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 The third limitation is their lack of robustness to outliers due, e.g., to sensor malfunctions or outdated maps.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。     

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).     

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     

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。     

Bayesian Landmark Learning for Mobile Robot Localization

To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optimality, since they rely on a human to determine what aspects of the sensor data to use in localization (e.g., what landmarks to use). This paper describes a learning algorithm, called BaLL, that enables mobile robots to learn what features/landmarks are best suited for localization, and also to train artificial neural networks for extracting them from the sensor data. A rigorous Bayesian analysis of probabilistic localization is presented, which produces a rational argument for evaluating features, for selecting them optimally, and for training the networks that approximate the optimal solution. In a systematic experimental study, BaLL outperforms two other recent approaches to mobile robot localization.     

Active Visual Perception for Mobile Robot Localization

Localization is a key issue for a mobile robot, in particular in environments where a globally accurate positioning system, such as GPS, is not available. In these environments, accurate and efficient robot localization is not a trivial task, as an increase in accuracy usually leads to an impoverishment in efficiency and viceversa. Active perception appears as an appealing way to improve the localization process by increasing the richness of the information acquired from the environment. In this paper, we present an active perception strategy for a mobile robot provided with a visual sensor mounted on a pan-tilt mechanism. The visual sensor has a limited field of view, so the goal of the active perception strategy is to use the pan-tilt unit to direct the sensor to informative parts of the environment. To achieve this goal, we use a topological map of the environment and a Bayesian non-parametric estimation of robot position based on a particle filter. We slightly modify the regular implementation of this filter by including an additional step that selects the best perceptual action using Monte Carlo estimations. We understand the best perceptual action as the one that produces the greatest reduction in uncertainty about the robot position. We also consider in our optimization function a cost term that favors efficient perceptual actions. Previous works have proposed active perception strategies for robot localization, but mainly in the context of range sensors, grid representations of the environment, and parametric techniques, such as the extended Kalman filter. Accordingly, the main contributions of this work are: i) Development of a sound strategy for active selection of perceptual actions in the context of a visual sensor and a topological map; ii) Real time operation using a modified version of the particle filter and Monte Carlo based estimations; iii) Implementation and testing of these ideas using simulations and a real case scenario. Our results indicate that, in terms of accuracy of robot localization, the proposed approach decreases mean average error and standard deviation with respect to a passive perception scheme. Furthermore, in terms of efficiency, the active scheme is able to operate in real time without adding a relevant overhead to the regular robot operation.     

Robust Position Control of Assistive Robot for Paraplegics

Paraplegia refers to the paralysis of the lower limbs resulting from the damage to the spinal cord. Thus far, considerable efforts have been devoted to the rehabilitation of paraplegics and the improvement of their quality of life. This study focuses on the position control of the sit-stand mechanism of an assistive robot developed to aid paraplegics in shifting from a sitting to a standing position and vice versa. Two control techniques for the model were proposed: sliding mode control (SMC) and SMC integrated with a sliding perturbation observer (SMCSPO). The control algorithm was designed and implemented in MATLAB/Simulink. The simulation results indicate that the SMC is a nonlinear control; however, because the robot is a highly nonlinear model, which requires a high switching gain, the controller introduces chattering into the system. The SMC has been observed to exhibit inadequate performance when controlling a system with uncertainties. In contrast, the SMCSPO is a robust nonlinear control integrated with a nonlinear compensator, which performs better than the SMC even in the presence of external disturbances.

     

基于启发式搜索的移动机器人主动定位

提出了一种基于启发式搜索的主动定位算法.首先利用自适应粒子聚类算法对粒子进行聚类;然后分别构造路径规划树和解空间树,并根据优先级评估函数计算解空间树中所有节点的优先级,利用优先队列式分支限界法解决路径搜索问题;最后针对单个粒子簇分散问题提出了一种定位精度主动提升方法.仿真实验验证了所提出方法的有效性.     

Interpretation of Ultrasonic Readings for Autonomous Robot Localization

The work described in this paper is a contribution to providing mobility aid for people with motor disability. It constitutes a part of the VAHM project which aims to design a smart powered wheelchair able to control its displacements in a known environment. Original methods established for the static localisation of the wheelchair using readings provided by a belt of 14 ultrasonic sensors is presented. This approach is based on a classical matching of occupancy grids. Yet because of the presence of the person on the wheelchair any complementary movement intended to obtain additional measures is impossible. That is why our study is centred on the search for the best way to represent ultrasound measures, to model environment and to define the matching criterion in order to mitigate the imperfections of ultrasonic sensors. The method thus developed is implemented on our prototype. Examples are given of the tests carried out in real-life conditions in a typical environment consisting of a flat recreated in our laboratory. The results obtained using real and simulated readings show that the approach is reliable and fitted to our project.     

Optimal Robot Localization in Trees

The problem of localization, that is, of a robot finding its position on a map, is an important task for autonomous mobile robots. It has applications in numerous areas of robotics ranging from aerial photography to autonomous vehicle exploration. In this paper we present a new strategy LPS (Localize-by-Placement-Separation) for a robot to find its position on a map, where the map is represented as a geometric tree of bounded degree. Our strategy exploits to a high degree the self-similarities that may occur in the environment. We use the framework of competitive analysis to analyze the performance of our strategy. In particular, we show that the distance traveled by the robot is at most O( ) times longer than the shortest possible route to localize the robot, where n is the number of vertices of the tree. This is a significant improvement over the best known previous bound of O(n^2/3). Moreover, since there is a lower bound of Ω( ), our strategy is optimal up to a constant factor. Using the same approach we can also show that the problem of searching for a target in a geometric tree, where the robot is given a map of the tree and the location of the target but does not know its own position, can be solved by a strategy with a competitive ratio of O( ), which is again optimal up to a constant factor.     

Reinforcement Learning and Robust Control for Robot Compliance Tasks

The complexity in planning and control of robot compliance tasks mainly results from simultaneous control of both position and force and inevitable contact with environments. It is quite difficult to achieve accurate modeling of the interaction between the robot and the environment during contact. In addition, the interaction with the environment varies even for compliance tasks of the same kind. To deal with these phenomena, in this paper, we propose a reinforcement learning and robust control scheme for robot compliance tasks. A reinforcement learning mechanism is used to tackle variations among compliance tasks of the same kind. A robust compliance controller that guarantees system stability in the presence of modeling uncertainties and external disturbances is used to execute control commands sent from the reinforcement learning mechanism. Simulations based on deburring compliance tasks demonstrate the effectiveness of the proposed scheme.