基于AT89C52单片机多超声信号融合系统设计

本文设计并实现了一种多超声信号融合处理系统,主要用于移动机器人超声测距导航。系统针对超声回波信号的特点,使用AT89C52单片机对来自多个超声波传感器的微弱回波信号进行数字处理,并通过RS-232串行口与主机实现通讯。利用该系统,可以完成超声波信号的精确提取及多传感器信息的融合处理,为移动机器人在未知环境下的快速导航奠定了基础。     

Ultrasonic navigation for a wheeled nonholonomic vehicle

In this paper, we demonstrate a reliable and robust system for localization of mobile robots in indoors environments which are relatively consistent to a priori known maps. Through the use of an Extended Kalman Filter combining dead-reckoning, ultrasonic, and infrared sensor data, estimation of the position and orientation of the robot is achieved. Based on a thresholding approach, unexpected obstacles can be detected and their motion predicted. Experimental results from implementation on our mobile robot, Nomad-200, are also presented.     

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。     

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.     

基于虚拟现实技术的移动机器人视觉伺服控制系统设计

为了有效确保移动机器人视觉伺服控制效果,提高移动机器人视觉伺服控制精度,设计了基于虚拟现实技术的移动机器人视觉伺服控制系统。通过三维视觉传感器和立体显示器等虚拟环境的I/O设备、位姿传感器、视觉图像处理器以及伺服控制器元件,完成系统硬件设计。从运动学和动力学两个方面,搭建移动机器人数学模型,利用标定的视觉相机,生成移动机器人实时视觉图像,通过图像滤波、畸变校正等步骤,完成图像的预处理。利用视觉图像,构建移动机器人虚拟移动环境。在虚拟现实技术下,通过目标定位、路线生成、碰撞检测、路线调整等步骤,规划移动机器人行动路线,通过控制量的计算,实现视觉伺服控制功能。系统测试结果表明,所设计控制系统的位置控制误差较小,姿态角和移动速度控制误差仅为0.05°和0.12m/s,移动机器人碰撞次数较少,具有较好的移动机器人视觉伺服控制效果,能够有效提高移动机器人视觉伺服控制精度。     

一种移动机器人超声波导航系统

本文设计了一种用于移动机器人的超声波导航系统，介绍了系统的工作原理及结构， 并进行了样车实验．实际的导航实验表明：系统可用于室内结构环境中的移动机器人导航．     

基于主动视觉和超声的机器人目标跟踪系统

运动目标跟踪技术是未知环境下移动机器人研究领域的一个重要研究方向。该文提出了一种基于主动视觉和超声信息的移动机器人运动目标跟踪设计方法,利用一台SONY EV-D31彩色摄像机、自主研制的摄像机控制模块、图像采集与处理单元等构建了主动视觉系统。移动机器人采用了基于行为的分布式控制体系结构,利用主动视觉锁定运动目标,通过超声系统感知外部环境信息,能在未知的、动态的、非结构化复杂环境中可靠地跟踪运动目标。实验表明机器人具有较高的鲁棒性,运动目标跟踪系统运行可靠。     

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.     

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     

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     

基于单片机的移动机器人路径规划模糊控制器设计

提出了一种基于模糊控制的移动机器人路径控制的设计思路,并给出了以ATmage128单片机为核心的控制系统硬件设计方案。系统预先设定目标,移动机器人根据超声波传感器感知周围环境,采用模糊控制进行路径规划,从而进行机器人自主移动到达目标。论文给出了模糊控制隶属度函数,介绍了模糊控制规则。同时介绍了显示电路、超声波测距电路、电机驱动电路、无线通信电路等硬件模块接口电路。     

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.     

ATmega16与PC机的移动机器人定位系统研究

为提高移动机器人定位系统的可靠性,设计了组合使用光纤陀螺仪、光电码盘和超声波传感器的定位系统,系统采用CAN总线的数据传输方式。ATmega16采集各传感器数据,再以CAN总线方式传输给PC机;PC机平台综合处理光纤陀螺、光电码盘与超声波返回的数据,实现移动机器人定位。定位算法以航迹推算为主,超声波传感器起辅助定位作用。实验表明定位系统可靠有效。     

移动机器人超声波避碰传感器系统设计

为了避免移动机器人在行进过程中与障碍物发生碰撞，设计了一种基于LPC2119微控制器的超声波避碰传感器系统。文章首先介绍了超声波发射电路、回波电压放大电路和检测电路的设计方法；巧妙地应用74HC154的简单电路实现了多路超声波信号的循环发射；利用LM567实现了锁相环回波检测电路，针对超声波传感器之间的回波干扰，提出了一种有效的解决方案；最后根据实验测量结果，应用最小二乘法得到了距离补偿公式，在移动机器人运行的过程中，该系统实时地循环检测机器人周围的障碍物信息，有效地避免了碰撞现象的发生。     

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。     

Mobile robot navigation in a partially structured static environment, using neural predictive control

This paper presents a way of implementing a model-based predictive controller (MBPC) for mobile robot navigation when unexpected static obstacles are present in the robot environment. The method uses a nonlinear model of mobile robot dynamics, and thus allows an accurate prediction of the future trajectories. An ultrasonic ranging system has been used for obstacle detection. A multilayer perceptron is used to implement the MBPC, allowing real-time implementation and also eliminating the need for high-level data sensor processing. The perceptron has been trained in a supervised manner to reproduce the MBPC behaviour. Experimental results obtained when applying the neural-network controller to a TRC Labmate mobile robot are given in the paper.     

Feature extraction method for a robot map using neural networks

Many map-building algorithms using ultrasonic sensors have been developed for mobile robot applications. In indoor environments, the ultrasonic sensor system gives some uncertain data. To compensate for this effect, a new feature extraction method using neural networks is proposed. A new, effective representation of the target is defined, and the reflection wave data patterns are learnt using neural networks. As a consequence, the targets are classified as planes, corners, or edges, which all frequently occur in indoor environments. We constructed our own robot system for the experiments which were carried out to show the performance. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

基于BP神经网络的移动机器人避障设计及仿真

为了解决移动机器人在复杂环境中如何高效精确地躲避障碍物的问题,提出了一种基于BP神经网络的避障方法。建立了机器人的避障运动模型并设计了神经网络避障控制系统;分析了机器人在运动过程中与障碍物的位置关系,使用超声波传感器采集距离信息,进行BP神经网络输入、输出训练并采用Matlab工具进行仿真试验。结果表明,该方法可以高效精确地实现移动机器人的自主避障,运行相对稳定、轨迹连续平滑,达到了较为理想的避障效果。验证了方法的可行性和有效性,为移动机器人自主避障提供了一种新的控制方法。     

Tethered mobile robot for automating highway maintenance operations

The use of a self-propelled mobile robot working in close proximity to a support vehicle for purposes of power, materials, etc. would be extremely valuable for highway maintenance and construction operations. This paper discusses the development of such a uniqe mobile robot system and emphasizes the robot's control system. The wheeled mobile robot is differentially steered and tethered to a support vehicle via a mechanical linkage which has position tracking capability. This mobile robot system has been termed the tethered mobile robot (TMR), and such an approach has the potential for use in a wide variety of highway maintenance operations. An optimal control system applicable to many highway maintenance operations is designed and implemented through the utilization of recently developed servo control technology. Experimental results presented show significant promise for the TMR system.