An Efficient Localization Algorithm Based on Vector Matching for Mobile Robots Using Laser Range Finders

This paper describes an efficient localization algorithm based on a vector-matching technique for mobile robots with laser range finders. As a reference the method uses a map with line-segment vectors, which can be built from a CAD layout of the indoor environment. The contribution of this work lies in the overall localization process. First, the proposed sequential segmentation method enables efficient vector extraction from scanned data. Second, a reliable and efficient vector-matching technique is proposed. Finally, a cost function suitable for vector-matching is proposed for nonlinear pose estimation with solutions for both nonsingular and singular cases. Simulation results show that the proposed localization method works reliably even in a noisy environment. The algorithm was implemented for our wheelchair-based mobile robot and evaluated in a 135 m long corridor environment. Categories (3), (6).     

基于神经网络的移动机器人路径规划

针对移动机器人未知环境下的安全路径规划,本文采用了一种局部连接Hopfield神经网络（ANN）规划器。对任意形状环境,ANN中兼顾处理了“过近”和“过远”来形成安全 路径,而无需学习过程。为在单处理器上进行有效的在线路径规划,提出用基于距离变换的串行模拟,加速数值势场的传播。仿真表明,该方法具有较高的实时性和环境适应性。     

使用NDT激光扫描匹配的移动机器人定位方法

提出一种将基于扫描匹配的蒙特卡洛定位方法,作为移动机器人完成自主任务的鲁棒性定位方法． 采用一种新的正态分布转换(NDT)激光扫描匹配算法,将从单个激光扫描重构的2D离散数据点集转换成2维平面内分段连续可微的概率分布,并使用Hessian矩阵法与另外的扫描相匹配,可以避免点与点之间对应的复杂问题．实验结果表明,该定位算法可以利用自然环境特征有效地完成室内环境下的自主定位．     

Neural Networks for Mobile Robot Localisation using Infra-Red Range Sensing

For a robot to be fully autonomous whilst mobile, it is necessary for it to be able to determine its position in its environment. Most of the work on this problem has concentrated on using geometrical techniques which are typically implemented as part of a Kalman filter cycle. This paper examines the possibility of using a neural network to assist in the task of estimating the position of the robot. This is beneficial because it does not require beacons to be placed in the environment or the use of an explicit map of the environment. It does not require knowledge of the previous estimate of the robot’s position. In this paper, Radial Basis Function networks and Multi-Layer Perceptrons are trained to estimate the functional relationship between preprocessed range sensor data and the position of the robot. This approach is assessed using both simulated and real range data.     

移动机器人同步定位与地图构建研究进展

同步定位与地图构建（Simultaneous localization and mapping, SLAM）作为能使移动机器人实现全自主导航的工具近来倍受关注.本文对该领域的最新进展进行综述,特别侧重于一些旨在降低计算复杂度的简化算法的分析上,同时对它们进行分类,并指出其优点和不足.本文首先建立了SLAM问题的一般模型,指出了解决SLAM问题的难点;然后详细分析了基于EKF的一些简化算法和基于其他估计思想的方法;最后,对于多机器人SLAM和主动SLAM等前沿课题进行了讨论,并指出了今后的研究方向.     

Dual Adaptive Dynamic Control of Mobile Robots Using Neural Networks

This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.     

基于分布式感知的移动机器人同时定位与地图创建

为了创建大规模环境的精确栅格地图,提出一种基于分布式感知的两层同时定位与地图创建（SLAM）算法．在局部层,机器人一旦进入了一个新的摄像头视野,便依据机器人本体上的激光和里程计信息,采用Rao-Blackwellized粒子滤波方法创建一个新的局部栅格地图．与此同时,带有检测标志的机器人在摄像头视野内以曲线方式运动,以解决该摄像头的标定问题．在全局层,一系列的局部地图组成一个连接图,局部地图间的约束对应于连接图的边．为了生成一个准确且全局一致的环境地图,采用随机梯度下降法对连接图进行优化．实验结果验证了所提算法的有效性．     

Neural Networks Based Control of Mobile Robots: Development and Experimental Validation

The paper proposes a neural networks approach to the solution of the tracking problem for mobile robots. Neural networks based controllers are investigated in order to exploit the nonlinear approximation capabilities of the nets for modeling the kinematic behavior of the vehicle and for reducing unmodeled tracking errors contributions. The training of the nets and the control performances analysis have been done in a real experimental setup. The proposed solutions are implemented on a PC‐based control architecture for the real‐time control of the LabMate mobile base and are compared with classical kinematic control schemes. Experimental results are satisfactory in terms of tracking errors and computational efforts. © 2003 Wiley Periodicals, Inc.     

移动机器人扩展卡尔曼滤波定位与传感器误差建模

针对里程计在定位过程中存在累积误差的问题,建立了一种通用的移动机器人里程计误差模型,对里程计误差进行实时反馈补偿.在利用激光雷达进行环境特征提取过程中,根据激光雷达原始数据存在的误差,建立了激光雷达的观测误差模型,并根据环境特征和机器人的相对位置关系,建立了移动机器人观测模型.最后,结合里程计和激光雷达误差模型,利用扩展卡尔曼滤波(EKF)实现了基于环境特征跟踪的移动机器人定位.实验结果验证了里程计和激光雷达误差模型的引入,在增加较短定位时间的情况下,可以有效地提高移动机器人的定位精度.     

