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。     

基于多传感器的移动机器人路径规划

提出一种基于多传感器的移动机器人路径规划策略。利用声纳传感器和CCD摄像机对环境进行探测,得到关于障碍物的信息,通过一种简单、快速的数据融合算法计算出障碍物相对于机器人的位置坐标。采用切线法进行路径规划,实现了移动机器人在不确定环境下的路径规划,使机器人可以很好地避开障碍物,并以局部最优或次最优路径到达指定位置。实验结果验证了该路径规划算法的良好性能。     

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.     

A nonparametric learning approach to range sensing from omnidirectional vision

We present a novel approach to estimating depth from single omnidirectional camera images by learning the relationship between visual features and range measurements available during a training phase. Our model not only yields the most likely distance to obstacles in all directions, but also the predictive uncertainties for these estimates. This information can be utilized by a mobile robot to build an occupancy grid map of the environment or to avoid obstacles during exploration—tasks that typically require dedicated proximity sensors such as laser range finders or sonars. We show in this paper how an omnidirectional camera can be used as an alternative to such range sensors. As the learning engine, we apply Gaussian processes, a nonparametric approach to function regression, as well as a recently developed extension for dealing with input-dependent noise. In practical experiments carried out in different indoor environments with a mobile robot equipped with an omnidirectional camera system, we demonstrate that our system is able to estimate range with an accuracy comparable to that of dedicated sensors based on sonar or infrared light.     

模糊神经网络信息融合在管道机器人导航中的应用

考虑到石油管道的封闭性和复杂性,很难识别环境特征,将基于模糊神经网络的多传感器信息融合用于解决管道中管道机器人的导航问题。采用CCD摄像头和距离传感器来识别管道中的障碍物和弯道,并根据环境信息制定控制决策。建立了机器人物理模型和模糊神经网络拓扑结构,并对神经网络进行了学习训练。最后,对其中一种环境类型进行了仿真验证,证实了算法的有效性。     

神经网络的多传感器数据融合基于新算法在障碍物识别中的应用

本文提出了一种用于自主式移动机器人的障碍物类型识别的数据融合新方法，有两种不同的神经网络——小脑模型联接控制器（ＣＭＡＣ）和多层前向网分别对来自ＣＣＤ摄象机的二维图象和来自超声测距系统的距离信息进行数据融合，而这两种神经网络事先都用围绕障碍物采集的数据集进行过离线训练．为了验证该系统的有效性，我们构造了一系列的仿真实验．实验结果表明，一台个人计算机就能实时地识别出障碍物的类型．     

移动机器人的多传感器测距系统设计

在移动机器人的路径规划过程中,必须掌握障碍物的距离信息.基于超声波和红外传感器的测距原理,设计了一种移动机器人多传感器测距系统,可测量0～200 cm距离内存在的障碍物,测量误差小于1 %.采用超声波和红外2种传感器组成3组测距采集系统,采集机器人3个不同方位的障碍物信息,解决了单一传感器测距盲区的问题,并详细介绍了该系统的软件和硬件设计.     

一种安全工作于城区环境的消防侦察机器人

介绍了一种应用于城区环境的消防侦察机器人．它采用具有关节链轮的差动轮式结构,适于爬楼梯和斜坡．采用实时泄漏补偿方式维持车体内具有恒定正压,确保机器人的防爆安全性能．在失去无线控制信号时,机器人根据侦察过程中记录的传感器数据,按原路径返回,并自动回撤到安全区域．由于使用单一的编码器获得的位置信息具有累积误差,本文采用新颖的编码器与超声波传感器数据集成的方法,并使用迭代最近点算法进行数据匹配,调整机器人的位置和方向角,获得了较高的自动回撤精度．     

Acoustic and video sensor integration for object recognition

Through our studies of methods for measuring the shapes of objects in three-dimensional object recognition, we have developed a method for constructing detailed solid object images that employs the fusion of the data from acoustic sensors and a charge-coupled device (CCD) camera. This method uses a matrix of ultrasonic sensors to obtain data on the position and height of the object. These data are used to automatically extract the two-dimensional images of the object from gray-scale camera images. By combining the results with distance information, a detailed solid image of the object is obtained. This method produces markedly better resolution than using acoustic data alone. Thus, by using it in combination with a neural network recognition mechanism, it is possible to automatically recognize small objects that are difficult to distinguish by means of acoustic sensing alone, even if they can be detected. This paper reports the newly developed sensor fusion mechanism, presents the results of experiments on an experimental system, and discusses the features of the method.     

Obstacle arrangement detection using multichannel ultrasonic sonar for indoor mobile robots

In the last few years, mobile robot systems that perform complicated tasks have been studied. To work in complicated environments, the robot has to avoid collisions with obstacles. Therefore the robot needs to detect the arrangement of any surrounding obstacles. We considered a simple distance estimation algorithm using ultrasonic sonar. Since the algorithm was able to estimate distance accurately, we also attempted stereo reception using two ultrasonic microphones. The stereo reception sonar was able to detect the direction of obstacles. In order to make precise measurements, we attempted to use the signal coherence of ultrasonic waves. In order to install a small system into mobile robots and to detect any surrounding obstacles, we designed a multichannel sonar signal processing system using a high-performance embedded microcontroller. This article describes our ideas for the distance estimation algorithm for ultrasonic sonar, and a design for a signal processing system using a high-performance microcontroller.     

Visual navigation and obstacle avoidance using a steering potential function

Humans have a remarkable ability to navigate using only vision, but mobile robots have not been nearly as successful. We propose a new approach to vision-guided local navigation, based upon a model of human navigation. Our approach uses the relative headings to the goal and to obstacles, the distance to the goal, and the angular width of obstacles, to compute a potential field over the robot heading. This potential field controls the angular acceleration of the robot, steering it towards the goal and away from obstacles. Because the steering is controlled directly, this approach is well suited to local navigation for nonholonomic robots. The resulting paths are smooth and have continuous curvature. This approach is designed to be used with single-camera vision without depth information but can also be used with other kinds of sensors. We have implemented and tested our method on a differential-drive robot and present our experimental results.     

