Simultaneous localization and map building for mobile robotnavigation

Inexpensive ultrasonic sensors, incremental encoders, and grid-based probabilistic modeling are used for improved robot navigation in indoor environments. For model-building, range data from ultrasonic sensors are constantly sampled and a map is built and updated immediately while the robot is travelling through the workspace. The local world model is based on the concept of an occupancy grid. The world model extracted from the range data is based on the geometric primitive of line segments. For the extraction of these features, methods such as the Hough transform and clustering are utilized. The perceived local world model along with dead-reckoning and ultrasonic sensor data are combined using an extended Kalman filter in a localization scheme to estimate the current position and orientation of the mobile robot, which is subsequently fed to the map-building algorithm. Implementation issues and experimental results with the Nomad 150 mobile robot in a real-world indoor environment (office space) are presented     

室内机器人及其路径跟踪网络设计

近年来,室内移动机器人的研究和设计成为关注的焦点。我们采用单片机作为机器人的核心控制器,利用超声波传感器、碰撞传感器、步进电机及其控制芯片Ta8435联合制作开发了机器人实验平台。最后介绍了模糊控制、模糊神经网络,并利用模糊控制和模糊神经网络技术对室内机器人导航中的模糊控制避障和模糊神经网络路径跟踪作了MATLAB仿真研究,达到了预期的目的。     

An optimized modular neural network controller based on environment classification and selective sensor usage for mobile robot reactive navigation

A new approach to the design of a neural network (NN) based navigator is proposed in which the mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment. This navigator can be optimized for any user-defined objective function through the use of an evolutionary algorithm. The motivation of this research is to develop an efficient methodology for general goal-directed navigation in generic indoor environments as opposed to learning specialized primitive behaviors in a limited environment. To this end, a modular NN has been employed to achieve the necessary generalization capability across a variety of indoor environments. Herein, each NN module takes charge of navigating in a specialized local environment, which is the result of decomposing the whole path into a sequence of local paths through clustering of all the possible environments. We verify the efficacy of the proposed algorithm over a variety of both simulated and real unstructured indoor environments using our autonomous mobile robot platform.     

Application of ultrasonic sensors in a smart environment

A key application of sensor networks in smart environments is in monitoring activities of people. We develop several scenarios in which ultrasonic sensors are used for monitoring of patients and the elderly. In each scenario, we apply different algorithms for data fusion and sensor selection using quality-based or time division approaches. We have devised trajectory-matching algorithms to classify trajectories of movement of people in indoor environments. The trajectories are divided into several routine classes and the current trajectory is compared against the known routine trajectories. The initial results are quite promising, and show the potential usability of ultrasonic sensors in monitoring indoor movements of people, and in capturing and classifying trajectories.     

Guide robot intelligent navigation in urban environments

This paper presents an intelligent robotic system to guide visually impaired people in urban environments. The robot is equipped with two laser range finders, global positioning system (GPS), camera, and compass sensors. All the sensors data are processed by a single laptop computer. We have implemented different navigation algorithms enabling the robot to move autonomously in different urban environments. In pedestrian walkways, we utilize the distance to the edge (left, right, or both) to determine the robot steering command. In difference from pedestrian walkways, in open squares where there is no edge information, artificial neural networks map the GPS and compass sensor data to robot steering command guiding the visually impaired to the goal location. The neural controller is designed such as to be employed even in environments different from those in which they have been evolved. Another important advantage is that a single neural network controls the robot to reach multiple goal locations inside the open square. The proposed algorithms are verified experimentally in a navigation task inside the University of Toyama Campus, where the robot moves from the initial to goal location.     

An approach to learning mobile robot navigation

This paper describes an approach to learning an indoor robot navigation task through trial-and-error. A mobile robot, equipped with visual, ultrasonic and laser sensors, learns to servo to a designated target object. In less than ten minutes of operation time, the robot is able to navigate to a marked target object in an office environment. The central learning mechanism is the explanation-based neural network learning algorithm (EBNN). EBNN initially learns function purely inductively using neural network representations. With increasing experience, EBNN employs domain knowledge to explain and to analyze training data in order to generalize in a more knowledgeable way. Here EBNN is applied in the context of reinforcement learning, which allows the robot to learn control using dynamic programming.     

