A graph approach to generate all possible regression submodels

A regression graph to enumerate and evaluate all possible subset regression models is introduced. The graph is a generalization of a regression tree. All the spanning trees of the graph are minimum spanning trees and provide an optimal computational procedure for generating all possible submodels. Each minimum spanning tree has a different structure and characteristics. An adaptation of a branch-and-bound algorithm which computes the best-subset models using the regression graph framework is proposed. Experimental results and comparison with an existing method based on a regression tree are presented and discussed.     

Neural networks for the EMOBOT robot control architecture

Within the EMOBOT approach to adaptive behaviour, the task of learning to control the behaviour is one of the most interesting challenges. Learned action selection between classically implemented control mechanisms, with respect to internal values and sensor readings, provides a way to modulate a variety of behavioural capabilities. To demonstrate the potential of the learning emotional controller, we chose a 10-5-12 MLP to implement the , controller of the EMOBOT. Since no teacher vector is available for the chosen task, the neural network is trained with a reinforcement strategy. The emotion-value-dependent reinforcement signal, together with the output of the network, is the basis with which to compute an artificial teacher vector. Then, the established gradient descent method (backpropagation of error) is applied to train the neural network. First results obtained by extensive simulations show that a still unrevealed richness in behaviour can be realised when using the neural-network-based learning emotional controller.     

Continuous case-based reasoning

Case-based reasoning systems have traditionally been used to perform high-level reasoning in problem domains that can be adequately described using discrete, symbolic representations. However, many real-world problem domains, such as autonomous robotic navigation, are better characterized using continuous representations. Such problem domains also require continuous performance, such as on-line sensorimotor interaction with the environment, and continuous adaptation and learning during the performance task. This article introduces a new method for continuous case-based reasoning, and discusses its application to the dynamic selection, modification, and acquisition of robot behaviors in an autonomous navigation system, SINS (self-improving navigation system). The computer program and the underlying method are systematically evaluated through statistical analysis of results from several empirical studies. The article concludes with a general discussion of case-based reasoning issues addressed by this research.     

Object–object interaction affordance learning

This paper presents a novel object–object affordance learning approach that enables intelligent robots to learn the interactive functionalities of objects from human demonstrations in everyday environments. Instead of considering a single object, we model the interactive motions between paired objects in a human–object–object way. The innate interaction-affordance knowledge of the paired objects are learned from a labeled training dataset that contains a set of relative motions of the paired objects, human actions, and object labels. The learned knowledge is represented with a Bayesian Network, and the network can be used to improve the recognition reliability of both objects and human actions and to generate proper manipulation motion for a robot if a pair of objects is recognized. This paper also presents an image-based visual servoing approach that uses the learned motion features of the affordance in interaction as the control goals to control a robot to perform manipulation tasks.     

Simultaneous localization and odometry self calibration for mobile robot

This paper presents both the theory and the experimental results of a method allowing simultaneous robot localization and odometry error estimation (both systematic and non-systematic) during the navigation. The estimation of the systematic components is carried out through an augmented Kalman filter, which estimates a state containing the robot configuration and the parameters characterizing the systematic component of the odometry error. It uses encoder readings as inputs and the readings from a laser range finder as observations. In this first filter, the non-systematic error is defined as constant and it is overestimated. Then, the estimation of the real non-systematic component is carried out through another Kalman filter, where the observations are obtained by two subsequent robot configurations provided by the previous augmented Kalman filter. There, the systematic parameters in the model are regularly updated with the values estimated by the first filter. The approach is theoretically developed for both the synchronous and the differential drive. A first validation is performed through very accurate simulations where both the drive systems are considered. Then, a series of experiments are carried out in an indoor environment by using a mobile platform with a differential drive.     

