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。     

Ultrasonic sensor data integration and its application to environment perception

To move in an unknown or uncertain environment, a mobile robot must collect information from various sensors and use it to construct a representation of the external world. Ultrasonic sensors can provide range data for this purpose in a simple and cost-effective way. However, most ultrasonic sensors are not sufficient for environment recognition because of their large beam opening angles. In this article the beam-opening-angle problem is solved by fusing data from multiple ultrasonic sensors. We propose two methods for sensor data fusion. One uses an artificial neural network (ANN), and the other is based on a mathematical model. Simulations and experiments show that the mathematical model is more accurate when there is no noise in the sensor readings, but the ANN method is better when the sensors are subject to much noise. To extract line segments from the ultrasonic image, we develop a line extractor that is more efficient than traditional line fitting methods in this application. Experimental results show that this method is effective for environment perception in a robotic system. © 1996 John Wiley & Sons, Inc.     

一种低功耗程控滤波器的设计

针对目前移动机器人同时定位与地图创建(SLAM)研究中多采用激光雷达或超声环作为测距传感器导致系统复杂、成本高的问题,提出了一种利用舵机带动单超声传感器扫描的低成本设计方案。在高斯超声模型基础上,利用贝叶斯公式对栅格地图进行概率更新,并结合Sobel边缘检测算法提取特征点,实现了由不确定的移动机器人坐标系向固定的以环境特征点为原点的全局环境坐标系的转换及全局定位,为在相同环境下通过重复实验进行多地图融合研究奠定了基础。该低成本移动机器人设计的有效性通过实验得以验证。     

Segment-Based Map Building Using Enhanced Adaptive Fuzzy Clustering Algorithm for Mobile Robot Applications

In this paper, we present a technique for on-line segment-based map building in an unknown indoor environment from sonar sensor observations. The world model is represented with two-dimensional line segments. The information obtained by the ultrasonic sensors is updated instantaneously while the mobile robot is moving through the workspace. An Enhanced Adaptive Fuzzy Clustering Algorithm (EAFC) along with Noise Clustering (NC) is proposed to extract and classify the line segments in order to construct a complete map for an unknown environment. Furthermore, to alleviate the problem of extensive computation associated with the process of map building, the workplace of the mobile robot is divided into square cells. A compatible line segment merging technique is then suggested to combine the similar segments after the extraction of the line segment by EAFC along with NC algorithm. The performance of the algorithm is demonstrated by experimental results on a Pioneer II mobile robot.     

Concurrent Map Building and Behavior Learning for Navigation in Unknown Environments

In this paper, we report a robust and low-cost navigation algorithm for an unknown environment based on integration of a grid-based map building algorithm with behavior learning. The study focuses on mobile robots that utilize ultrasonic sensors as their prime interface with the outside world. The proposed algorithm takes into account environmental information to augment the readings from the low angular accuracy sonar measurements for behavior learning. The environmental information is obtained by an online grid-based map learning design that is concurrently operating with the behavior learning algorithm. The proposed algorithm is implemented and tested on an in-house-built mobile robot, and its performance is verified through online navigation in an indoor environment.     

多传感器信息融合在移动机器人定位中的应用

机器人自定位是实现自主导航的关键问题之一。为了满足机器人在导航时精确定位的要求,提出一种基于多传感器信息融合的自定位算法。根据对机器人运动机构的分析和运动机构间的刚体约束,建立起机器人的运动学模型;由传感器的工作原理建立里程计和超声波传感器的观测模型;利用扩展卡尔曼滤波(EKF)算法将里程计和超声波传感器采集的数据进行融合;最后,由匹配的环境特征对机器人的位置进行修正,得到精确的位置估计。实验结果表明:该算法明显地消除了里程计的累计误差,有效地提高了定位精度。     

复合机构移动机器人红外和超声阵列信息处理方法

传感器是移动机器人认识和了解外部环境的重要途径。在导航过程中,移动机器人要对当前环境进行实时感知和快速理解,并加以识别从而准确避开障碍物。论文提出一种适用于复合机构移动机器人的红外阵列和超声阵列传感器信息采集和处理方法。结合笔者研制的“基于复合机构的非结构环境移动机器人”,给出了系统具体的软、硬件的设计和局部路径规划实现方法。实验验证了该方法的可靠性和有效性。     

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.     

Indoor Mobile Robot Local Path Planner with Trajectory Tracking

This paper deals with the design and implementation of an indoor mobile robot local path planner. This latter is based essentially on an ultrasonic perception system, where the covered region of sight is widened and the apparent distance between any two adjacent sensors can be adjusted. So, good resolution can be obtained and laterally positioned obstacles with respect to the robot line of sight are well identified. Furthermore, we propose a technique to improve the odometric method, to reduce the systematic errors and to detect floor irregularities. Following a predefined trajectory is provided by the control of the trailing wheel deviation angle.     

Correlation-Based Scan Matching Using Ultrasonic Sensors for EKF Localization

This paper presents a localization method for a mobile robot equipped with only low-cost ultrasonic sensors. Correlation-based Hough scan matching was used to obtain the robot’s pose without any predefined geometric features. A local grid map and a sound pressure model of ultrasonic sensors were used to acquire reliable scan results from uncertain and noisy ultrasonic sensor data. The robot’s pose was measured using correlation-based Hough scan matching, and the covariance was calculated. Localization was achieved by fusing the measurements from scan matching with the robot’s motion model through the extended Kalman filter. Experimental results verified the performance of the proposed localization method in a real home environment.     