A robot self-localization system based on omnidirectional color images

A self-localization system for autonomous mobile robots is presented. This system estimates the robot position in previously learned environments, using data provided solely by an omnidirectional visual perception subsystem composed of a camera and of a special conical reflecting surface. It performs an optical pre-processing of the environment, allowing a compact representation of the collected data. These data are then fed to a learning subsystem that associates the perceived image to an estimate of the actual robot position. Both neural networks and statistical methods have been tested and compared as learning subsystems. The system has been implemented and tested and results are presented.     

Vision‐based terrain characterization and traversability assessment

This article presents novel techniques for real‐time terrain characterization and assessment of terrain traversability for a field mobile robot using a vision system and artificial neural networks. The key terrain traversability characteristics are identified as roughness, slope, discontinuity, and hardness. These characteristics are extracted from imagery data obtained from cameras mounted on the robot and are represented in a fuzzy logic framework using perceptual, linguistic fuzzy sets. The approach adopted is highly robust and tolerant to imprecision and uncertainty inherent in sensing and perception of natural environments. The four traversability characteristics are combined to form a single Fuzzy Traversability Index, which quantifies the ease‐of‐traversal of the terrain by the mobile robot. Experimental results are presented to demonstrate the capability of the proposed approach for classification of different terrain segments based on their traversability. © 2001 John Wiley & Sons, Inc.     

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.     

Intelligent System for Robotic Navigation Using ANFIS and ACOr

In this article we propose an intelligent system for mobile robot navigation in different environments, using ANFIS and ACOr. This system is capable of ensuring to mobile robot to navigate by reacting to the various situations encountered in different environments. In a first step, we use the ANFIS controller (Adaptive network-based fuzzy inference system) in which the contribution of the fuzzy logic of TAKAJI-SUGENO is added to that of the neural networks in a suitable way. In the second step, the ant colony method in a continuous environment ACOr (Ant colony optimization for continuous domains) is grafted into the second layer of the ANFIS network for hybridization. Simulations of the movements of the robot and the graphic interfaces are realized under the C ++ language.     

Reactive navigation in dynamic environment using a multisensorpredictor

A reactive navigation system for an autonomous mobile robot in unstructured dynamic environments is presented. The motion of moving obstacles is estimated for robot motion planning and obstacle avoidance. A multisensor-based obstacle predictor is utilized to obtain obstacle-motion information. Sensory data from a CCD camera and multiple ultrasonic range finders are combined to predict obstacle positions at the next sampling instant. A neural network, which is trained off-line, provides the desired prediction on-line in real time. The predicted obstacle configuration is employed by the proposed virtual force based navigation method to prevent collision with moving obstacles. Simulation results are presented to verify the effectiveness of the proposed navigation system in an environment with multiple mobile robots or moving objects. This system was implemented and tested on an experimental mobile robot at our laboratory. Navigation results in real environment are presented and analyzed.     

冗余度机器人运动模型的神经网络辨识及实现

为提高网络学习速度,提出了一种新的动态神经网络结构——状态延迟输入动态递归神经网络.以德国PowerCube^TM模块化机器人为研究对象,将机器人系统返回的关节位置信息和OPTOTRAK30203维运动测量系统测得的机器人末端位置信息作为神经网络的学习样本,对包含各种影响因素的机器人运动模型进行了辨识,所得结果及误差分析,说明了SDIDRNN在学习能力上的优越性.     