可移动机器人的马尔可夫自定位算法研究

马尔可夫定位算法是利用机器人运动环境中的概率密度分布进行定位的方法.使用该 方法机器人可在完全不知道自己位置的情况下通过传感器数据和运动模型来估计自己的位置. 但是,在研究中发现它还存在一些问题,如概率减小到零后就无法恢复.对只有距离传感器的机 器人在对称的环境中仅仅采用该算法就无法确定位置.为了解决这些问题,文中给出了修正算 法,并建议在机器人上装上方向仪(如指南针或陀螺仪等),然后利用定义的一个角度高斯分布 函数来构造新的机器人感知模型.在此基础上详细地阐述了一种新的自定位技术.最后,采用仿 真程序验证了机器人在对称环境中运动时这一新算法的可行性.     

An Active System for Three-Dimensional Localization of Mobile Robots

This paper presents an active system, which is composed of a laser range finder and four artificial reflectors, for the three-dimensional (3D) localization of a mobile robot. In this system, it will be proved that the position and the orientation of a mobile robot in a 3D space with respect to a reference frame can be determined provided that the four artificial reflectors are not installed in the same plane. Since the artificial reflectors cannot be treated as points in practice, the proposed localization procedure will be formulated as a nonlinear programming problem to account for actual sizes of the artificial reflectors. To show the validity and feasibility of the proposed method, a series of experiments will be given for illustration.     

基于全景视觉的移动机器人同步定位与地图创建研究

提出了一种基于全景视觉的移动机器人同步定位与地图创建（Omni-vSLAM）方法．该方法提取 颜色区域作为视觉路标;在分析全景视觉成像原理和定位不确定性的基础上建立起系统的观测模型,定位出 路标位置,进而通过扩展卡尔曼滤波算法（EKF）同步更新机器人位置和地图信息．实验结果证明了该方法在 建立环境地图的同时可以有效地修正由里程计造成的累积定位误差．     

移动机器人基于激光测距和单目视觉的室内同时定位和地图构建

该文研究了部分结构化室内环境中自主移动机器人同时定位和地图构建问题.基于激光和视觉传感器模型的不同,加权最小二乘拟合方法和非局部最大抑制算法被分别用于提取二维水平环境特征和垂直物体边缘.为完成移动机器人在缺少先验地图支持的室内环境中的自主导航任务,该文提出了同时进行扩展卡尔曼滤波定位和构建具有不确定性描述的二维几何地图的具体方法.通过对于SmartROB-2移动机器人平台所获得的实验结果和数据的分析讨论,论证了所提出方法的有效性和实用性.     

人工神经网络在滥用检测上的应用

目前大多数滥用检测(misusedetection)的方法,是使用基于规则的专家系统来鉴别已知攻击的种种迹象的,然而,一旦攻击模式改变,这些技术就很难识别出来,而人工神经网络则有着非常大的潜力,可以基于有限、不完整和非线形的数据资源对网络行为进行鉴别和分类。     

人工神经网络在滥用检测上的应用

目前大多数滥用检测(misuse detection)的方法,是使用基于规则的专家系统来鉴别已知攻击的种种迹象的,然而,一旦攻击模式改变,这些技术就很难识别出来,而人工神经网络则有着非常大的潜力,可以基于有限、不完整和非线形的数据资源对网络行为进行鉴别和分类.     

移动机器人改进激光SLAM算法研究

针对传统RBPF(Rao-Blackwellised particle filter)算法存在定位精度低、粒子退化、粒子多样性丧失的问题,提出了一种基于激光雷达的改进SLAM(simultaneous localization and mapping)算法.首先基于主成分分析法对相邻帧的点云进行粗配准,再采用改进点到线...     

A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80×25 m illustrate the appropriateness of the approach.     

无线传感器网络中移动节点定位算法研究

提出一种利用临时锚节点的蒙特卡罗箱定位算法.该算法是基于蒙特卡罗定位方法之上,通过引入节点平均速率来获取临时锚节点,并利用一跳范围内的临时锚节点构建最小锚盒、增强样本过滤条件,从而加速了采样和样本过滤.此外,在样本的获取上采用了非随机采样的均衡采样方法,有效地降低了采样次数.仿真结果表明:该算法同蒙特卡罗定位算法等相比,提高了节点的定位精度,降低了节点的能耗.     

面向拥挤环境的移动机器人改进粒子滤波定位

在动态变化的拥挤环境中,移动机器人的传统地图匹配定位算法会由于观测信息的剧烈变化,导致定位性能明显下降甚至完全失效.对此本文提出了一种基于可定位性估计的改进粒子滤波定位算法.本算法一方面借助观测模型的可定位性矩阵估计激光测距仪观测数据的可信度,另一方面通过预测模型的协方差矩阵估计里程计数据的可信度,进而根据这两个指标调节观测信息对预测位姿的修正值.在多种典型走廊环境中,与经典粒子滤波定位算法做了对比实验,结果表明了本文算法对提高复杂环境下移动机器人定位性能的有效性.     

基于神经动力学的非完整移动机器人跟踪控制

主要研究了非完整移动机器人轨迹跟踪问题。基于后退控制和神经动力学生物激励模型,采用自适应参数调节的方法提出了一种新的跟踪控制器。该控制器能够生成平稳合理的速度,解决了以往大部分跟踪控制器所产生的速度突变问题,并且具有很好的鲁棒性。运用李雅普诺夫稳定性理论证明控制系统的稳定性。对连续、离散轨迹的仿真及与传统后退方法的比较分析验证了该方法的有效性。