鲁棒的机器人蒙特卡洛定位算法

提出一种基于粒子滤波器的机器人定位算法. 首先利用一并行扩展卡尔曼滤波器作为粒子预测分布, 将当前观测的部分信息融入, 以改善滤波效果, 减小所需粒子数; 然后提出变密度函数边界的马尔可夫链蒙特卡洛(Markov chain Monte Carlo, MCMC)重采样方法, 以提高粒子的细化能力; 最后结合普通重采样方法, 提出一种改进的MCMC重采样的机器人定位算法, 减少粒子匮乏效应的同时, 提高了定位精度. 实验结果表明, 该算法较传统方法在计算复杂度、定位精度和鲁棒性方面都有显著提高.     

一种鲁棒高效的移动机器人定位方法

摘要利用基于自适应粒子滤波与地图匹配方法实现了机器人的自定位. 提出了一种采用距离相似性度量以及几何相似性度量的二次更新方法,对常规的基于激光测距仪的粒子滤波定位方法进行了改进,既增强了系统的鲁棒性,又提高了系统的计算效率. 仿真结果表明,移动机器人利用该定位方法可以在室内环境中利用自然特征进行鲁棒高效的自定位.     

Robust Autonomous Robot Localization Using Interval Analysis

This paper deals with the determination of the position and orientation of a mobile robot from distance measurements provided by a belt of onboard ultrasonic sensors. The environment is assumed to be two-dimensional, and a map of its landmarks is available to the robot. In this context, classical localization methods have three main limitations. First, each data point provided by a sensor must be associated with a given landmark. This data-association step turns out to be extremely complex and time-consuming, and its results can usually not be guaranteed. The second limitation is that these methods are based on linearization, which makes them inherently local. The third limitation is their lack of robustness to outliers due, e.g., to sensor malfunctions or outdated maps. By contrast, the method proposed here, based on interval analysis, bypasses the data-association step, handles the problem as nonlinear and in a global way and is (extraordinarily) robust to outliers.Luc Jaulin: on leave from Laboratoire d'Ingénierie des Systèmes Automatisés, Université d'Angers, 2 bd Lavoisier, 49045 Angers, FranceLuc Jaulin: on leave from Laboratoire d'Ingénierie des Systèmes Automatisés, Université d'Angers, 2 bd Lavoisier, 49045 Angers, FranceLuc Jaulin: on leave from Laboratoire d'Ingénierie des Systèmes Automatisés, Université d'Angers, 2 bd Lavoisier, 49045 Angers, France     

一种鲁棒的室内移动机器人定位方法

该文意图解决移动机器人在室内环境中定位难的问题,提出了卡尔曼滤波定位方法。移动机器人依靠自身配置的声纳传感器感知环境,并提取环境中的墙角作为几何特征,通过预测值与实际观测值之间匹配和修正的循环过程实现了该定位算法,实验结果表明该方法是鲁棒的。     

Robust Robot Localization by Sensors with Different Degree of Accuracy

In this paper the robust robot localization problem with respect to uncertainties on environment features is formulated in a stochastic setting, and an Extended Kalman Filtering (EKF) approach is proposed for the integration of odometric, gyroscopic, and sonar measures. As gyroscopic readings are much more reliable than the other ones, the localization algorithm gives rise to a nearly singular EKF. This problem is dealt with by defining a reduced order nonsingular EKF. The robust solution has been implemented and tested on a powered wheelchair.     

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.     

Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis

Many classification problems involve high dimensional inputs and a large number of classes. Multiclassifier fusion approaches to such difficult problems typically centre around smart feature extraction, input resampling methods, or input space partitioning to exploit modular learning. In this paper, we investigate how partitioning of the output space (i.e. the set of class labels) can be exploited in a multiclassifier fusion framework to simplify such problems and to yield better solutions. Specifically, we introduce a hierarchical technique to recursively decompose a C-class problem into C_1 two-(meta) class problems. A generalised modular learning framework is used to partition a set of classes into two disjoint groups called meta-classes. The coupled problems of finding a good partition and of searching for a linear feature extractor that best discriminates the resulting two meta-classes are solved simultaneously at each stage of the recursive algorithm. This results in a binary tree whose leaf nodes represent the original C classes. The proposed hierarchical multiclassifier framework is particularly effective for difficult classification problems involving a moderately large number of classes. The proposed method is illustrated on a problem related to classification of landcover using hyperspectral data: a 12-class AVIRIS subset with 180 bands. For this problem, the classification accuracies obtained were superior to most other techniques developed for hyperspectral classification. Moreover, the class hierarchies that were automatically discovered conformed very well with human domain experts’ opinions, which demonstrates the potential of using such a modular learning approach for discovering domain knowledge automatically from data. Received: 21 November 2000, Received in revised form: 02 November 2001, Accepted: 13 December 2001     