模糊神经网络信息融合方法在机器人避障中的应用

基于Takagi—Sugeno(T—S)模型的模糊神经网络不但具有模糊逻辑和神经网络两者的优点，又具有很好的学习能力。将基于T—S模型的模糊神经网络的信息融合算法应用在移动机器人的避障运动中，采用了多个超声测距传感器探测障碍物的距离和方向，经过模糊神经网络信息融合后，实现了机器人对障碍物和环境类型的识别以及无冲突的运动。实验表明：此方法能够使机器人安全避障。     

Cooperative control of multiple neural networks for an indoor blimp robot

We report on the cooperative control of multiple neural networks for an indoor blimp robot. In our research group, the indoor blimp robot has been studied to achieve various flying robot applications. The objective of this article is to propose a robust controller that can adapt to mechanical accidents such as the breakdown of propellers. In our proposed method, each propeller thrust is independently calculated by a small neural network. We confirm the advantage of the proposed method against the control by a single large neural network. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

记忆神经网络在机器人导航领域的应用与研究进展

记忆神经网络非常适合解决时间序列决策问题,将其用于机器人导航领域是非常有前景的新兴研究领域。本文主要讨论记忆神经网络在机器人导航领域的研究进展。给出几种基本记忆神经网络结合导航任务的工作机理,总结了不同模型的优缺点;对记忆神经网络在导航领域的研究进展进行简要综述;进一步介绍导航验证环境的发展;最后梳理了记忆神经网络在导航问题所面临的复杂性挑战,并预测了记忆神经网络在导航领域未来的发展方向。     

基于多传感器的家庭服务机器人局部导航方法研究

本文提出了一种基于多传感器的家庭服务机器人局部导航方法。首先,采用单个摄像头获取居室内障碍物的图像信息,利用超声波传感器和红外线传感器探测障碍物的距离信息。然后,据此计算在机器人运动方向上障碍物的遮挡空间或者多个障碍物之间的实际距离,再根据机器人自身的大小计算避开障碍物应该转动的方向及角度,从而实现居室内的自主导航。最后,仿真实验结果证明了该方法的有效性。     

Multiobjective Evolution of Neural Controllers and Task Complexity

Robots operating in everyday life environments are often required to switch between different tasks. While learning and evolution have been effectively applied to single task performance, multiple task performance still lacks methods that have been demonstrated to be both reliable and efficient. This paper introduces a new method for multiple task performance based on multiobjective evolutionary algorithms, where each task is considered as a separate objective function. In order to verify the effectiveness, the proposed method is applied to evolve neural controllers for the Cyber Rodent (CR) robot that has to switch properly between two distinctly different tasks: 1) protecting another moving robot by following it closely and 2) collecting objects scattered in the environment. Furthermore, the tasks and neural complexity are analyzed by including the neural structure as a separate objective function. The simulation and experimental results using the CR robot show that the multiobjective-based evolutionary method can be applied effectively for generating neural networks that enable the robot to perform multiple tasks simultaneously.     

管道内腐蚀缺陷超声在线检测机器人设计

针对管道内腐蚀缺陷超声在线检测数据量大、灵敏度低的难题,采用超声反射单脉冲距离检测法,设计制造了一种新型长输管道缺陷检测机器人.设计了满足检测精度的探头阵列、机器人结构以及检测数据处理系统;进行了标准模拟缺陷检测实验,校准了阵列探头的性能.实验表明,所设计机器人检测数据量小,其检测灵敏度可达到检测10 mm×10 mm×1 mm(长×宽×深)的缺陷.     

Autonomous exploration using rapid perception of low-resolution image information

We present a technique for mobile robot exploration in unknown indoor environments using only a single forward-facing camera. Rather than processing all the data, the method intermittently examines only small 32×24 downsampled grayscale images. We show that for the task of indoor exploration the visual information is highly redundant, allowing successful navigation even using only a small fraction of the available data. The method keeps the robot centered in the corridor by estimating two state parameters: the orientation within the corridor, and the distance to the end of the corridor. The orientation is determined by combining the results of five complementary measures, while the estimated distance to the end combines the results of three complementary measures. These measures, which are predominantly information-theoretic, are analyzed independently, and the combined system is tested in several unknown corridor buildings exhibiting a wide variety of appearances, showing the sufficiency of low-resolution visual information for mobile robot exploration. Because the algorithm discards such a large percentage of the pixels both spatially and temporally, processing occurs at an average of 1000 frames per second, thus freeing the processor for other concurrent tasks.