基于模糊卡尔曼滤波的移动机器人定位研究

为了满足移动机器人准确定位的要求,提出了一种基于模糊卡尔曼滤波（FKF）的自定位算法。利用扩展卡尔曼滤波（EKF）算法融合里程计和声纳的观测数据,并针对EKF中观测噪声方差估计不准确导致滤波器性能下降甚至发散的问题,提出了基于模糊逻辑的自适应调节算法。该算法通过监测新息实际方差和理论方差的一致程度,在线调整观测噪声的方差值。仿真结果表明,此方法较EKF提高了系统的定位精度和鲁棒性。     

Rotation estimation for mobile robot based on single-axis gyroscope and monocular camera

The rotation matrix estimation problem is a keypoint for mobile robot localization, navigation, and control. Based on the quaternion theory and the epipolar geometry, an extended Kalman filter (EKF) algorithm is proposed to estimate the rotation matrix by using a single-axis gyroscope and the image points correspondence from a monocular camera. The experimental results show that the precision of mobile robot s yaw angle estimated by the proposed EKF algorithm is much better than the results given by the image-only and gyroscope-only method, which demonstrates that our method is a preferable way to estimate the rotation for the autonomous mobile robot applications.     

Mobile robot localization based on PSO estimator

Localization is fundamental to autonomous operation of the mobile robot. A particle filter (PF) is widely used in mobile robot localization. However, the robot localization based PF has several limitations, such as sample impoverishment and a degeneracy problem, which reduce significantly its performance. Evolutionary algorithms, and more specifically their optimization capabilities, can be used in order to overcome PF based on localization weaknesses. In this paper, mobile robot localization based on a particle swarm optimization (PSO) estimator is proposed. In the proposed method, the robot localization converts dynamic optimization to find the best robot pose estimate, recursively. Unlike the localization based on PF, the resampling step is not required in the proposed method. Moreover, it does not require noise distribution. It searches stochastically along the state space for the best robot pose estimate. The results show that the proposed method is effective in terms of accuracy, consistency, and computational cost compared with localization based on PF and EKF.     

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。     

基于改进UKF算法的移动机器人定位方法研究

针对移动机器人在多传感器融合定位过程中因噪声统计特性未知或不准确引起的定位精度不高的问题,提出了一种基于Sage-Husa滤波改进的无损卡尔曼滤波(UKF)移动机器人定位算法。首先建立了移动机器人定位相关模型;然后根据噪声统计特性时变特点利用Sage-Husa中的噪声估计器,对状态噪声和量测噪声进行自适应地估计,减小扰动噪声给定位解算带来的误差;接着在状态更新时引入收敛因子,加快算法收敛速度;最后将UKF算法和改进的UKF算法应用到实验室移动机器人中进行仿真实验。实验结果表明,所提出的算法对状态扰动具有较强的抵制能力,对机器人定位的准确性与稳定性的提升具有显著效果。     

A Robust Regression Model for Simultaneous Localization and Mapping in Autonomous Mobile Robot

Segment-based maps as sub-class of feature-based mapping have been widely applied in simultaneous localization and map building (SLAM) in autonomous mobile robots. In this paper, a robust regression model is proposed for segment extraction in static and dynamic environments. We adopt the MM-estimate to consider the noise of sensor data and the outliers that correspond to dynamic objects such as the people in motion. MM-estimates are interesting as they combine high efficiency and high breakdown point in a simple and intuitive way. Under the usual regularity conditions, including symmetric distribution of the errors, these estimates are strongly consistent and asymptotically normal. This robust regression technique is integrated with the extended Kalman filter (EKF) to build a consistent and globally accurate map. The EKF is used to estimate the pose of the robot and state of the segment feature. The underpinning experimental results that have been carried out in static and dynamic environments illustrate the performance of the proposed segment extraction method.     

基于模糊自适应卡尔曼滤波的移动机器人定位方法*

针对移动机器人定位过程中噪声统计特性不确定的问题,提出一种模糊自适应扩展卡尔曼滤波定位方法。利用模糊理论和协方差匹配技术对扩展卡尔曼滤波算法中的观测噪声协方差R进行自适应调整,实现定位算法性能的在线改进;同时采用传感器故障诊断与修复算法来监测传感器的工作状态,提高定位算法的鲁棒性。将该方法用于观测噪声统计特性未知情况下的移动机器人定位。实验结果表明,该方法可以有效地降低观测噪声先验信息不确定的影响,提高机器人定位的精度。     

EKF-Based Localization of a Wheeled Mobile Robot in Structured Environments

This paper deals with the problem of mobile-robot localization in structured environments. The extended Kalman filter (EKF) is used to localize the four-wheeled mobile robot equipped with encoders for the wheels and a laser-range-finder (LRF) sensor. The LRF is used to scan the environment, which is described with line segments. A prediction step is performed by simulating the kinematic model of the robot. In the input noise covariance matrix of the EKF the standard deviation of each robot-wheel’s angular speed is estimated as being proportional to the wheel’s angular speed. A correction step is performed by minimizing the difference between the matched line segments from the local and global maps. If the overlapping rate between the most similar local and global line segments is below the threshold, the line segments are paired. The line parameters’ covariances, which arise from the LRF’s distance-measurement error, comprise the output noise covariance matrix of the EKF. The covariances are estimated with the method of classic least squares (LSQ). The performance of this method is tested within the localization experiment in an indoor structured environment. The good localization results prove the applicability of the method resulting from the classic LSQ for the purpose of an EKF-based localization of a mobile robot.     

