Self-organized architecture for outdoor mobile robot navigation

INTRODUCTION Autonomous mobile robot is a machine able to extract information from its environment and use knowledge about its world to move safely in a meaningful and purposive manner, it can operate on its own without a human directly controlling it (Ehlert,1999). The primary task of a mobile robot is envi-ronmental navigation as basis for more useful tasks. In contrast to indoor mobile robots and industrial manipulation robots both working in relatively static and structured environme…     

移动机器人自主导航技术研究综述

介绍了移动机器人的几种导航方式。对自主导航技术的关键技术———路径规划技术进行了深入研究,对人工智能以及多传感器信息融合技术在移动机器人导航中的应用进行了论述,并对移动机器人自主导航技术的发展趋势进行了展望。     

A novel method for mobile robot simultaneous localization and mapping

INTRODUCTION A key prerequisite for a truly autonomous robot is that it can simultaneously localize itself and accu-rately map its surroundings (Kortenkamp et al., 1998), which is known as Simultaneous Localization and Mapping (SLAM), which, when phrased as a state estimation problem, involves a variable number of dimensions. Murphy and Russell (2001) adopted Rao-Blackwellized particle filters (RBPFs) as an effective way for representing alternative hypotheses on robot paths and ass…     

无迹卡尔曼滤波在新型地形无源组合导航系统中的应用

为了提高自主水下航行器的导航精度,提出了一种新型地形无源组合导航系统.根据水下环境特点和航行器导航高精度和低成本的要求,采用捷联式惯性导航系统、地形辅助导航系统、多普勒速度声纳、电子磁罗经和利用UKF进行信息融合的导航计算机组成新型水下地形无源组合导航系统,并给出了EKF线性滤波方程、UKF非线性滤波方程和导航传感器量测方程.对比仿真实验表明,采用建议的传感器和UKF信息融合方式比采用EKF方式提高了水下航行器导航定位的精度.采用不同的导航传感器和UKF信息融合方法的地形无源组合导航系统可以有效地减小水下航行器导航位置误差,提高组合导航的稳定性和精度.     

基于TDOA/Doppler测量的联邦UKF移动位置估计算法(英文)

为了进一步提高移动台的跟踪和定位性能,提出了一种基于联邦滤波结构和简化UKF的移动位置最优估计与融合新方法.该算法以Singer移动台运动模型作为参考系统,以简化UKF滤波器作为子滤波器,对2组独立检测的TDOA和Doppler测量值进行局部估计;然后在主滤波器中,对子滤波器的估计结果按标量加权进行最优融合,得到全局最优或次最优融合估计结果;最后主滤波器利用全局估计结果对子滤波器和参考系统进行反馈和信息重置,以进行下一步估计.仿真试验中,对该算法用于移动台位置估计的效果和性能进行评估,并与基于TDOA和基于Doppler的简化UKF方法做比较.仿真结果表明,该算法能有效降低移动台位置估计的误差和方差,具有良好的均方根误差和均值误差CDF性能.     

基于Kalman滤波器的INS/WSN紧组合导航系统模型

在基于惯性导航系统和无线传感器网络的组合导航系统中,为了解决传统导航信息松组合方法中测量信息可观性较差的问题,提出了一种基于卡尔曼滤波器的导航信息紧组合模型.当无线传感器网络的信号可用时,组合导航系统将惯性导航系统测量得到的未知节点与已知节点的距离与无线传感器网络测量得到的距离作差,差值作为卡尔曼滤波器的测量信息.由于...     

一种基于实时行为控制的快速移动机器人

This paper discusses and compares some common architectures used in autonomous mobile robotics. Then it describes a behavior-based autonomous mobile robot that was implemented successfully in the Robotics Lab of the Department of Precision Mechanical Engineering. Fuzzy controller was used to implement the emergency behavior, the behaviors arbitration was implemented using the sub-sumption architecture. In an unknown dynamic indoor environment, the robot achieved real-time obstacle avoidance properties that are cruel for mobile robotics.     

基于组合导航的四旋翼飞行器控制系统研究

根据刚体动力学原理,建立了四旋翼飞行器的动力学模型,利用捷联惯导和GPS组合作为四旋翼飞行器的导航系统,采用方向余弦矩阵的变换方法,实现了对飞行器的姿态解算;采用卡尔曼滤波算法实现姿态角信号的校正,实现了四旋翼飞行器的导航;采用基于PID算法的悬停状态的控制策略,实现了四旋翼飞行器的垂直起降和稳定的悬停．     

基于GPS测姿和陀螺仪的角度融合定位方法研究

针对城市峡谷等遮挡严重场景下GPS定位精度严重下降问题,提出一种基于GPS测向与陀螺仪角度测量的融合定位方法。该方法通过改进传统的角度测量,利用陀螺仪短时内相对测量精度高的特点,采用卡尔曼滤波,以陀螺仪的相对角度修正GPS测向的绝对角度,再利用GPS/INS组合导航算法进行定位,使定位结果精度更高、更稳定。对提出的方法进行模拟仿真以及车载实验,实验结果表明：通过角度融合后的定位拥有更高的定位精度和定位稳定性,在复杂环境下效果很好,提高了定位可靠性。     

Gaussian particle filter based pose and motion estimation

Determination of relative three-dimensional (3D) position, orientation, and relative motion between two reference frames is an important problem in robotic guidance, manipulation, and assembly as well as in other fields such as photogrammetry. A solution to pose and motion estimation problem that uses two-dimensional (2D) intensity images from a single camera is de- sirable for real-time applications. The difficulty in performing this measurement is that the process of projecting 3D object features to 2D images is a nonlinear transformation. In this paper, the 3D transformation is modeled as a nonlinear stochastic system with the state estimation providing six degrees-of-freedom motion and position values, using line features in image plane as measuring inputs and dual quaternion to represent both rotation and translation in a unified notation. A filtering method called the Gaussian particle filter (GPF) based on the particle filtering concept is presented for 3D pose and motion estimation of a moving target from monocular image sequences. The method has been implemented with simulated data, and simulation results are provided along with comparisons to the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) to show the relative advantages of the GPF. Simulation results showed that GPF is a superior alternative to EKF and UKF.     

