移动机器人的改进无迹粒子滤波蒙特卡罗定位算法

粒子滤波是移动机器人蒙特卡罗定位(Monte Carlo localization, MCL)的核心环节. 首先, 针对粒子滤波过程的粒子退化问题, 利用迭代Sigma点卡尔曼滤波来精确设计粒子滤波器的提议分布, 以迭代更新方式将当前观测信息融入顺序重要性采样过程, 提出IUPF (Improved unscented particle filter)算法. 然后, 将IUPF与移动机器人MCL相结合, 给出IUPF-MCL定位算法的实现细节. 仿真结果表明, IUPF-MCL是一种精确鲁棒的移动机器人定位算法.     

基于栅格方法的移动机器人实时导航和避障

本文对移动机器人的实时导航和避障设计了基于栅格的控制算法，用该算法控制清华大学计算机系研制的ＴＨＭＲ－Ⅲ移动机人能在未知的环境中，实量检测出障碍物，并实时规划出合理路径，稳定，平滑连续地向着目标行驶，最后给出了ＴＨＭＲ－Ⅲ在几种典型环境中行驶的实验结果。     

室内移动机器人超声传感器导航网络分布研究

对于室内广域移动机器人,超声波传感器网络是能够保证给定导航准确度的一种有效方法。合理的超声波传感器网络分布是实现超声波网络导航的重要条件。论述了超声波网络合理分布的原则,据此提出并分析了获得合理超声波传感器网络分布的四个步骤,仿真试验结果验证了它的有效性。     

面向医院的移动机器人导航系统设计

本文设计了一种采用RFID和机器视觉相结合的自主移动机器人导航系统.该系统在搜索和识别走廊内RFID标签和门牌号的基础上实现自定位和导航.本文首先给出了移动机器人导航系统的整体设计,然后分别给出了基于RFID辅助定位方法和基于DSP的门牌号识别方法,最后在博创UpVoyager移动机器人平台上验证了本系统的有效性.     

基于ROS的室内自主移动机器人设计

设计一款基于ROS的i室内自主移动机器人,根据机器人实际功能需求,选择基于四轮差速驱动的机器人底盘。该机器人以Jetson Nano为核心控制器,ArdunoMega2560板和驱动板为辅控制器,辅控制器采用一体化设计,将各功能子电路通过优化布局方式集成一块PCB扩展板,增加了辅控制器的可靠性和稳定性,并能减少了接线的数量。该机器i人利用ROS系统把机器人所用所要进行的一系列工程整合在一起,激光雷达为主要传感器。通过上位机（JetsonNanio）与下位机（Arduno板和驱动板）的通信,使下位机接收到上位机的消息,发送指令到驱动板,驱动板进行速度和方向的控制。ArdunoMega2560板接收上位机的指令后向电机驱动板传输信号,电机驱动板通过连接PWM端口来控制小车左右电机的转速,从而实现差速转向。通过测试,移动机器人通过激光雷达进行SLAM建图,实现机器人能够自主导航、自主避障等功能。     

自主移动机器人基于行为的导航策略及其实现

为了开发具有自主导航功能的移动机器人，我们必须提供智能推理的能力，模糊控制和基于行为的导航可以实现这一点，本文讨论了把模糊逻辑推理应用到自主移动机器人的行为控制系统中。     

基于无迹卡尔曼滤波的室内定位系统

卡尔曼滤波抑制非视距(NLOS)所产生的误差效果不明显;而使用扩展卡尔曼滤波解决非视距(NLOS)时,需要解算复杂的雅可比矩阵,使计算量大为增加。文中首先对获取的TDOA数据进行无迹卡尔曼滤波并得到最优解;其次将滤波后的数据,通过CHAN算法进行位置解算。实验结果表明,文中所用无迹卡尔曼滤波算法对抑制误差产生了很好的效果,提高了定位精度,具有较大的实际应用价值。     

