超声波测距系统在移动机器人中的设计与应用

给出了基于单片机控制的超声波测距系统的软件和硬件设计。采用增加超声波测距电路的通道数的方法,提高了移动机器人在避障与导航方面的精度与可靠性。给出了12路超声波测距系统在移动机器人中的实际应用。     

并行超声波测距系统在移动机器人上的应用

超声波传感器被广泛应用于机器人避障系统中.基于超声波的测距原理,设计了一种并行测距系统.介绍了系统的硬件组成和软件实现方法.针对多路传感器并行测距容易产生干扰的现象,分析了干扰产生的原因,提出一种有效的解决办法.将系统用于移动机器人避障实验,给出了系统测量范围的实验标定结果.     

基于Linux的多路超声波测距系统设计

根据超声波测距的工作原理,设计基于ARM9与嵌入式Linux的多路超声波测距系统的硬件电路及软件工作流程.用Linux设备驱动的思想编写超声波设备驱动,并用于移动机器人的避障导航系统.     

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.     

嵌入式Linux下多路超声波测距系统设计

本文描述了机器人避障过程中超声波测距的工作原理和设计方法。介绍了多路超声波测距系统的硬件设计及软件工作流程。分析了嵌入式Linux系统下超声波测距模块驱动程序的结构,并结合S3C44B0X uClinux系统的轮式移动机器人平台,实现了机器人在未知环境下的导航避障过程。     

基于ARM的移动机器人测距传感器设计

为实现移动机器人安全避障,设计了一种基于S3C44BOX微控制器的8路超声波测距系统.具体介绍了该超声波测距传感器的软、硬件设计及工作原理.系统利用ARM外围丰富的I/O接口和内部定时器精确控制选通开关芯片CD4066的使能端,以消除多路超声波传感器之间回波干扰和直接串扰的问题,并采用最小二乘法对测量数据进行了分析和修正.实验研究结果表明,该系统具有良好的测距性能.     

基于P87C591的CAN总线智能超声波测距系统

在移动机器人的安全避障技术中,为了准确定位障碍物,往往采用多路超声波传感器测距系统,势必造成系统硬件复杂,软件编写困难.为节省系统资源,介绍了一种以飞利浦P87C591作为超声波传感器控制核心及CAN总线控制器的CAN总线智能超声波测距系统,详细介绍了其硬件电路构成、工作原理及软件设计思想.通过该设计的简单应用,大大节省了系统硬件资源,简化了软件编写复杂度,且该系统硬件结构简单,有较强的实用性.     

移动机器人的超声波测距传感器设计

针对移动机器人研究了一种基于单片机AT89C51作为控制系统的超声波测距传感器,实现其硬件电路设计和软件算法的开发,分析了其系统组成、实验结果及误差。该系统以空气中超声波的传播速度为确定条件,利用反射超声波测量待测距离。结果表明,该设计提高了移动机器人的障碍回避能力及导航方面的精度和可靠性。     

移动机器人多传感器信息融合测距系统设计

介绍了用于移动机器人运动导航的多传感器测距系统的硬件组成和数据融合方法.测距系统由超声波测距模块、红外测距模块和数据融合模块组成,超声波测距模块和红外测距模块分别检测机器人周围环境中障碍物的距离,数据融合模块则通过SMBus总线收集两个模块采集到的距离数据,并采用自适应加权数据融合算法对这些数据进行处理,以获得可信的距离信息.实验表明,所设计的测距系统可靠性高、配置灵活,能够满足机器人自主导航中的避障要求.     

基于弯曲振动超声换能器的远距离测量

未知环境下的实时障碍探测是陆地自主车(也称室外移动机器人)智能导航的关键技术.超声波传感器与其它环境探测传感器相比,具有价格低廉、便于安装使用且不受光线影响的优点,在国内外移动机器人测距系统中得到了广泛应用.但由于作用距离较短的原因,超声波传感器一般用于机器人低速运行在静态环境中的短距离测量,无法作为高速行驶陆地自主车的前视传感器来使用.针对这一情况,研制了一种高功率低频率的远距离超声传感器,其波束角为9,最大作用距离可超过35m,能满足高速行驶的陆地自主车进行远距离测距的要求.     

基于S3C44B0X的移动机器人的应用研究

本文以S3C44B0X为控制中心,设计了移动机器人的硬件平台,并给出了软件设计策略,实现室内未知环境的移动机器人的导航,采用多传感器感知外界环境的障碍信息,结合光电编码器和电子罗盘获取机器人当前的位置,运用嵌入式实时多任务操作系统μC/OS-II设计机器人多任务运行策略。实验表明,该移动机器人能够实现避障功能并能达到目标点。     

A faster path planner using accelerated particle swarm optimization

The idea of placing small mobile robots to move around in a large building to detect potential intruders has been around for some time. However, there are still two major hurdles to overcome: to locate itself in the environment and to make a decision on how to move around safely and effectively at a reasonable computation cost. This paper describes a mathematical model for developing a scheme for an autonomous low cost mobile robot system using visual simultaneous localization and mapping and accelerated particle swarm intelligent path planner. The results indicated that this system could provide a solution for the problem of indoor mobile robot navigation. Advances in computer technology make this technique a cost effective solution for a future home service robot.     

