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.     

Intelligent Adaptive Mobile Robot Navigation

This paper deals with the application of a neuro-fuzzy inference system to a mobile robot navigation in an unknown, or partially unknown environment. The final aim of the robot is to reach some pre-defined goal. For this purpose, a sort of a co-operation between three main sub-modules is performed. These sub-modules consist in three elementary robot tasks: following a wall, avoiding an obstacle and running towards the goal. Each module acts as a Sugeno–Takagi fuzzy controller where the inputs are the different sensor information and the output corresponds to the orientation of the robot. The rule-base is generated by the controller after some learning process based on a neural architecture close to that used by Wang and Menger. This leads to adaptive neuro-fuzzy inference systems (ANFIS) (one for each module). The adaptive navigation system (ANFIS), based on integrated reactive-cognitive parts, learns and generates the required knowledge for achieving the desired task. However, the generated rule-base suffers from redundancy and abundance of data, most of which are less useful. This makes the assignment of a linguistic label to the associated variable difficult and sometimes counter-intuitive. Consequently, a simplification phase allowing elimination of redundancy is required. For this purpose, an algorithm based on the class of fuzzy c-means algorithm introduced by Bezdek and we have developed an inclusion structure. Experimental results confirm the meaningfulness of the elaborated methodology when dealing with navigation of a mobile robot in unknown, or partially unknown environment.     

多传感器技术在机器人系统中的应用和研究

探讨了多传感器融合系统的软件结构设计方案，说明了如何设计一个基于多传感器的自主式智能机器人系统，提出了基于BP神经网络的融合方法。搭建了一个多传感器的智能机器人试验平台，实现了由两个摄像头组成的立体视觉和超声波传感器的融合。试验表明，多传感器融合技术在自主式智能系统中具有重要作用。     

Increasing Intelligence in Autonomous Wheelchairs

The ideas shown in this paper have been developed having in mind the main goal of designing a completely autonomous wheelchair prototype. The state of the art of mobile robotics is compared with the new trends in the field. The idea of autonomy made us focus our research in extracting the best from conventional Intelligent Mobile Robots techniques, looking towards the concept of Autonomous Mobile Systems (AMS). In order to clarify the body of the presentation, some practical examples, developed at our laboratory (DISAM-UPM), are included in parallel with the main discourse.     

自主移动机器人混合式体系结构的设计与实现

针对以往自主移动机器人体系结构在实时处理方面的不足,提出了一种基于多智能体的混合式体系结构,统一规划了机器人系统的软硬件结构,在该体系结构中设计并实现了协调Agent和推理Agent两种智能体,针对紧急事件进行了更实时的Agent实现,有效的提高的自主移动机器人在突发事件时的实时性,提出了使用多样化的信息组织形式,增强了系统的自适应能力和易扩展性。本文的实验结果表明在紧急状态下,系统的反应时间有效缩短,增加了系统的智能性和实时性。     

基于多传感器信息融合的移动机器人导航系统

介绍了多传感器信息融合的基本原理,给出了基于多传感器信息融合的移动机器人导航系统结构。建立了移动机器人数学模型,运用基于扩展卡尔曼滤波的信息融合方法实现了移动机器人导航算法。通过实验验证了基于多传感器信息融合的移动机器人导航系统和导航算法的有效性。     

Using Real-Time Stereo Vision for Mobile Robot Navigation

This paper describes a working vision-based mobile robot that navigates and autonomously explores its environment while building occupancy grid maps of the environment. We present a method for reducing stereo vision disparity images to two-dimensional map information. Stereo vision has several attributes that set it apart from other sensors more commonly used for occupancy grid mapping. We discuss these attributes, the errors that some of them create, and how to overcome them. We reduce errors by segmenting disparity images based on continuous disparity surfaces to reject spikes caused by stereo mismatches. Stereo vision processing and map updates are done at 5 Hz and the robot moves at speeds of 300 cm/s.     

基于势场的移动机器人自主导航研究综述

人工势场法因简单实用、易于实现等优点，在机器人导航中获得广泛应用。势函数的构造也随着其它相关技术的发展不断得到完善。该文综述了势函数应用于机器人自主导航的发展历程，对其存在的问题作了分析，并对各种改进方案做了分析和比较，对影响其发展的制约性问题做了深入探讨。     

自主移动机器人巡线控制研究

针对自主移动机器人传统巡线控制中存在的不足,在使用灰度传感器采集地面轨迹信息的同时,引入角度传感器对行进方向的角度信息进行采集,由此克服了传统巡线控制中单一传感器采集信息不全的缺点;设计了PID控制加模糊控制的复合控制器,并给出复合控制器算法.在此基础上建立实验系统,通过对其进行仿真,结果证明该控制策略不仅适于自主移动...     