A remote control training system for rat navigation in complicated environment

INTRODUCTION Electrical stimulation in the animal brain is an indispensable means to investigate brain mechanisms in many experimental setups. However, the cable connections between the stimulation instruments and the animal brain always limit the animal’s freedom of movement and thereby cause entwisting and breaking of the cables, especially in freely moving animal ex-periments. In order to solve this problem, telemetry systems have been designed to deliver the stimulationsignals as sub…     

GPS控制网测量数据处理和分析

根据GPS控制网集合各类观测数据,结合部分点的实测速度为基础,建立大陆地壳运动速度场模型．基于测量的动态系统状态,采用序贯卡尔曼滤波的广义测量平差方法,对GPS点进行地壳运动的整网平差．并对分析整网平差结果进行精度统计和外部数据校核。数据分析结果表明卡尔曼滤波平差法的合理性和可靠性．     

基于二阶异常位模型的重力异常畸变级联Kalman滤波校正方法

借助基于二阶高斯-马尔可夫异常位模型的重力异常协方差函数,得到了海洋重力测量中重力异常信号的状态方程．结合实际重力仪的系统状态方程和系统量测方程,提出了级联卡尔曼滤波方法,并将其应用于重力异常畸变信号的校正处理中．在信号处理过程中,首先采用卡尔曼逆滤波恢复含高频干扰的重力异常,然后采用自适应卡尔曼滤波,以重力异常状态方程为系统方程估计实际重力异常值,并与单一卡尔曼逆滤波器的处理结果进行了对比分析．仿真和试验表明,级联卡尔曼滤波方法和单一卡尔曼逆滤波都能在一定程度上减小重力异常信号的畸变,但在相同背景条件下,级联卡尔曼滤波方法的性能优于单一逆卡尔曼滤波．     

一种基于GPS/INS的导弹制导控制系统

导弹要准确的击中目标,制导技术是关键的环节。目前GPS技术的发展和应用,推动了导弹采用GPS/INS组合制导技术的发展。本文设计了采用GPS/INS组合制导技术的导弹制导控制系统,对GPS/INS组合制导技术进行了研究,并对其中采用的卡尔曼滤波算法进行了详细的介绍。     

基于神经网络的移动机器人路径规划研究

利用一种并列连接的神经网络结构对移动机器人路径进行规划,并把神经网络与模拟退火算法相结合,解决了局部极值问题,最终收敛到全局最优解。计算机仿真研究表明:模拟退火算法具有计算简单,初值鲁棒性强以及通用易实现等优点。     

On self-recovery mechanism and additional maneuverability of a mini mobile robot

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Environment map building and localization for robot navigation based on image sequences

SLAM is one of the most important components in robot navigation. A SLAM algorithm based on image sequences captured by a single digital camera is proposed in this paper. By this algorithm, SIFT feature points are selected and matched between image pairs sequentially. After three images have been captured, the environment＇s 3D map and the camera＇s positions are initialized based on matched feature points and intrinsic parameters of the camera. A robust method is applied to estimate the position and orientation of the camera in the forthcoming images. Finally, a robust adaptive bundle adjustment algorithm is adopted to optimize the environment＇s 3D map and the camera＇s positions simultaneously. Results of quantitative and qualitative experiments show that our algorithm can reconstruct the environment and localize the camera accurately and efficiently.     

Real-time localization estimator of mobile node in wireless sensor networks based on extended Kalman filter

Localization of the sensor nodes is a key supporting technology in wireless sensor networks (WSNs). In this paper, a real-time localization estimator of mobile node in WSNs based on extended Kalman filter (KF) is proposed. Mobile node movement model is analyzed and online sequential iterative method is used to compute location result. The detailed steps of mobile sensor node self-localization adopting extended Kalman filter (EKF) is designed. The simulation results show that the accuracy of the localization estimator scheme designed is better than those of maximum likelihood estimation (MLE) and traditional KF algorithm.     

小生境伪并行遗传算法在全自主移动机器人路径规划中的应用

1IntroductionPath planning of autonomous mobile robot is pivotaltechnique for machine intelligence,which ai ms to finda non-collision path frominitial position to objectiveposition according to evaluation functions in anobstacle space[1].It can be described as travelersalesman problem(TSP),a typical combinationopti mization problem,which belongs to the well-known NP-hard opti mization[2].The mathematicaldefinition can be regarded as a mapG=(V,E),where eachlinee∈Ehas a nonnegative powerω(…     

基于模糊逻辑的移动机器人智能控制器设计（英文）

为了提高移动机器人在未知环境下的自主性,设计了一种基于模糊逻辑的智能控制器.该控制器可以辅助装备多传感器的机器人在室内未知环境下安全地进行自主导航.首先,基于模糊逻辑设计了2种实现移动机器人智能导航的行为：路径追踪行为和避障行为.使用模糊逻辑的方法可以通过模仿人类驾驶经验,降低移动机器人在未知环境下实现自主导航的复杂性.然后,利用有限状态机对2种行为进行融合,以确保在未知的室内环境下机器人能够安全地追踪一条事先指定的路径.控制器的输入为机器人的传感器数据,输出为要求机器人运动的方向.最后,仿真和实验结果证实了该控制器的有效性.