可自动回撤煤矿探测机器人设计

为了解决煤矿井下探测机器人工作行程受限的难题,设计了一种可自主延长通信距离和自动回撤的煤矿探测机器人。当无线控制信号减弱时,机器人通过中继器弹射系统延长其无线通信距离;当无线控制信号突然中断时,机器人启动自动回撤功能,根据探测过程中已经记录的传感器数据按原路径回撤,自动回撤到安全位置。机器人自动回撤功能采用编码器与激光传感器数据集成的方法,并使用ICP算法进行双重数据匹配,以逐步调整机器人的位置和方向角,实现较高的回撤精度。测试结果表明,该机器人具有工作行程距离长的特点和自动回撤功能,可以在未知的煤矿灾难现场稳定、安全地工作,可替代煤矿救援人员深入煤矿井下完成初期危险的探测工作,为进一步的救援提供重要的现场数据信息。     

基于卡尔曼与改进SVM移动机器人定位研究

用四轮差动驱动轮式机器人作为试验平台,以编码器和陀螺仪作为机器人的定位系统,建立了机器人的运动学方程。采用卡尔曼滤波器对两种传感器的数据进行融合,以减小编码器和陀螺仪的误差,再通过最小二乘支持向量机建立回归曲线模型,获得机器人的位姿信息。     

基于A*算法的油气安全智能巡检机器人设计

石油化工车间风险高且工作人员活动空间狭窄,大型巡检设备无法进入场地;设计开发一款智能巡检机器人代替工作人员对危险环境进行巡检,与原有的固定位置传感器相配合,以降低事故发生率,保证生产安全;该巡检机器人以DSP为核心控制器,利用A*算法规划巡检路径;通过旋转编码器与MPU6050配合智能算法实现自动循迹;搭载各类环境传感器,实时监测车间环境;使用C++Builder设计人机交互界面;利用WiFi模块实现机器人与上位机的数据实时互传;测试结果表明,可以实现对机器人速度和位置的精确控制,并能准确、实时地显示甲烷、一氧化碳等危险气体浓度变化,对危险提前预警,提醒工作人员处理。     

基于5G通信技术的巡逻机器人定位误差自动补偿方法

以提升巡逻机器人执行巡逻任务能力为目的,提出基于5G通信技术的巡逻机器人定位误差自动补偿方法。该方法在巡逻机器人工作区域架设5G无线通信网络,并将AD7380型号位置传感器安装在巡逻机器人上,利用位置传感器获取巡逻机器人工作时的位置信息后,利用5G无线通信网络将其传输到用户PC端,得到巡逻机器人位置采样点数据；以该数据为基础,使用拉丁超立方采样方法描述巡逻机器人在其巡逻空间内的位置,得到巡逻机器人空间位置数据；再依据巡逻机器人空间位置数据,计算该机器人预设目标点误差矢量,并构建巡逻机器人定位误差补偿模型,利用该模型补偿巡逻机器人定位误差。实验结果表明：该方法可全面获取巡逻机器人在其巡逻空间内的位置信息,可有效且精准的对其定位误差进行自动补偿,使巡逻机器人定位误差保持在可允许范围内,应用效果较为显著。     

基于TLE5012B的高精度多圈绝对式磁编码器设计

针对工业领域某些伺服电机位置控制需要高精度、小体积、强抗干扰能力的多圈绝对值编码器的特殊场合,设计了一种基于英飞凌公司的角度传感器TLE5012B的多圈绝对式磁编码器,该编码器可提供最高15位分辨率的位置信息.利用ARM微处理器对角度传感器的数据进行采集和处理,提供单圈绝对位置、多圈值、UVW信号和ABZ信号,采用SSI通信接口输出数据.本编码器具有掉电记忆、位置清零等功能,而且成本低,已应用于实际工控系统中.     

基于阻抗模型的下肢康复机器人交互控制系统设计

为帮助下肢功能障碍患者进行康复训练,设计了下肢康复机器人。对于该机器人的控制,采用传统系统无法柔顺控制,导致机器人运动轨迹偏离预设轨迹。针对该现象,提出了基于阻抗模型的下肢康复机器人交互控制系统设计。通过分析总体控制方案,设计系统硬件结构框图。采用L型二维力传感器,确定两个方向的人机交互力。使用绝对值编码器安装在各个关节处,其输出值作为髋关节、膝关节、踝关节电机的转动位置,增量编码器安装在电机轴上,测量值用来作为后期控制方法的输入参数。构建阻抗控制模型,能够调节机器人位置和速度,具有消除力误差功能。依据此力矩对参考运动轨迹进行设计,实时获取患者康复训练的跟踪、主动柔顺和接近状态信息。在柔顺训练实验测出人机交互力,通过实验结果知,在检测到人体主动力矩异常时,系统能够重新优化轨迹,具有良好柔顺控制效果。     