超声在移动机器人障碍物检测中的应用研究

在移动机器人的相关技术的研究中,移动机器人障碍物检测是机器人研究的一个重要方向。以上海英集斯自动化技术公司生产的MT-R机器人为研究对象,首先利用其内部安装的超声传感器及相关软件测量机器人前方障碍物的距离,得出测量结果,并分析误差原因;其次利用机器人前面三个超声传感器进行避障实验,运行过程基本能够满足一般要求,但对特殊障碍如有桌洞的障碍物,机器人钻入桌洞,无法避开。单独采用超声传感器不能满足机器人对障碍物的精确识别,有必要结合其他传感器提高障碍物的测量精度。     

基于多传感器信息融合的移动机器人避障

为了更好地解决移动机器人在未知环境下的自主避障问题,采用多传感器信息融合的方法,通过多个超声传感器对障碍物信息进行采集。合理确立模糊控制器的输入输出,通过模糊推理将障碍物距离信息模糊化,建立模糊规则并解模糊,以达到对移动机器人的安全避障的控制。通过建立移动机器人运动模型,设计了仿真平台,得到实验结果表明:该算法具有良好的可行性。     

基于多传感器融合的机器人障碍物检测和识别

该文研究了在移动机器人中基于神经网络的多种传感器信息融合技术。通过由CCD摄像机获得的彩色图像与超声波传感器组获得的距离作为辅助视觉系统实现整个视觉系统功能。移动机器人的障碍物检测和识别的可靠性与精度比在任何单一传感器所获得的信息都有很大的提高。     

Development of distance recognition using an ocellus camera for an autonomous personal robot

We are attempting to develop an autonomous personal robot that has the ability to perform practical tasks in a human living environment by using information derived from sensors. When a robot operates in a human environment, the issue of safety must be considered in regard to its autonomous movement. Thus, robots absolutely require systems that can recognize the external world and perform correct driving control. We have thus developed a navigation system for an autonomous robot. The system requires only image data captured by an ocellus CCD camera. In this system, we allow the robot to search for obstacles present on the floor. Then, the robot obtains distance recognition necessary for evasion of the object, including data of the obstacle’s width, height, and depth by calculating the angles of images taken by the CCD camera. We applied the system to a robot in an indoor environment and evaluated its performance, and we consider the resulting problems in the discussion of our experimental results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Localization of a mobile robot based on an ultrasonic sensor using dynamic obstacles

In this article, we propose a localization scheme for a mobile robot based on the distance between the robot and moving objects. This method combines the distance data obtained from ultrasonic sensors in a mobile robot, and estimates the location of the mobile robot and the moving object. The movement of the object is detected by a combination of data and the object’s estimated position. Then, the mobile robot’s location is derived from the a priori known initial state. We use kinematic modeling that represents the movement of a robot and an object. A Kalman-filtering algorithm is used for addressing estimation error and measurement noise. Throughout the computer simulation experiments, the performance is verified. Finally, the results of experiments are presented and discussed. The proposed approach allows a mobile robot to seek its own position in a weakly structured environment. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

A corner differentiation algorithm by a single sonar sensor for mobile robots

Ultrasonic sensors have been widely applied in mobile robots to obtain environmental information and avoid obstacles. In general, a typical domestic environment consists of planes, edges and corners. It is usually difficult to distinguish a plane from a corner directly with a single ultrasonic sensor. To overcome this difficulty, a corner differentiation algorithm for a single ultrasonic sensor is proposed in this paper. The algorithm is based on the features of all of the actual reflection points from the environment obtained by a reflection search algorithm from which the corners are realized by a corner differentiation algorithm. The experimental results show that the developed algorithm can successfully detect all planes and corners. Furthermore, an environmental map can be built based on the information obtained on planes and corners. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Attention-driven monocular scene reconstruction for obstacle detection, robot navigation and map building

In this paper, we present a feature-based approach for monocular scene reconstruction based on Extended Kalman Filters (EKF). Our method processes a sequence of images taken by a single camera mounted frontally on a mobile robot. Using a combination of various techniques, we are able to produce a precise reconstruction that is free from outliers and can therefore be used for reliable obstacle detection and 3D map building. Furthermore, we present an attention-driven method that focuses the feature selection to image areas where the obstacle situation is unclear and where a more detailed scene reconstruction is necessary. In extensive real-world field tests we show that the presented approach is able to detect obstacles that are not seen by other sensors, such as laser range finders. Furthermore, we show that visual obstacle detection combined with a laser range finder can increase the detection rate of obstacles considerably, allowing the autonomous use of mobile robots in complex public and home environments.     

Navigation system for an autonomous robot using an ocellus camera in an indoor environment

Autonomous and mobile robots are being expected to provide various services in human living environments. However, many problems remain to be solved in the development of autonomous robots that can work like humans. When a robot moves, it is important that it be able to have self-localization abilities and recognize obstacles. For a human, the present location can be correctly checked through a comparison between memorized information assuming, it is correct, and the present situation. In addition, the distance to an object and the perception of its size can be estimated by a sense of distance based on memory or experience. Therefore, the environment for robotic activity assumed in this study was a finite-space such as a family room, an office, or a hospital room. Because an accurate estimation of position is important to the success of a robot, we have developed a navigation system with self-localization ability which uses only a CCD camera that can detect whether the robot is moving accurately in a room or corridor. This article describes how this system has been implemented and tested with our developed robot.