Workspace mapping based on multisensor information fusion using heterogeneous onboard sensors

In recent years, multiple robot systems that perform team operations have been developed. These robot systems are expected to execute complicated tasks smoothly in a given congested workspace. In this article, we propose a workspace mapping algorithm using ultrasonic stereo sonar and an image sensor in order to operate the mobile robots among obstacles. This workspace mapping algorithm involves two steps: (1) the position detection of obstacles using ultrasonic stereo sonar, and (2) the shape detection of obstacles using an image sensor. While each robot moves around in the given workspace, the two steps of the mapping algorithm are repeated and sensor data are collected. The robot measures the distance and the direction of obstacles using ultrasonic stereo sonar. The shape of obstacles is also captured using an onboard image sensor. A workspace map is created based on the sensor data accumulated from the proposed method, and successful results are also obtained through experiments.     

A Navigation Aid for Blind People

This paper presents a navigation aid for the blind based on a microcontroller with synthetic speech output. The system consists of two vibrators, two ultrasonic sensors mounted on the user??s shoulders and another one integrated into the cane. It is able to give information to the blind about urban walking routes and to provide real-time information on the distance of over-hanging obstacles within 6 m along the travel path ahead of the user. The suggested system can then sense the surrounding environment via sonar sensors and sending vibro-tactile feedback to the user of the position of the closest obstacles in range. For the ultrasonic cane, it is used to detect any obstacle on the ground. Experimental results show the effectiveness of the proposed system for blind navigation.     

基于改进RANSAC的消防机器人双目障碍检测

针对消防机器人自主作业的障碍物快速检测问题,给出了一种基于改进随机采样一致性估计的双目障碍物检测算法。该算法首先采集双目视觉左右视图,进行半全局立体匹配获取视差信息,然后采用随机采样一致性估计的平面拟合法提取地平面模型,并采用预检验法和内点阈值限定法同时对随机采样一致性估计进行改进,从而提高算法效率,实现障碍物快速检测。实验结果证明该方法能够准确、快速检测障碍物,满足消防机器人作业需求。     

基于声纳环传感器的机器人避障研究

任何一种移动机器人要实现未知环境自主导航都必须有效而可靠地感知环境信息,而超声波传感器在检测障碍物距离信息方面应用十分广泛。介绍了旅行家II号声纳环传感系统的设计与实现原理,并对声纳的精度进行了测试。在此基础上,提出了移动机器人一种简单避障策略,并运用2种基本避障实验:静态避障和动态避障,验证了该避障策略的正确性和有效性。     

Mobile robot map making using sonar

This article describes a method of producing high-resolution maps of an indoor environment with an autonomous mobile robot equipped with sonar range-finding sensors. This method is based on investigating obstacles in the near vicinity of a mobile robot. The mobile robot examines the straight line segments extracted from the sonar range data describing obstacles near the robot. The mobile robot then moves parallel to the straight line sonar segments, in close proximity to the obstacles, continually applying sonar barrier test. The sonar barrier test exploits the physical constraints of sonar data, and eliminates noisy data. This test determines whether or not a sonar line segment is a true obstacle edge or a false reflection. Low resolution sonar sensors can be used with the method described. The performance of the algorithm is demonstrated using a Denning Corp. Mobile Robot, equipped with a ring of Polaroid Corp. Ultrasonic Rangefinders.     

Robust DOA estimation and target docking for mobile robots

Direction of arrival (DOA) guided automated target acquisition and docking system is proposed for mobile robots employing the dual-directional antenna system. The dual-directional antenna estimates the DOA of the signal of interest using the ratio of the signal strengths between two adjacent antennas. In practice, DOA estimation poses a significant technical challenge, since the RF signal is easily distorted by the environmental conditions. Therefore, the robot often loses its way in an electromagnetically disturbed environment. To cope with this problem, a robust DOA estimation algorithm is developed based on Kalman filtering. This algorithm allows the robot to reduce the potential error in the estimated DOA, and adjust the robot’s heading to the target transponder without needing to know the positions of current and previous measurements in a global coordinate system. The simulation and experiment results clearly demonstrate that the mobile robot equipped with the developed system is able to dock to a target transponder in an indoor environment partially occupied by obstacles.     