Automatic motion detection for surveillance

Automatic motion detection features are able to enhance surveillance efficiency and quality. The aim of this research is to recognize and detect motion automatically around a robot's environment in order to equip a mobile robot for a surveillance task. The required information is based on the input obtained from a charge coupled device (CCD) camera mounted on the mobile robot. As the first step toward achieving the goal, it is necessary to have a stationary mobile robot and moving objects. Experiments in a different environment, such as different movements, size of moving objects, and lighting conditions, have also been conducted. The “adjacent pixels comparison” is the proposed method to detect motion in this experiment. The results have verified that the motion detection experiments operate as expected. This work was presented in part at the 11^th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

群体自主微小型移动机器人的合作

单个微小型机器人由于自身能力的限制，因此必须多个机器人联合起来才可以完 成指定的任务，所以机器人之间的协作在微操作领域就显得尤其重要。该文利用增强式的 学 习方法，使得微小型机器人具有一定的学习能力，增强了对不确定环境的适应性，并采 用了 一种基于行为的群体自主式微小移动机器人的协作结构，用于机器人的故障排除，仿 真结果 说明了这种体系结构的有效性。     

Simulation systems for the design of robot mechanisms

Building environmental models by a vision-guided mobile robot is a key problem in robotics. This paper presents a new strategy of the vision-guided mobile robot for building models of an unknown environment by panoramic sensing. The mobile robot perceives with two types of panoramic sensing: one is for acquiring omnidirectional visual information at an observation point to find the outline structure of the local environment and the other is for acquiring visual information along a route to build local environmental models. Before exploring the environment, the robot looks around and finds the outline structure of the local environment as a reference frame for acquiring the local models. Then the robot builds the local models while moving along the directions of the outline structure (the outline structure is represented by a simple convex polygon, each side of which has a direction). We have implemented the above-mentioned robot behaviors into a mobile robot which has multiple vision agents. The multiple vision agents can simultaneously execute different vision tasks needed for panoramic sensing.     

基于EKF的全景视觉机器人SLAM算法

研究全景视觉机器人同时定位和地图创建（SLAM）问题。针对普通视觉视野狭窄, 对路标的连续跟踪和定位能力差的问题, 提出了一种基于改进的扩展卡尔曼滤波（EKF）算法的全景视觉机器人SLAM方法, 用全景视觉得到机器人周围的环境信息, 然后从这些信息中提取出环境特征, 定位出路标位置, 进而通过EKF算法同步更新机器人位姿和地图库。仿真实验和实体机器人实验结果验证了该算法的准确性和有效性, 且全景视觉比普通视觉定位精度更高。     

Discovering and restoring changes in object positions using an autonomous robot with laser rangefinders

This paper describes an object rearrangement system for an autonomous mobile robot. The objective of the robot is to autonomously explore and learn about an environment, to detect changes in the environment on a later visit after object disturbances and finally, to move objects back to their original positions. In the implementation, it is assumed that the robot does not have any prior knowledge of the environment and the positions of the objects. The system exploits Simultaneous Localisation and Mapping (SLAM) and autonomous exploration techniques to achieve the task. These techniques allow the robot to perform localisation and mapping which is required to perform the object rearrangement task autonomously. The system includes an arrangement change detector, object tracking and map update that work with a Polar Scan Match (PSM) Extended Kalman Filter (EKF) SLAM system. In addition, a path planning technique for dragging and pushing an object is also presented in this paper. Experimental results of the integrated approach are shown to demonstrate that the proposed approach provides real-time autonomous object rearrangements by a mobile robot in an initially unknown real environment. Experiments also show the limits of the system by investigating failure modes.     

Rapid, safe, and incremental learning of navigation strategies

In this paper we propose a reinforcement connectionist learning architecture that allows an autonomous robot to acquire efficient navigation strategies in a few trials. Besides rapid learning, the architecture has three further appealing features. First, the robot improves its performance incrementally as it interacts with an initially unknown environment, and it ends up learning to avoid collisions even in those situations in which its sensors cannot detect the obstacles. This is a definite advantage over nonlearning reactive robots. Second, since it learns from basic reflexes, the robot is operational from the very beginning and the learning process is safe. Third, the robot exhibits high tolerance to noisy sensory data and good generalization abilities. All these features make this learning robot's architecture very well suited to real-world applications. We report experimental results obtained with a real mobile robot in an indoor environment that demonstrate the appropriateness of our approach to real autonomous robot control.     

A Novel Approach for Mobile Robot Navigation with Dynamic Obstacles Avoidance

This paper proposes a new approach for trajectory optimization of a mobile robot in a general dynamic environment. The new method combines the static and dynamic modes of trajectory planning to provide an algorithm that gives fast and optimal solutions for static environments, and generates a new path when an unexpected situation occurs. The particularity of the method is in the representation of the static environment in a judicious way facilitating the path planning and reducing the processing time. Moreover, when an unexpected obstacle blocks the robot trajectory, the method uses the robot sensors to detect the obstacle, finds a best way to circumvent it and then resumes its path toward the desired destination. Experimental results showed the effectiveness of the proposed approach.     

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。