Gaussian fields for semi-supervised regression and correspondence learning

Gaussian fields (GF) have recently received considerable attention for dimension reduction and semi-supervised classification. In this paper we show how the GF framework can be used for semi-supervised regression on high-dimensional data. We propose an active learning strategy based on entropy minimization and a maximum likelihood model selection method. Furthermore, we show how a recent generalization of the LLE algorithm for correspondence learning can be cast into the GF framework, which obviates the need to choose a representation dimensionality.     

Using roadmaps to classify regions of space for autonomous robot navigation

The roadmap approach to robot path planning is one of the earliest methods. Since then, many different algorithms for building roadmaps have been proposed and widely implemented in mobile robots but their use has always been limited to planning in static, totally known environments. In this paper we combine the use of dynamic analogical representations of the environment with an efficient roadmap extraction method, to guide the robot navigation and to classify the different regions of space in which the robot moves. The paper presents the general reference architecture for the robotic system and then focuses on the algorithms for the construction of the roadmap, the classification of the regions of space and their use in robot navigation. Experimental results indicate the applicability and robustness of this approach in real situations.     

Learning decision trees from decision rules: A method and initial results from a comparative study

A standard approach to determining decision trees is to learn them from examples. A disadvantage of this approach is that once a decision tree is learned, it is difficult to modify it to suit different decision making situations. Such problems arise, for example, when an attribute assigned to some node cannot be measured, or there is a significant change in the costs of measuring attributes or in the frequency distribution of events from different decision classes. An attractive approach to resolving this problem is to learn and store knowledge in the form of decision rules, and to generate from them, whenever needed, a decision tree that is most suitable in a given situation. An additional advantage of such an approach is that it facilitates buildingcompact decision trees, which can be much simpler than the logically equivalent conventional decision trees (by compact trees are meant decision trees that may contain branches assigned aset of values, and nodes assignedderived attributes, i.e., attributes that are logical or mathematical functions of the original ones). The paper describes an efficient method, AQDT-1, that takes decision rules generated by an AQ-type learning system (AQ15 or AQ17), and builds from them a decision tree optimizing a given optimality criterion. The method can work in two modes: thestandard mode, which produces conventional decision trees, andcompact mode, which produces compact decision trees. The preliminary experiments with AQDT-1 have shown that the decision trees generated by it from decision rules (conventional and compact) have outperformed those generated from examples by the well-known C4.5 program both in terms of their simplicity and their predictive accuracy.     

基于MLP和SPSO的机器人行为选择与运动控制方法

针对传统行为选择机制(ASM)不能很好地做出控制决策的问题,提出一种基于多层感知(MLP)前馈神经网络的ASM,并将其应用到移动机器人目标跟踪中。首先,根据具体应用场景预定义多个机器人行为。然后,根据机器人配备的图像和红外传感器获得的目标位置和障碍物信息,通过MLP神经网络从预定义行为中选择出所需执行的行为。另外,为了构造最优的MLP模型,采用一种简化粒子群算法(SPSO)来优化网络权值参数。机器人目标跟踪仿真的结果表明,提出的ASM能够准确选择出合适的行为,实现了控制机器人跟踪目标移动且能够避开各种障碍物。     

双足足球机器人守门策略研究

首先提出了双足足球机器人的体系结构,然后给出了基本步态算法,针对FIRA类人型项目比赛规则,提出了全新的双足足球机器人守门策略,最后给出了分析测距试验数据,验证了测距准确性和守门策略的合理性.     

门牌识别系统中的鲁棒性分割方法

Robot navigation based on character recognition is an effective vision method for compensating the disadvantage of ultrasonic and infrared sensors. A typical example of character recognition for mobile robot navigation is the doorplate recognition system. The captured doorplate images contain unexpected noise from irregular illumination conditions, various imaging angles, different imaging distances, etc. The unexpected noise may still exist after segmentation step. In this paper, a robust segmentation method based on speculating the candidates of the characters and feeding back the classification result to the segmentation process is presented. If the candidates of doorplate characters cannot be determined at the segmentation step, a speculation according to known knowledge is executed. The threshold for character extraction from candidates is adjusted when the corresponding character is rejected after classification. The experimental results indicate that the recognition results are effectively improved with the proposed segmentation method.     

