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.     

多传感器信息融合在移动机器人定位中的应用

机器人自定位是实现自主导航的关键问题之一。为了满足机器人在导航时精确定位的要求,提出一种基于多传感器信息融合的自定位算法。根据对机器人运动机构的分析和运动机构间的刚体约束,建立起机器人的运动学模型;由传感器的工作原理建立里程计和超声波传感器的观测模型;利用扩展卡尔曼滤波(EKF)算法将里程计和超声波传感器采集的数据进行融合;最后,由匹配的环境特征对机器人的位置进行修正,得到精确的位置估计。实验结果表明:该算法明显地消除了里程计的累计误差,有效地提高了定位精度。     

分布式感知协作的扩展Monte Carlo定位算法

针对移动机器人难以单纯依赖自身传感器定位的问题,提出了一种分布式感知协作的扩展Monte Carlo定位方法.在定位过程中,机器人根据感知更新前后采样分布信息熵、有效采样数目及采样分布均匀性的变化,适时地从环境传感器的检测模型进行重采样,从而有效减少其位姿估计的不确定性.在算法的具体实现过程中,采用彩色摄像头作为环境传感器,摄像头的参数由机器人进行在线标定;然后依据标定的参数获得摄像头的检测模型.实验验证了该算法在解决全局定位和机器人绑架问题时的有效性.     

超小型水下机器人智能定位系统

提出一种新的超小型水下机器人智能定位系统.融合短基线定位声纳、电子罗盘、X/Y倾角传感器和深度传感器组成超小型水下机器人定位的硬件系统,使用基于构件的方法开发了针对超小型水下机器人定位系统的水上控制计算机三维虚拟显示软件.使用马尔可夫自定位算法在实测定位声纳信号的间隙推算水下机器人的位置,提高定位速度,使其与姿态传感器同步.设计了定位算法有效性的测试实验,结果证明算法有效.     

基于多数据融合的智能定位传感器避障算法研究

在智能定位传感器内增加避障算法，可使机器人拥有自动躲避障碍的能力，该文基于多数据融合设计智能定位传感器避障算法。设置激光雷达测距和超声波测距作为多传感器障碍检测的方法，获取机器人当前位置与障碍点坐标的相对几何关系，计算机器人与障碍点位置的距离，定位路面障碍点，对2种传感器收集到的数据进行多元障碍定位信息的加权融合。设置智能机器人避障轨迹目标函数以及约束条件，设计机器人避障算法，得到基于定位传感器的机器人避障方法。实验结果表明，在简单环境及复杂环境下机器人均未与障碍物相撞。在运行轨迹中随机放置障碍物，机器人能够及时完成运行轨迹的变化。由此可见，该避障算法具备较好的应用前景，可应用于各种智能机器人中。     

基于特征点的机器人快速自定位研究及应用

提出一种在已知的结构化环境中，利用激光传感器信息进行移动机器人快速自定位的算法．该算法从传感器信息中快速提取环境特征点，根据特征点的坐标映射模型，确定机器人的坐标和方向角．它克服了传统算法运算量大、实时性差的问题且定位精度高，将其应用于机器人足球赛中，取得了较好的定位效果．     

动态多行人环境中基于最优交互避碰的机器人导航

在动态的多行人环境中,服务机器人仅依赖于自身传感器、以第一人称视角自主导航时. 机器人自主定位的不确定性以及对周围行人运动状态估计的不确定性均增加,这给机器人导航决策带来了困难. 为解决这个问题,提出一种基于最优交互避碰的机器人自主导航法. 本方法采用一种改进的粒子PHD滤波法即NP-PHDF法跟踪多个行人的状态. NP-PHDF法结合了卡尔曼粒子滤波及PHD滤波优点,因此它可以跟踪数目变化的多个目标,能够跟踪突然的加减速以及急转弯运动,并且能够抵抗遮挡. 同时,与基于粒子滤波的机器人自主定位法类似,NP-PHDF法使得行人运动状态的不确定性能够以粒子的分布来度量. 为降低状态估计的不确定性,本文提出一种“圈粒子”的粒子圈存法从粒子的分布中提取机器人和行人的真实状态. 算法的有效性在实际场景实验中得到了验证.     

非完整移动机器人鲁棒运动规划

非完整移动机器人利用传感器可以解决不确定性模型和未知环境中的许多问题. 利用移动机器人上配备的传感器的信息组合提出了一种在线视点寻求算法, 结合移动机器人的运动方程和传感器的量测方程采用扩展Kalman估计来对移动机器人的位置进行修正, 以降低运动的不确定性, 从而得到一种鲁棒的规划算法, 仿真的结果证明了上述方法是行之有效的.     

运用异质传感器信息融合的移动机器人自定位

采用单类、单一传感器很难获得移动机器人的准确定位.为此,运用异质传感器信息融合来提高定位精度.首先,建立机器人运动方程和CCD摄像机观测模型.然后,利用扩展卡尔曼滤波器进行状态估计,选择Q,R矩阵抑制系统的模型噪声和量测噪声,并实现移动机器人的自定位.接着,建立超声波传感器的观测模型,获得机器人的自定位信息.最后,运用BP神经网络,将两种自定位信息进行融合,实现两类传感器的优缺点互补.仿真实验表明,运用异质传感器信息融合能明显地提高移动机器人的自定位精度.     