自重构移动微机器人设计与自动对接

设计了一种模块化的自重构移动微机器人系统。系统中的每个微机器人模块既可作为动力单元,拖动其它模块进行更远距离的探测,也可自动脱离成为无线通信节点,拓展整个系统的通信距离。通过这2种方式扩展了单个微型移动机器人的探测范围。研制了由1个红外接收器和4个红外发射器组成的导引系统,并基于其设计了一种新型的自动对接方法。最后进行了微机器人之间的对接实验,验证了该方法具有很高的效率和可靠性。     

移动机器人的一种室内自然路标定位法

实时定位是移动机器人导航的一个基本前提。该文提出了一种利用墙棱边及墙平面(EdgeandPlane,EP)路标或者广义EP路标进行定位的方法,使用了异步数据融合的方法对移动机器人进行了定位。仅利用传感器对墙平面的距离数据进行测量就实现了机器人的定位。仿真显示这些方法能减少机器人的定位时间,提高对传感器测量数据的利用效率。     

Localization of an Autonomous Mobile Robot Based on Ultrasonic Sensory Information

Based on ultrasonic sensory information, an approach is proposed for localization of autonomous mobile robot (AMRs). In the proposed method, it will be proven that the combination of three ultrasonic transmitters and two receivers can determine both the position and the orientation of an AMR with respect to a reference frame uniquely. In this manner, since only ultrasonic sensors are used, the proposed method will be highly cost-effective and easy to implement. To show the validity and feasibility of the proposed method, the hardware configuration and a series of experiments will be given for illustration.     

面向多区域扫描的机器人作业序列规划与自主避障研究

针对移动式机器人多区域扫描作业任务,本文基于人工智能思想研究机器人作业序列规划和自主避障方法,从而保证机器人自主完成使命任务。采用激光测距系统LMS200作为移动机器人环境测量传感器,开发了机器人与LMS200的串口通信程序;提出采用人工智能遍历算法实现机器人自主作业序列规划,保证规划结果在时间上是最优的;针对复杂环境中存在未知障碍的问题,设计了基于人工势场法的机器人自主避障算法,并通过仿真试验进行了算法验证,结果表明：本文所研究方法是有效且可行的。     

一种动态未知环境中自主机器人的导航方法

提出一种动态未知环境中机器人自主导航方法,利用少量的人类辅助避免了繁琐的地图描述．该方法分两个阶段：用户引导阶段和自主导航阶段．在用户引导阶段,利用多种传感器信息融合生成局部环境的粗略的极坐标地图,利用它可以得到全局地图,还给出了消除传感器数据误差的方法;在自主导航阶段,利用引导阶段得到的地图在动态环境中进行运动,并给出了运动控制的约束条件以及动态避障的方法．机器人利用该方法可以处理突发的障碍物,还能对路径进行优化,实验结果证明了其有效性．     

Coupled Oscillator Control of Autonomous Mobile Robots

This paper introduces a nonlinear oscillator scheme to control autonomous mobile robots. The method is based on observations of a successful control mechanism used in nature, the Central Pattern Generator. Simulations were used to assess the performance of oscillator controller when used to implement several behaviors in an autonomous robot operating in a closed arena. A sequence of basic behaviors (random wandering, obstacle avoidance and light following) was coordinated in the robot to produce the higher behavior of foraging for light. The controller is explored in simulations and tests on physical robots. It is shown that the oscillator—based controller outperforms a reactive controller in the tasks of exploring an arena with irregular walls and in searching for light.     

全自主移动机器人分层递阶控制研究

本文提出了一种基于多CPU的分层递阶控制策略,应用于全自主移动机器人的结构化设计。以分层递阶控制为核心思想的自主机器人采用智能级、协调级和执行级级连的结构,其中智能级和协调级配备独立的微处理器。实验表明,所设计的自主机器人系统具备良好的环境感知能力和控制精度,可以通过开放的VC++平台实现对机器人的控制。     

Cooperative Mobile Robotics: Antecedents and Directions

There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting cooperative behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of cooperative robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations.     

一种移动机器人环境模型——灰色定性地图

针对移动机器人的环境建模问题,提出一种综合拓扑地图和儿何地图特点的混合环境模型——灰色定性地图.用凸剖分算法将环境中的自由空间分解为一组凸多边形.灰色定性地图的定性层由凸多边形及其之间的邻接关系构成,用于模拟人类在路径规划时的高层定性推理.定量层由凸多边形顶点的坐标和势场向量构成,用于决定机器人在连续空间中的运动方向和速度.理论分析和实验均表明:灰色定性地图可以模拟人类对环境认知的知识表达,并且可以仪由凸多边形邻接信息和顶点信息支持机器人完成路径规划且确保路径的平滑性,有效地降低了环境模型的空间复杂度.     

基于超声波的移动机器人的同时定位和地图构建

为了在移动机器人SLAM过程中得到更精确的定位和二维地图构建,对一种利用超声波传感器信息进行栅格地图创建的方法提出了改进;该方法利用Bayes法则对信息进行融合,利用粒子滤波和航位推算相结合的方法对机器人进行精确定位和创建地图,然后利用移动的栅格法进行地图的全局更新,提出了一种地图的校验方法;通过实验,在粒子数为200的情况下分别得到了算法改进前和改进后的地图构建结果,通过比较,证明了使用该算法进行移动机器人定位和地图构建更加精确。