基于STM32单片机的寝室扫地机器人

寝室扫地机器人技术的研究与设计旨在通过科技改变学生的日常生活。本文介绍了该寝室扫地机器人的驱动系统、感知系统、清扫系统、控制系统和校正系统。硬件上以STM32F103ZET6为微控制器,通过超声波模块、红外传感器、MPU6050传感器和光电编码器反馈的信息来确定位置信息和下一步工作。结合学生寝室固定环境采用固定模式清扫。采用闭环控制系统使扫地车尽可能走直线,提高清扫覆盖率。通过光电编码器和直流电机的差速控制实现任意固定角度的转弯,并记录小车直线行驶的路程信息。前方的超声波传感器判断该扫地车离墙壁和障碍物距离,红外传感器检测墙壁和障碍物,避免发生碰撞。通过固定的转弯反应动作实现避障。针对寝室固定环境,该扫地机器人具有较强应用价值。     

机器人自主感知模型在网络机器人中的应用

基于机器人自主感知模型(RAPM)构建了机器人系统,该系统可通过网络自主感知获得动态网络信息,实时建立路径,使机器人完成超出其视野范围的任务．机器人自主感知模型具有很强的扩展能力和合理的构架,突破了以往移动机器人仅使用自身传感器或使用网络固定传感器的局限,对提高基于Internet的机器人智能及其行为能力有重要意义．实验结果证明了机器人自主感知模型的可行性和有效性．     

基于多传感器融合的自动导引车系统设计与控制策略

自动导引车在柔性制造业、物流业和自动仓储中具有广泛的应用前景和价值.提出并设计了一种基于CCD摄像头、RFID传感器、编码器、陀螺仪、声纳等多种传感器的自动导引车系统.通过对多种传感器数据进行信息融合达到控制目的,在实验室中搭建了模拟环境,开发了原型系统并进行测试,实验结果证明该方法提高了自动导引车系统的实时性和导航精度.     

机器人柔性视觉检测系统现场标定技术

针对江淮车身厂的轿车车身提出了一种基于工业机器人的柔性视觉检测系统．通过控制机器人在空间的位姿变换,立体视觉传感器能够依次到达空间指定测量位置采集空间特征点的图像信息,并通过计算图像数据获得该点的三维坐标数据．利用双经纬仪和精密靶标对机器人视觉测量系统进行整体标定,同时完成了测试试验数据的采集及计算,取得了较好的测量效果．该系统柔性好、测量无死角,能适应生产线多车型检测的需要．     

Hazardous area navigation with temporary beacons

A new area expansion algorithm for the localization scheme, using temporary beacons, is proposed in this paper. The effective area of the active beacons is limited by the strength of the ultrasonic signals in a noisy environment. When a mobile robot needs to move into a hazardous area or into an unstructured environment where the beacons with pre-specified position information are not available, the localization may solely rely on dead reckoning sensors such as encoders. To overcome the error accumulation by using dead-reckoning, a new scheme is developed, in this paper, in which the mobile robot carries a few temporary beacons which do not have any pre-stored position information. When the mobile robot encounters a dangerous or unstructured environment, it utilizes the temporary beacons to localize itself. An auto-calibration algorithm has been developed to provide the position information to the temporary beacons before they are used for the localization. With these temporary beacons and the auto-calibration algorithm, mobile robots can safely pass unstructured areas. The effectiveness of the temporary beacons and auto-calibration algorithm is verified through real experiments of mobile robot navigation.     

Simultaneous localization and map building for mobile robotnavigation

Inexpensive ultrasonic sensors, incremental encoders, and grid-based probabilistic modeling are used for improved robot navigation in indoor environments. For model-building, range data from ultrasonic sensors are constantly sampled and a map is built and updated immediately while the robot is travelling through the workspace. The local world model is based on the concept of an occupancy grid. The world model extracted from the range data is based on the geometric primitive of line segments. For the extraction of these features, methods such as the Hough transform and clustering are utilized. The perceived local world model along with dead-reckoning and ultrasonic sensor data are combined using an extended Kalman filter in a localization scheme to estimate the current position and orientation of the mobile robot, which is subsequently fed to the map-building algorithm. Implementation issues and experimental results with the Nomad 150 mobile robot in a real-world indoor environment (office space) are presented     

基于模拟退火算法的人工势场法路径规划研究

针对传统人工势场法在路径规划中存在局部极值小点问题,使得移动机器人无法运动到目标点,提出一种基于模拟退火算法的人工势场法,其利用模拟退火算法在出现局部极小点的位置附近增设随机目标点,引导移动机器人逐渐逃离出局部极小点区域.最终通过Matlab仿真表明,所设计的方法能使移动机器人逃离局部极小点位置,成功到达目标点位置,并...     