RFID和WSNs技术在机器人协同定位中的应用研究

针对常用定位技术难以满足移动机器人协同定位精度高、实时性强的要求,提出了射频识别(RFID)和无线传感器网络(WSNs)混合定位技术。采用有源RFID定位技术,参考身份标识ID对应的电子标签在未知环境中的位置坐标,利用接收到的信号强度结合改进的极大似然估计算法确定出单个机器人的位置坐标。基于WSNs技术,确定出父节点机器人与预协作子机器人之间的相对方向和距离信息。给出了机器人自身绝对位置和协作机器人之间相对定位的算法,并通过实际电路和程序设计进行了实验验证。结果表明:该方法有效,可取,可解决多机器人可协同定位的范围小,精度低,实时性差等问题。     

Dynamic localization with hybrid trilateration for mobile robots in intelligent space

In this paper, we propose a new localization algorithm based on a hybrid trilateration algorithm for obtaining an accurate position of a robot in intelligent space. The proposed algorithm is also able to estimate a position of the moving robot by using the extended Kalman filter, taking into consideration time synchronization and velocity of the robot. For realizing the localization system, we employ several smart sensors as beacons on the ceiling in intelligent space and as a listener attached to the robot. Finally, simulation results show the feasibility and effectiveness of the proposed localization algorithm compared with existing trilateration algorithms.     

Multirobot Localization in Highly Symmetrical Environments

The paper addresses and solves the problem of multirobot collaborative localization in highly symmetrical 2D environments, such as the ones encountered in logistic applications. Because of the environment symmetry, the most common localization algorithms may fail to provide a correct estimate of the position and orientation of the robot, if its initial position is not known, no specific landmark is introduced, and no absolute information (e.g., GPS) is available: the robot can estimate its position with respect to the walls of the corridor, but it could be critical to determine in which corridor it is actually moving. The proposed algorithm is based upon a particle filter cooperative Monte Carlo Localization (MCL) and implements a three-stage procedure for the global localization and the accurate position tracking of each robot of the team. Online simulations and experimental tests, which investigate different situations with respect to the number of robots involved and their initial positions, show how the proposed solution can lead to the global localization of each robot, with a precision sufficient to be used as starting point for the subsequent robot tracking.     

A stereo matching approach based on particle filters and scattered control landmarks

In robot localization, particle filtering can estimate the position of a robot in a known environment with the help of sensor data. In this paper, we present an approach based on particle filtering, for accurate stereo matching. The proposed method consists of three parts. First, we utilize multiple disparity maps in order to acquire a very distinctive set of features called landmarks, and then we use segmentation as a grouping technique. Secondly, we apply scan line particle filtering using the corresponding landmarks as a virtual sensor data to estimate the best disparity value. Lastly, we reduce the computational redundancy of particle filtering in our stereo correspondence with a Markov chain model, given the previous scan line values. More precisely, we assist particle filtering convergence by adding a proportional weight in the predicted disparity value estimated by Markov chains. In addition to this, we optimize our results by applying a plane fitting algorithm along with a histogram technique to refine any outliers. This work provides new insights into stereo matching methodologies by taking advantage of global geometrical and spatial information from distinctive landmarks. Experimental results show that our approach is capable of providing high-quality disparity maps comparable to other well-known contemporary techniques.     

Concurrent localization of multiple robots

A concurrent localization method for multiple robots using ultrasonic beacons is proposed. This method provides a high-accuracy solution using only low-price sensors. To measure the distance of a mobile robot from a beacon at a known position, the mobile robot alerts one beacon to send out an ultrasonic signal to measure the traveling time from the beacon to the mobile robot. When multiple robots requiring localization are moving in the same block, it is necessary to have a schedule to choose the measuring sequence in order to overcome constant ultrasonic signal interference among robots. However, the increased time delay needed to estimate the positions of multiple robots degrades the localization accuracy. To solve this problem, we propose an efficient localization algorithm for multiple robots, where the robots are in groups of one master robot and several slave robots. In this method, when a master robot calls a beacon, all the group robots simultaneously receive an identical ultrasonic signal to estimate their positions. The effectiveness of the proposed algorithm has been verified through experiments.     

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.     

A Probabilistic Approach to Collaborative Multi-Robot Localization

This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser range-finders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization.     

Vision-based 3-D trajectory tracking for unknown environments

This paper describes a vision-based system for 3-D localization of a mobile robot in a natural environment. The system includes a mountable head with three on-board charge-coupled device cameras that can be installed on the robot. The main emphasis of this paper is on the ability to estimate the motion of the robot independently from any prior scene knowledge, landmark, or extra sensory devices. Distinctive scene features are identified using a novel algorithm, and their 3-D locations are estimated with high accuracy by a stereo algorithm. Using new two-stage feature tracking and iterative motion estimation in a symbiotic manner, precise motion vectors are obtained. The 3-D positions of scene features and the robot are refined by a Kalman filtering approach with a complete error-propagation modeling scheme. Experimental results show that good tracking and localization can be achieved using the proposed vision system.     

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.     

基于信息融合地图的水下机器人位置估计

建立了一种对声纳和视觉图像进行融合的模型,提出了采用高斯方法和对水下环境进行描述建立融合地图的新的表达方法。首先假定传感器的观测信息为高斯分布,通过空间关系的变换和投影将声纳和视觉投影到公共的状态空间,然后对各传感器的其它信息进行加权,并嵌入到其中,得到适合计算机处理的传感器地图。提出了对水下机器人进行位置估计及地图匹配的算法,在导航过程中通过找出当前地图与参考地图的最大相关系数,从而对机器人位置进行更新,得出其最佳位置估计。仿真结果显示：采用融合地图对水下机器人的位置估计是连续的、可计算的、有效的。