一种分层递阶的定性拓扑推理方法

文章针对现有的定性表示方法和拓扑推理算法存在的问题,提出了一种新的方法.首先,提出了基于概念邻域结构的定性表示方法.然后,给出了不同粒度层次上拓扑关系复合表的计算方法.最后,设计得出分层递阶的拓扑推理算法.文章给出的方法具有较高的认知合理性,所提出的推理算法可根据问题来选择合适的表示和推理层次,在已有推理算法给不出解的情况下,可以给出问题的合理解,对一般定性推理研究有参考价值.     

基于主动环形闭合约束的移动机器人分层同时定位和地图创建

基于Rao-Blackwellized 粒子滤波器提出了一种基于主动闭环策略的移动机器人分层同时定位和地图创建(simultaneous localization and mapping, SLAM)方法,基于信息熵的主动闭环策略同时考虑机器人位姿和地图的不确定性;局部几何特征地图之间的相对关系通过一致性算法估计,并通过环形闭合约束的最小化过程回溯修正.在仅有单目视觉和里程计的基础上,建立了鲁棒的感知模型;通过有效的尺度不变特征变换(scale invariant feature transform, SIFT)方法提取环境特征,基于KD-Tree的最近邻搜索算法实现特征匹配.实际实验表明该方法为实现SLAM提供了一种有效可靠的途径.     

一个可用于机器人定位的映射变换

在摄像机以某一角度俯拍地面的情形下,根据透视透影的成像原理,推导了一个从地面各点到摄像机图像间的变换公式,也得到了从摄像机图像中各点到地面的变换公式。只要知道世界坐标系中两个点之间的相对位置及其在摄像机图像的位置,就可以得到从摄像机图像到地面坐标系变换关系,这两个变换互为逆变换,它们可以在机器人定位及其他应用过程中发挥重要作用。     

机器人班组的自组织递阶控制

研究了机器人班组在执行各种任务时的协调控制.由于机器人班组是由多个能力有限的机器人组成的,被操作的对象可以是一个刚体、柔性体或机械系统,而且需要跟踪的运动也可以是各种各样的,所以整个系统是相当复杂的.这样的机械系统,按其力学性质可以将要实现的控制任务加以分解,从而实现递阶控制,各层的控制只完成被分解出来的特定的较简单的任务,而各机器人之间的协调由自组织算法自动完成.     

Robust Algorithms for Object Localization

Model-based localization, the task of estimating an object's pose from sensed and corresponding model features, is a fundamental task in machine vision. Exact constant time localization algorithms have been developed for the case where the sensed features and the model features are the same type. Still, it is not uncommon for the sensed features and the model features to be of different types, i.e., sensed data points may correspond to model faces or edges. Previous localization approaches have handled different model and sensed features of different types via sampling and synthesizing virtual features to reduce the problem of matching features of dissimilar types to the problem of matching features of similar types. Unfortunately, these approaches may be suboptimal because they introduce artificial errors. Other localization approaches have reformulated object localization as a nonlinear least squares problem where the error is between the sensed data and model features in image coordinates (the Euclidean image error metric). Unfortunately, all of the previous approaches which minimized the Euclidean image error metric relied on gradient descent methods to find the global minima, and gradient descent methods may suffer from problems of local minima. In this paper, we describe an exact, efficient solution to the nonlinear least squares minimization problem based upon resultants, linear algebra, and numerical techniques. On a SPARC 20, our localization algorithm runs in a few microseconds for rectilinear polygonal models, a few milliseconds for generic polygonal models, and one second for generalized polygonal models (models composed of linear edges and circular arcs).     