Robust Segmentation Method for Doorplate Recognition System

Robot navigation based on character recognition is an effective vision method for com- pensating the disadvantage of ultrasonic and infrared sensors.A typical example of character recog- nition for mobile robot navigation is the doorplate recognition system.The captured doorplate images contain unexpected noise from irregular illumination conditions,various imaging angles,dif- ferent imaging distances,etc.The unexpected noise may still exist after segmentation step.In this paper,a robust segmentation method based on speculating the candidates of the characters and feeding back the classification result to the segmentation process is presented.If the candidates of doorplate characters cannot be determined at the segmentation step,a speculation according to known knowledge is executed.The threshold for character extraction from candidates is adjusted when the corresponding character is rejected after classification.The experimental results indicate that the recognition results are effectively improved with the proposed segmentation method.     

一种基于视觉的移动机器人定位系统

具有自主的全局定位能力是自主式稳定机器人传感器系统的一项重要功能,为了实现这个目的,国内外均在不断地研究发展各种定位传感器系统,这里介绍了一种采用光学蝗全方位位置传感器系统,该传感器系统由主动式路标、视觉传感器、图象采集与数据处理系统组成,其视觉传感器和数据处理系统可安装在移动机器人上,然后可通过观测路标物「视角定位的方法,计算出机器人在世界坐标系中的位置和方向,实验证明,该系统可以只的在线定位,     

A model-based sound localization system and its application to robot navigation

This paper describes a mobile robot equipped with a real time sound localization system as well as a sonar system for obstacle detection. The sound localization method is based on a model of the precedence effect of the human auditory system to cope with echoes and reverberations. Sound localization and robot navigation experiments were conducted. The results show that the robot is capable of localizing sounding objects in a reverberant environment and approaching the objects without collisions, even when the objects were behind obstacles. Environment flexibility and error robustness of the system were discussed as well.     

Navigation of an AUV for investigation of underwater structures

There is a great demand for autonomous underwater vehicles (AUVs) to investigate artificial underwater structures such as piles and caissons in harbours, and risers and jackets of deep-sea oilfields. This paper proposes an autonomous investigation method of underwater structures using AUVs that is implemented by initially detecting the target objects, localizing them, then approaching them by taking video images while closely tracing their shape. A laser ranging system and a navigation method based on the relative position with respect to the target objects are introduced to realize this behaviour.     

Novel ensemble methods for regression via classification problems

Regression via classification (RvC) is a method in which a regression problem is converted into a classification problem. A discretization process is used to covert continuous target value to classes. The discretized data can be used with classifiers as a classification problem. In this paper, we use a discretization method, Extreme Randomized Discretization (ERD), in which bin boundaries are created randomly to create ensembles. We present two ensemble methods for RvC problems. We show theoretically that the proposed ensembles for RvC perform better than RvC with the equal-width discretization method. We also show the superiority of the proposed ensemble methods experimentally. Experimental results suggest that the proposed ensembles perform competitively to the method developed specifically for regression problems.     

A virtual environment-based system for the navigation of underwater robots

Efficient teleoperation of underwater robot requires clear 3D visual information of the robot's spatial location and its surrounding environment. However, the performance of existing telepresence systems is far from satisfactory. In this paper, we present our virtual telepresence system for assisting tele-operation of an underwater robot. This virtual environment-based telepresence system transforms robot sensor data into 3D synthetic visual information of the workplace based on its geometrical model. It provides the operators with a full perception of the robot's spatial location. In addition, we propose a robot safety domain to overcome the robot's location offset in the virtual environment caused by its sensor errors. The software design of the system and how a safety domain can be used to overcome robot location offset in virtual environment will be examined. Experimental tests and its result analysis will also be presented in this paper.