模糊控制在移动机器人路径规划中的应用

针对移动机器人最优路径规划问题,设计了一种模糊智能控制方法。利用超声波传感器对机器人周围环境进行探测,得到关于障碍物和目标的信息。通过设计模糊控制器,把得到的障碍与目标位置信息模糊化,建立模糊规则并解模糊最终使机器人可以很好地避障,并且解决了模糊算法存在的死锁问题,从而实现了移动机器人的路径规划。仿真实验结果表明了模糊算法优于人工势场法,具有有效性和可行性。     

测距传感器数据在线自适应加权融合

针对移动机器人的测距系统,采用了红外线传感器与超声波传感器共同测距,避免了因使用单个传感器进行多次测量而降低系统的实时性和产生信号串扰问题;应用自适应加权数据融合估计算法对实时测量数据进行在线融合估计,只对当前采样时刻的测量数据进行自适应加权融合,而各传感器的加权因子则通过传感器的测量数据进行方差在线学习估计以自适应方式进行调整,使融合结果的均方误差始终最小,实现两种传感器在功能上的互补;实验结果表明,该方法提高了整体测距精度,得到了被测距离更加准确的估计.     

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.     

基于AdaBoost算法的智能轮椅避障研究

随着计算机图像处理能力和技术的发展,视觉传感器在移动机器人导航和障碍物识别中的应用越来越受到重视.将AdaBoost算法用于智能轮椅的障碍物识别,在Visual C++6.0平台下,用AdaBoost算法训练得到用于障碍物检测的强分类器,然后利用该分类器进行检测出目标障碍物,并用模糊神经网络的方法对轮椅的声纳信息,视觉...     

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。     

栅格法视觉传感集成及机器人实时避障

研究移动机器人在室内环境下集成双目视觉和激光测距仪信息进行障碍物实时检测。由双目视觉系统检测环境获取视差信息,通过直接对视差信息进行地平面拟合的方法快速检测障碍物;拟合过程中采用了随机采样一致性估计算法去除干扰点的影响,提高了障碍物检测的鲁棒性。用栅格地图表示基于机器人坐标系的地平面障碍物信息并对栅格信息进行提取,最后把双目视觉与激光测距得到的栅格信息进行集成。实验表明,通过传感信息集成,移动机器人既得到了充分的障碍物信息,又保证了检测的实时性、准确性。     

基于主动式全景视觉的移动机器人障碍物检测

针对现有的移动机器人视觉系统计算资源消耗大、实时性能欠佳、检测范围受限等问题,提出一种基于主动式全景视觉传感器(AODVS)的移动机器人障碍物检测方法。首先,将单视点的全方位视觉传感器(ODVS)和由配置在1个平面上的4个红色线激光组合而成的面激光发生器进行集成,通过主动全景视觉对移动机器人周边障碍物进行检测;其次,移动机器人中的全景智能感知模块根据面激光发生器投射到周边障碍物上的激光信息,通过视觉处理方法解析出移动机器人周边障碍物的距离和方位等信息;最后,基于上述信息采用一种全方位避障策略,实现移动机器人的快速避障。实验结果表明,基于AODVS的障碍物检测方法能在实现快速高效避障的同时,降低对移动机器人的计算资源的要求。     

Concurrent localization of multiple robots

A concurrent localization method for multiple robots using ultrasonic beacons is proposed. This method provides a high-accuracy solution using only low-price sensors. To measure the distance of a mobile robot from a beacon at a known position, the mobile robot alerts one beacon to send out an ultrasonic signal to measure the traveling time from the beacon to the mobile robot. When multiple robots requiring localization are moving in the same block, it is necessary to have a schedule to choose the measuring sequence in order to overcome constant ultrasonic signal interference among robots. However, the increased time delay needed to estimate the positions of multiple robots degrades the localization accuracy. To solve this problem, we propose an efficient localization algorithm for multiple robots, where the robots are in groups of one master robot and several slave robots. In this method, when a master robot calls a beacon, all the group robots simultaneously receive an identical ultrasonic signal to estimate their positions. The effectiveness of the proposed algorithm has been verified through experiments.     