基于场景识别的移动机器人定位方法研究

提出了一种基于场景识别的移动机器人定位方法．对CCD采集的工作环境的系列场景图像,用多通道Gabor 滤波器提取场景图像的全局纹理特征,然后通过SVM分类器来识别场景图像,实现机器人的逻辑定位．在移动机器人CASIA-I 上对该算法进行了实验．实验结果表明,该定位方法可达到91.11%的定位准确率,对光照、对比度等因素有较强的鲁棒性,并且满足机器人实时定位的要求．     

Robust Combining of Disparate Classifiers through Order Statistics

Integrating the outputs of multiple classifiers via combiners or meta-learners has led to substantial improvements in several difficult pattern recognition problems. In this article, we investigate a family of combiners based on order statistics, for robust handling of situations where there are large discrepancies in performance of individual classifiers. Based on a mathematical modelling of how the decision boundaries are affected by order statistic combiners, we derive expressions for the reductions in error expected when simple output combination methods based on the median, the maximum and in general, the ith order statistic, are used. Furthermore, we analyse the trim and spread combiners, both based on linear combinations of the ordered classifier outputs, and show that in the presence of uneven classifier performance, they often provide substantial gains over both linear and simple order statistics combiners. Experimental results on both real world data and standard public domain data sets corroborate these findings. Received: 17 November 2000, Received in revised form: 07 November 2001, Accepted: 22 November 2001     

Autonomous Mobile Robot Localization and Navigation Using a Hierarchical Map Representation Primarily Guided by Vision

While impressive progress has recently been made with autonomous vehicles, both indoors and on streets, autonomous localization and navigation in less constrained and more dynamic environments, such as outdoor pedestrian and bicycle‐friendly sites, remains a challenging problem. We describe a new approach that utilizes several visual perception modules—place recognition, landmark recognition, and road lane detection—supplemented by proximity cues from a planar laser range finder for obstacle avoidance. At the core of our system is a new hybrid topological/grid‐occupancy map that integrates the outputs from all perceptual modules, despite different latencies and time scales. Our approach allows for real‐time performance through a combination of fast but shallow processing modules that update the map's state while slower but more discriminating modules are still computing. We validated our system using a ground vehicle that autonomously traversed three outdoor routes several times, each 400 m or longer, on a university campus. The routes featured different road types, environmental hazards, moving pedestrians, and service vehicles. In total, the robot logged over 10 km of successful recorded experiments, driving within a median of 1.37 m laterally of the center of the road, and localizing within 0.97 m (median) longitudinally of its true location along the route.     

移动机器人的鲁棒输出跟踪

本文讨论了一类不确定非完整系统的鲁棒输出跟踪问题。首先给出了在适当条件下受限系统的降阶状态实现及有关性质；进而给出了三轮移动机器人在纯滚动与非打滑条件下的简化模型，并结合变结构控制方法对该模型给出了具体的鲁棒输出跟踪控制规律。     

全向移动足球机器人的鲁棒方差控制

针对存在参数不确定性及不确定性扰动的全向移动足球机器人模型,提出具有方差约束和闭环极点约束的鲁棒控制方法。该方法对状态协方差进行最小化优化处理,使控制系统更能有效地抑制扰动的影响,同时约束系统闭环极点区域,使控制系统具有良好的动态特性,最后还对控制器的能量进行了最小化优化。基于线性矩阵不等式(LMI)方法,用Mat-lab求解器得出上述优化问题的结果,进而获得了鲁棒控制器,并通过仿真说明了该方法具有良好的控制效果。     

服务机器人分层式图形化编程平台

为了简化服务机器人编程流程,针对服务机器人任务规划特点,基于分层编程的理念,设计了一套简易图形化编程平台。平台由基本模块层、执行协调层、任务规划层3层组成,用户在不同层次采用不同编程方式,兼顾了编程效率和执行效率。同时,提供了友好的图形化编程界面,方便机器人编程。在交龙服务机器人上的编程实验验证了该平台编程的有效性和便捷性。