Multi-object identification for mobile robot using ultrasonic sensors

This paper proposes an algorithm for indoor mobile robot to retrieve information of multiobjects, where not only the range and the bearing angle but also the shape (edge or plane) of object is discriminated by a single measurement of robot having single transmitter and multi-receiver of ultrasonic sensors. The feasibility of the proposed algorithm is simulated for the 3 different objects in the case of using 1 transmitter and 5 receivers.     

自主移动机器人室内定位方法研究综述

自主移动机器人的室内定位作为机器人研究领域中最基本的问题已被广泛研究。根据定位技术和传感器的不同,将室内定位方法分为航迹推算定位、地图匹配定位和基于信标定位三类。详细介绍了超声波网络定位系统和基于无线射频识别（RFID）的定位方法。对几种基于概率的定位算法做了分析和对比,并对自主移动机器人室内定位方法的研究方向做了展望。     

Automatic concrete tunnel inspection robot system

This paper describes the development of an automatic concrete tunnel inspection system by an autonomous mobile robot. This work was motivated by the accidents in recent years that were caused by falling parts of the inner wall of concrete tunnels. This brought about serious damage to national property and led to the great concern throughout the whole society. Here, we proposed a non-destructive automatic tunnel inspection method. In this method, we aim to inspect the tunnel automatically and completely at high speed by using non-destructive sensors. For the non-destructive sensors, we employ 24 ultrasonic range sensors and six video cameras. These sensors are mounted on the same plane in the shape of a semi-ring. This ultrasonic range sensor video camera semi-ring is called a URS/VC semi-ring. This URS/VC semi-ring is mounted on an autonomous mobile robot to inspect the concrete tunnel. Experiment results show that this system can detect deformed inner walls at divisions of 14 mm when the robot moves at 20 mm/s.     

基于ROS与Kinect的移动机器人同时定位与地图构建

同时定位与地图构建(SLAM)技术一直以来都是移动机器人实现自主导航和避障的核心问题,移动机器人需要借助传感器来探测周围的物体同时构建出相应区域的地图。由于传统的1D和2D传感器,如超声波传感器、声呐和激光测距仪等在建图过程中无法检测出Z轴(垂直方向)上的信息,易增加机器人发生碰撞的概率,同时影响建图结果的精确度。本文利用Kinect作为机器人SLAM的传感器,将其采集到的三维信息转化成二维的激光数据进行地图构建,同时借助机器人操作系统(robot operating system,ROS)进行仿真分析和实际测试。结果表明Kinect可以弥补1D和2D传感器采集信息的不足,同时能够较好的保持建图的完整性和可靠性,适用于室内的移动机器人SLAM实现。     

Self-localization and obstacle avoidance for a mobile robot

This article presents a fast self-localization method based on ZigBee wireless sensor network and laser sensor, an obstacle avoidance algorithm based on ultrasonic sensors for a mobile robot. The positioning system and positioning theory of ZigBee which can obtain a rough global localization of the mobile robot are introduced. To realize accurate local positioning, a laser sensor is used to extract the features from environment, then the environmental features and global reference map can be matched. From the matched environmental features, the position and orientation of the mobile robot can be obtained. To enable the mobile robot to avoid obstacle in real-time, a heuristic fuzzy neural network is developed by using heuristic fuzzy rules and the Kohonen clustering network. The experiment results show the effectiveness of the proposed method.     

Application of Heuristic Asymmetric Mapping for mobile robot navigation using ultrasonic sensors

In this paper, an experimental study of a navigation system that allows a mobile robot to travel in an environment about which it has no prior knowledge is described. Data from multiple ultrasonic range sensors are fused into a representation called Heuristic Asymmetric Mapping to deal with the problem of uncertainties in the raw sensory data caused mainly by the transducer's beam-opening angle and specular reflections. It features a fast data-refresh rate to handle a dynamic environment. Potential-field method is used for on-line path planning based on the constructed gridtype sonar map. The mobile robot can therefore learn to find a safe path according to its self-built sonar map. To solve the problem of local minima in conventional potential field method, a new type of potential function is formulated. This new method is simple and fast in execution using the concept from distance-transform path-finding algorithms. The developed navigation system has been tested on our experimental mobile robot to demonstrate its possible application in practical situations. Several interesting simulation and experimental results are presented.This work was supported partly by the National Science Council of Taiwan, ROC under the grant NSC-82-0422-E-009-321.     

Life-long optimization of the symbolic model of indoor environments for a mobile robot.

The use of a symbolic model of the spatial environment becomes crucial for a mobile robot that is intended to operate optimally and intelligently in indoor scenarios. Constructing such a model involves important problems that are not solved completely at present. One is called anchoring, which implies to maintain a correct dynamic correspondence between the real world and the symbols in the model. The other problem is adaptation: among the numerous possible models that could be constructed for representing a given environment, optimization involves the selection of one that improves as much as possible the operations of the robot. To cope with both problems, in this paper, we propose a framework that allows an indoor mobile robot to learn automatically a symbolic model of its environment and to optimize it over time with respect to changes in both the environment and the robot operational needs through an evolutionary algorithm. For coping efficiently with the large amounts of information that the real world provides, we use abstraction, which also helps in improving task planning. Our experiments demonstrate that the proposed framework is suitable for providing an indoor mobile robot with a good symbolic model and adaptation capabilities.     

基于光流传感器的移动机器人定位方法

详细介绍了光流传感器移动机器人定位系统的运动学原理,给出了理论模型推导;基于理论模型,搭建了基于光流传感器实验装置,将3只传感器置于移动机器人的固定位置,通过运动学几何关系,解算机器人的位姿;在室内进行了移动机器人的导航实验,推算了机器人的预测轨迹.实验证明了理论的可行性,可以作为短距离定位的一种导航方案.