基于路径识别的移动机器人视觉导航

跟随路径导引是自主式移动机器人广泛采用的一种导航方式,其中视觉导航具有其他传感器导航方式所无法比拟的优点,是移动机器人智能导航的主要发展方向。为了提高移动机器人视觉导航的实时性和准确性,提出了一个基于路径识别的视觉导航系统,其基本思想是首先用基于变分辨率的采样二值化和形态学去噪方法从原始场景图像中提取出目标支持点集,然后用一种改进的哈夫变化检测出场景中的路径,最后由路径跟踪模块分直行和转弯两种情况进行导航计算。实验结果表明,该视觉导航系统具有较好的实时性和准确性。     

室内自主式移动机器人定位方法

定位是确定机器人在其工作环境中所处位置的过程.应用各种传感器感知信息实现可靠的定位是自主式移动机器人最基本、也是最重要的一项功能之一.本文对室内自主式移动机器人的定位技术进行了综述,介绍了当前自主式移动机器人定位方法的研究现状.同时,对国内外具有典型性的研究方法进行了较洋细的介绍,并重点提出了几种室内自主式移动机器人通用的定位方法,对其中的地图构造、位姿估计方法进行了详细介绍.最后,论述了自主式移动机器人定位系统与地图构造中所面临的主要问题及其解决方法并指出了该领域今后的研究方向.     

一处室内轮式自主移动机器人的导航控制研究

介绍了一种室内移动机器人CASIA-I.对该机器人的运动机构做了较为详细地阐述,针对该运动机构给出了机器人的运动方程和一种导航控制算法,并根据该算法进行了软件仿真和实物实验.在软件仿真和实物实验两种环境下,机器人都能够实时避开障碍物奔向目标.仿真和实验表明:该移动平台具有良好的可靠性,且该导航控制算法是一种有效的导航算法.     

Qualitative navigation for mobile robots in indoor environments

This article describes a novel qualitative navigation method for mobile robots in indoor environments. The approach is based on qualitative representations of variations in sensor behavior between adjacent regions in space. These representations are used to localize and guide planning and reaction. Off-line, the system accepts as input a line-based diagram of the environment and generates a map based on a simple qualitative model of sensor behavior. During execution, the robot controller integrates this map into a reaction module. This architecture has been tested both in simulation and on a real mobile robot. Results from both trials are provided.     

A hybrid approach for autonomous navigation of mobile robots in partially-known environments

We propose a hybrid approach specifically adapted to deal with the autonomous-navigation problem of a mobile robot which is subjected to perform an emergency task in a partially-known environment. Such a navigation problem requires a method that is able to yield a fast execution time, under constraints on the capacity of the robot and on known/unknown obstacles, and that is sufficiently flexible to deal with errors in the known parts of the environment (unexpected obstacles). Our proposal includes an off-line task-independent preprocessing phase, which is applied just once for a given robot in a given environment. Its purpose is to build, within the known zones, a roadmap of near-time-optimal reference trajectories. The actual execution of the task is an online process that combines reactive navigation with trajectory tracking and that includes smooth transitions between these two modes of navigation. Controllers used are fuzzy-inference systems. Both simulation and experimental results are presented to test the performance of the proposed hybrid approach. Obtained results demonstrate the ability of our proposal to handle unexpected obstacles and to accomplish navigation tasks in relatively complex environments. The results also show that, thanks to its time-optimal-trajectory planning, our proposal is well adapted to emergency tasks as it is able to achieve shorter execution times, compared to other waypoint-navigation methods that rely on optimal-path planning.     

用于室外移动机器人实时自主导航的2.5维角度势场法

本文提出了一种针对装备有激光测距仪的地面移动机器人系统的实时自主导航算法.与现有的专为解决2维导航问题所设计的算法不同,新算法在导航过程中引入了障碍物的高度信息,并且使用2.5维角度势场法来满足在复杂的户外地形条件下的自主导航要求.首先,一幅激光测距仪扫描地图被划分为两种不同的功能扇区:导航扇区和监测扇区.然后,在充分利用障碍物高度信息的条件下,对导航扇区和监测扇区进行重构,从而获得一幅虚拟的2维激光扫描地图.最后,传统的角度势场法被进行了适当的改进,以便能够顺利的作用到虚拟的2维激光扫描地图上,产生恰当的导航指令.新算法在履带型地面移动机器人上进行了测试,实验结果充分的证明了其有效性和可实现性.     