Continuous mobile robot localization by using structured light and a geometric map

A mobile robot needs to know its position and orientation with accuracy in order to decide the control actions that permit it to finish the entrusted tasks successfully. To obtain this information, dead-reckoning-based systems have been used, and more recently inertial navigation systems. However, these systems have some errors that grow bigger as time goes by, therefore a moment comes when the information provided is useless. Because of this, there should be a periodic process that updates the robot position and orientation of the vehicle. The process to determine the robot position and orientation by using information originated from the external sensors is defined as the mobile robot relocalization. It is obvious that the greater the frequency of this process, the better the knowledge of its position the robot will have, and therefore its movements will be better directed to the point it must reach. The algorithm to achieve this can be classified in two large groups: relocalization through an a priori map of the environment and relocalization through the detection of landmarks present in that environment. The algorithm presented in the paper belongs to the first case. The sensor used is a combination of a laser diode and a CCD camera. The sensorial information is modelled as straight lines that will be matched with an a priori map of the environment. With this, the position of the mobile robot is estimated. The matching process is accomplished within an extended Kalman filter. The algorithm is able to work in real time, and it actualizes the position of the robot in a continuous way.     

Mobile Robot Self-Localization without Explicit Landmarks

Localization is the process of determining the robot's location within its environment. More precisely, it is a procedure which takes as input a geometric map, a current estimate of the robot's pose, and sensor readings, and produces as output an improved estimate of the robot's current pose (position and orientation). We describe a combinatorially precise algorithm which performs mobile robot localization using a geometric model of the world and a point-and-shoot ranging device. We also describe a rasterized version of this algorithm which we have implemented on a real mobile robot equipped with a laser rangefinder we designed. Both versions of the algorithm allow for uncertainty in the data returned by the range sensor. We also present experimental results for the rasterized algorithm, obtained using our mobile robots at Cornell. Received November 15, 1996; revised January 13, 1998.     

嵌入式移动机器人红外路标定位模块研究

针对室内环境移动机器人的自定位问题,提出一种嵌入式移动机器人红外路标定位模块。采用基于单应矩阵的初始标定算法和陒机初始标定方法,补偿由于实际使用中的安装误差所引起的定位偏差。实验结果表明,该模块易于嵌入式系统实现,定位模块位置精度可达厘米级别,角度定位精度雓于6°。     

基于ICP算法和粒子滤波的未知环境地图创建

为了实现移动机器人仅依靠激光测距仪和里程计实时地创建精确的栅格地图, 本文提出了一种结合最近点迭代(Iterative closest point, ICP)算法和Rao-Blackwellized粒子滤波的同时定位与地图创建方法. 该方法利用ICP算法对相邻两次激光扫描数据进行配准, 并将配准结果代替误差较大的里程计读数, 以改善基于里程计读数的建议分布函数; 同时通过采用改进的抽样策略, 提高了粒子滤波过程中的抽样效率, 降低创建地图所需的粒子数. 仿真结果表明了该方法的有效性.     

移动机器人编队视觉定位方法研究

该文提出一种基于视觉的移动机器人编队定位方法。该方法采用基于纹理的图象对机器人进行标识,然后使用纹理中的特征点对机器人的位置和姿态进行估计,使用最小二乘算法使估计结果误差最小。实验证明该方法能够有效地对编队的移动机器人定位,同时对环境干扰具有鲁棒性。     

A 3-level autonomous mobile robot navigation system designed by using reasoning/search approaches

This paper describes how soft computing methodologies such as fuzzy logic, genetic algorithms and the Dempster–Shafer theory of evidence can be applied in a mobile robot navigation system. The navigation system that is considered has three navigation subsystems. The lower-level subsystem deals with the control of linear and angular volocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot is at a medium level and is nonlinear. The nonlinear control design is implemented by a backstepping algorithm whose parameters are adjusted by a genetic algorithm. We propose a new extension of the controller mentioned, in order to rapidly decrease the control torques needed to achieve the desired position and orientation of the mobile robot. The high-level subsystem uses fuzzy logic and the Dempster–Shafer evidence theory to design a fusion of sensor data, map building, and path planning tasks. The fuzzy/evidence navigation based on the building of a local map, represented as an occupancy grid, with the time update is proven to be suitable for real-time applications. The path planning algorithm is based on a modified potential field method. In this algorithm, the fuzzy rules for selecting the relevant obstacles for robot motion are introduced. Also, suitable steps are taken to pull the robot out of the local minima. Particular attention is paid to detection of the robot’s trapped state and its avoidance. One of the main issues in this paper is to reduce the complexity of planning algorithms and minimize the cost of the search. The performance of the proposed system is investigated using a dynamic model of a mobile robot. Simulation results show a good quality of position tracking capabilities and obstacle avoidance behavior of the mobile robot.     

Appearance based robotics

Robot localization has been recognized as one of the most fundamental problems in mobile robotics. Localization can be defined as the problem of determining the position of a robot. More precisely, the aim of localization is to estimate the position of a robot in its environment, given local sensorial data. This information is essential for a broad range of mobile robots tasks; in particular, the robot behavior may depend on its position. This article presents a novel and efficient metric for appearance based robot localization. This metric is integrated in a framework that uses a partially observable Markov decision process as position evaluator, thus allowing good results even in partially explored environments and in highly perceptually aliased indoor scenarios.