Corridor navigation and wall-following stable control for sonar-based mobile robots

In this paper, a mobile robot control law for corridor navigation and wall-following, based on sonar and odometric sensorial information is proposed. The control law allows for stable navigation avoiding actuator saturation. The posture information of the robot travelling through the corridor is estimated by using odometric and sonar sensing. The control system is theoretically proved to be asymptotically stable. Obstacle avoidance capability is added to the control system as a perturbation signal. A state variables estimation structure is proposed that fuses the sonar and odometric information. Experimental results are presented to show the performance of the proposed control system.     

一种新颖的基于二分法和模糊控制的移动机器人避障系统

针对超声波传感器波束角窄导致移动机器人存在避障盲区的现状,研究了一种新颖的超声波避障系统。该系统采用六个超声波传感器构成特别设计的超声波阵列,实现无盲区检测中大型移动机器人前方及左右两侧障碍物的位置,充分保障运行安全性;同时在避障算法上,采用二分法和模糊控制相结合的控制算法,简化了模糊控制规则使系统具有很好的智能性和实时性,实现了移动机器人选择最佳避障路径并对新增的动态障碍物进行避障。将此避障控制系统应用于移动机器人上,实验结果表明：在未知环境下,实现对移动机器人周边的无盲区检测,并且能够实时根据周围障碍物的动态情况选择最佳避障路径,避免了其它避障控制算法中易出现的误避障和二次避障的情况。     

The localization of a mobile robot using a pseudolite ultrasonic system and a dead reckoning integrated system

In this paper, an integration system is proposed to improve the positioning performance of a mobile robot by fusing a Pseudolite Ultrasonic System (PUS), an absolute position measurement system using direct ultrasonic waves, with a Dead Reckoning (DR) odometer. As an integration algorithm of the absolute position measurement system and DR, two methods are proposed. In the loosely coupled method, the PUS and the DR calculate the position independently and a Kalman filter estimates the position using position information from the PUS and the DR. In the tightly coupled method, the PUS provides the distance between the ultrasonic transmitters and receivers without calculating the position directly and the DR provides the translational and rotational displacement of the mobile robot. The Kalman filter then estimates the position using information from the PUS and the DR. In addition, to improve the positioning performance in case the line-of-sight (LOS) between the ultrasonic transmitter and receiver is blocked due to obstacles, a positioning failure detection algorithm and reckoning methods are proposed. The positioning performances of the proposed PUS/DR integrated systems and the validity of the positioning failure detection algorithm are verified and evaluated by experiments.     

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。     

未知环境中移动机器人实时导航与避障的分层模糊控制

为了解决单模糊控制器的“规则库爆炸”问题,设计了一种分层的模糊控制器,用于指导移动机器人通过未知环境到达指定的目标点．控制器根据8个超声传感器的信息和目标相对于机器人的方位确定机器人的运动．首先,每个超声传感器的信息被输入到危险度模糊控制器（DFC）中,产生关于周围环境中障碍物危险度的模糊向量．这些模糊向量经过融合与归一化处理后分别输入到上层的速度模糊控制器（VFC）和角速度模糊控制器（RFC）的推理机中．VFC根据目标的距离和障碍物的危险度控制机器人的前进速度．RFC根据目标的方向和障碍物的危险度控制机器人的转向,并采用最大隶属度法的反模糊化策略解决“对称不确定”问题．仿真与实验结果证明了所设计的模糊控制器简单而有效．     

基于模糊神经网络的移动机器人沿墙导航控制

移动机器人沿墙导航控制包含了追踪和避障两种情况,是移动机器人研究中的常见问题。它是指机器人在一定方向上沿墙运动,或者更一般意义上的沿着物体轮廓运动,并与墙保持一定距离。移动机器人利用声纳采集机器人与墙体的距离和角度信息,通过模糊神经网络将输入数据进行融合,从而判断移动机器人的位姿信息,输出左右轮速度控制其动作。实验证明此方法可以有效地保证移动机器人在安全距离内沿墙体运动。对比采用模糊神经网络前后的实验,采用后的移动机器人沿墙导航控制轨迹优于采用前,均方误差大大减小。