自主移动机器人室内定位方法研究综述

自主移动机器人的室内定位作为机器人研究领域中最基本的问题已被广泛研究。根据定位技术和传感器的不同,将室内定位方法分为航迹推算定位、地图匹配定位和基于信标定位三类。详细介绍了超声波网络定位系统和基于无线射频识别（RFID）的定位方法。对几种基于概率的定位算法做了分析和对比,并对自主移动机器人室内定位方法的研究方向做了展望。     

Video based indoor exploration with autonomous and mobile robots

Techniques are proposed to support the video based development of systems for indoor exploration with mobile robots. The technique of redundant programming is often used to improve the reliability of operating systems, but the use of this technique is not common for CV (computer vision) applications. Also a new technique to create CAD (computer aided design) models from image data is described. These techniques were used for the development of an RV (robot vision) program. The observed recognition power exceeds the abilities of sophisticated but conventional programs clearly. This is documented with sample images, which show a table that has been taken from different distances. The quality of the images is very bad due to the fact that a camera was taken which has a very low resolution. Additionally the detection of the table was hampered, because the illumination in the images varied considerably. Sometimes the table was placed very near by a window with strong exposure to sunlight. Over-exposure of the table complicated the reconstruction because of this problem. Sometimes other objects irritated the detection. The program handled all these difficulties impressionably although it used no calibration techniques. No other robot-vision program is documented in the literature that gained the reported recognition rate.     

An autonomous stereovision-based navigation system (ASNS) for mobile robots

Recently, stereovision has appeared in robotics as a source of information for real-time mapping and path planning. In this paper, an intelligent motion system for mobile robots is designed and implemented using stereovision. The proposed system uses stereovision as a primary method for sensing the environment, and the system is able to navigate intelligently in an indoor environment with varying degrees of obstacle complexity. It creates noiseless and high-confidence 3D point clouds and uses these point clouds as an input for the mapping and path-planning modules. The proposed system was built by developing, enhancing, and integrating various techniques, modules and algorithms. The Stereovision-based Path-planning module is the integration of three main enhanced techniques: (1) the multi-baseline multi-view stereovision filter (MMSVF), (2) accurate floor detection and segmentation (AFDS), and (3) the intelligent gazing module (IGM). This Stereovision-based Path planning (MMSVF, IGM, and AFDS) was integrated with the Fuzzy Logic Motion Controller (FLMC). All techniques, modules and algorithms are implemented using a multi-threaded and client–server-based architecture. To prove the viability and robustness of our proposed system, we have integrated all components of the system into a fully functional mobile robot navigation system. We compared the performance of the main modules with that of similar modules in the literatures, and showed that our modules had better performance. Testing the whole system is more important than just testing each module individually. To the best of our knowledge, the literatures lack such testing. Hence, in this paper we present the performance of our complete integrated system in different environments using different parameters and different architectures.     

Adaptive navigation for autonomous robots

In many robotic exploration missions, robots have to learn specific policies that allow them to: (i) select high level goals (e.g., identify specific destinations), (ii) navigate (reach those destinations), (iii) and adapt to their environment (e.g., modify their behavior based on changing environmental conditions). Furthermore, those policies must be robust to signal noise or unexpected situations, scalable to more complex environments, and account for the physical limitations of the robots (e.g., limited battery power and computational power).In this paper we evaluate reactive and learning navigation algorithms for exploration robots that must avoid obstacles and reach specific destinations in limited time and with limited observations. Our results show that neuro-evolutionary algorithms with well-designed evaluation functions can produce up to 50% better performance than reactive algorithms in complex domains where the robot’s goals are to select paths that lead to seek specific destinations while avoiding obstacles, particularly when facing significant sensor and actuator signal noise.     

基于传感器信息的智能移动机器人导航评述

导航是研究智能移动机器人技术中的一个重要领域,对自主导航技术的关键问题——路径规划进行了评述。路径规划一般可分为基于模型的环境信息完全知道的全局路径规划和基于传感器的环境信息完全未知或部分未知的局部路径规划2种类型。分别对各种方法的发展现状进行了总结,指出了各种方法的优点和不足。