Local maneuvers planning: application to an autonomous wheelchair moving in constrained spaces

The problem approached in this paper is the simulation of maneuvers planning for an autonomous mobile robot moving in constrained spaces free of obstacles. The robot treated is an autonomous wheelchair for the disabled. Our approach is based on the principle that a maneuver is the concatenation of elementary moves with reversal. So, to perform a maneuver that enables reaching a final state, we have defined five elementary moves. The developed planner is built around a perception system, a guidance system and a locomotion system. Each elementary move is implemented using a fuzzy controller.     

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.     

模糊PID控制下移动机器人定位方法研究

机器人定位研究一直是机器人学研究的重点,但目前机器人定位方法都存在缺点,抗干扰能力差,不能做到准确定位,主要是由于环境等多方面因素的干扰,定位误差会逐渐加大;由于上述原因,提出了一种基于设定值加权模糊PID控制的移动机器人自定位方法;给出了定位过程的参数,为机器人移动建立模型,设计一种模糊 PID 控制器,根据误差及变化率大小,选择模糊定位或PID定位,实现移动机器人的智能定位,提高机器人定位准确的准确性;通过仿真实验结果证明:模糊PID控制的机器人自定位方法对移动机器人的定位过程有较好的改善作用,实用效果较好。     

基于多传感器融合的移动机器人定位

针对移动机器人定位系统中单一传感器定位精度低与环境地图的重要性问题, 提出了一种基于多传感器融合的移动机器人定位方法. 首先, 在未知环境下, 分别利用单一里程计, 扩展卡尔曼滤波(extended Kalman filter,EKF)算法融合里程计、惯性测量单元(inertial measurement unit, ...     

Mobile-robot pose estimation and environment mapping using an extended Kalman filter

In this paper an extended Kalman filter (EKF) is used in the simultaneous localisation and mapping (SLAM) of a four-wheeled mobile robot in an indoor environment. The robot’s pose and environment map are estimated from incremental encoders and from laser-range-finder (LRF) sensor readings. The map of the environment consists of line segments, which are estimated from the LRF’s scans. A good state convergence of the EKF is obtained using the proposed methods for the input- and output-noise covariance matrices’ estimation. The output-noise covariance matrix, consisting of the observed-line-features’ covariances, is estimated from the LRF’s measurements using the least-squares method. The experimental results from the localisation and SLAM experiments in the indoor environment show the applicability of the proposed approach. The main paper contribution is the improvement of the SLAM algorithm convergence due to the noise covariance matrices’ estimation.     

IEKF滤波在移动机器人定位中的应用

针对EKF中观测噪声方差估计不准确导致滤波器性能下降甚至发散的问题,提出了基于环境特征的迭代扩展卡尔曼滤波(IEKF)融合算法。该算法融合了里程计采集的机器人内部数据和激光雷达传感器采集的外部环境特征,在测量更新阶段多次迭代状态估计值并对机器人的位姿进行修正,减少了非线性误差,提高了定位精度。     

The development of an autonomous service robot. Implementation: “Lucas”—The library assistant robot

This paper describes an autonomous mobile device that was designed, developed and implemented as a library assistant robot. A complete autonomous system incorporating human–robot interaction has been developed and implemented within a real world environment. The robotic development is comprehensively described in terms of its localization systems, which incorporates simple image processing techniques fused with odometry and sonar data, which is validated through the use of an extended Kalman filter (EKF). The essential principles required for the development of a successful assistive robot are described and put into demonstration through a human–robot interaction application applied to the library assistant robot.     

一种基于人机交互的移动机器人反应式导航方法

针对未知环境中移动机器人的自主导航问题,提出了一种基于人机交互的反应式导航方法。在采用模糊逻辑实现机器人基本智能行为的基础上,利用基于优先级和有限状态机的混合行为协调方法建立"环境刺激-反应"机制,提高机器人的局部自主能力。提出将"人刺激-反应"机制引入机器人系统,提高机器人系统对环境的理解与决策能力。在不同环境模型中利用提出的方法对移向指定目标的机器人自主导航进行了仿真,仿真结果验证了该方法的有效性。     

T–S fuzzy model adopted SLAM algorithm with linear programming based data association for mobile robots

This paper describes a Takagi–Sugeno (T–S) fuzzy model adopted solution to the simultaneous localization and mapping (SLAM) problem with two-sensor data association (TSDA) method. Nonlinear process model and observation model are formulated as pseudolinear models and rewritten with a composite model whose local models are linear according to T–S fuzzy model. Combination of these local state estimates results in global state estimate. This paper introduces an extended TSDA (ETSDA) method for the SLAM problem in mobile robot navigation based on an interior point linear programming (LP) approach. Simulation results are given to demonstrate that the ETSDA method has low computational complexity and it is more accurate than the existing single-scan joint probabilistic data association method. The above system is implemented and simulated with Matlab to claim that the proposed method yet finds a better solution to the SLAM problem than the conventional extended Kalman filter–SLAM algorithm.