMU. We further provide performance results for an example application of body state and parameter estimation using the derived calibration data and discuss our results.     

A novel design of permanent magnet wheel with induction pin for mobile robot

Robots are utilized to automate works on a vertical plane of a large structure such as a ship and permanent magnet wheels have been utilized to make possible the robots to be attached to vertical plane and be in motion. In this paper, we propose a new design of the permanent magnet wheel for mobile robots to improve the adhesive force and facilitate the detachment of the wheel. In newly suggested design, the magnetic flux enhances the adhesive force during the attachment while induction pins redirect magnetic flux in order to achieve an easier detachment. To characterize the performance, finite element analysis is executed and experiment apparatus is constructed. The results show that the adhesive force is reduced effectively by using induction pins.     

移动机器人的超声波传感器发散角标定及应用

为了提高移动机器人在作业站点的定位精度,在考虑入射角的基础上,分析了发散角对超声波传感器测距的影响,建立了同时考虑发散角及入射角的超声波传感器测距函数关系表达式;提出了超声波传感器发散角的标定方法,并通过实验对发散角进行了标定;通过对比实验可以看出,所提出的关系表达式提高了超声波传感器距离测量精度。在此基础上提出了利用自动导航车(AGV)单个侧面两个超声波传感器实现移动机器人在作业站点侧向、前向及姿态3个方向的位姿定位方法,最后将该方法用于自主研发的移动机器人的定位试验,验证了该方法的正确性和有效性。     

多移动微小型机器人编队控制与协作避碰研究

针对多移动微小型机器人系统的协作避碰和队形保持,给出了一种分布式的编队控制方法。结合移动微小型机器人的运动控制模型,提出了一种路径规划方法,使其在运动中实时避免碰撞。在此基础上利用李雅普诺夫(Lyapunov)法设计了一种编队控制器。在有界误差范围内,该控制器能够保证多机器人的轨迹跟踪和协作避碰。通过将编队控制转化为跟踪整个队形质心的轨迹,降低了控制的复杂度,从而可以较好地应用到计算资源有限的多移动微小型机器人中。通过仿真、分析和对比,对以上控制方法的稳定性和可行性进行了验证,并进行了实际的编队和避碰控制实验。实验结果表明该方法可有效地应用于多移动微小型机器人的协作避碰和编队控制。     

基于无线网络传感器技术的危险源实时监测系统

本文介绍了一种基于无线网络传感器技术的危险源的实时监控系统,系统由DS18B20温度传感器、涡流振动传感器、LPC900系列低功耗MCU、ZigBee无线模块等组成,主要实现对危险源的温度检测和振动监测,并将其温度和振动信号的实时状态经ZigBee模块传送给上位机.本文对系统的原理、无线网络传感器的电路原理图和程序设计、无线通信模块中的信息帧格式和无线网络传感器的组网设计做了详细介绍.     

A software system for optimum design and trajectory planning of robots

This paper describes an interactive graphic robot-simulation system that has been developed to carry out optimum design and to provide a robot-independent off-line motion-planning tool. The system includes model building, optimum design, trajectory planning and graphic display, and is capable of detecting collisions. Emphasis is given to graphics facilities (translation, scaling, removal of hidden lines, image animation, etc.). The system is highly interactive through a menu technique. It was written in Turbo Pascal and implemented on an IBM PC microcomputer.