移动机器人导航中激光雷达测距性能研究

距离检测是移动机器人导航的一个关键问题.文章介绍了基于飞行时间法测距的二维激光雷达LMS291的工作原理,分析了影响LMS291测距性能的误差因素,通过统计分析对测距数据进行标定,研究了不同物体表面属性对测距精度的影响;通过混合像素测定方法确定LMS291的角度分辨率;通过对激光雷达测距性能的试验,建立测距误差模型.所得结论可以为复杂非结构化环境下移动机器人导航中障碍检测、环境建模及自定位提供支持.     

基于激光雷达的环境特征提取方法研究

环境特征提取是移动机器人导航中的研究难点,以2D激光雷达为环境感知传感器,针对室内环境中的应用提出一种环境特征提取方法;通过激光雷达的测距精度评估,基于近邻方法对环境中障碍进行空间分类特征提取;对该方法不足进行分析的基础之上,为了能够提取更为精确的特征信息,将SOM方法应用于激光雷达的环境特征提取中;结合装备2D激光雷达LMS291的自行研制的移动机器人采集的数据进行实验,实验结果验证了所提方法的有效性.     

基于DSP的多超声测距数据采集处理系统

介绍了自行设计的移动机器人CASIA-I中超声测距系统的软、硬件,以及超声测距数据与上位机通信的设计和实现过程。该系统以DSP-TMS320LF2407A作为核心处理器,以CAN总线为基础,实现了上述功能。经实验验证,测距范围为0.45m～3.5m,系统测距精度在0.7%以内,可以满足移动机器人室内导航的要求。     

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。     

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ROS的ICP-SLAM在嵌入式移动机器人上的实现与优化

基于激光雷达的ICP-SLAM在机器人操作系统中无法直接运行,且通过PC实现算法时,没有高效运载计算机资源,产生资源浪费,同时降低了移动机器人系统的灵活性。通过在嵌入式系统中搭建机器人操作系统,在系统中移植MRPT函数库中的ICP-SLAM算法,对算法进行优化,调整算法运行的CPU占用率,使用基于激光雷达的移动机器人进行建图。结果表明机器人操作系统生成的栅格地图可以满足机器人自主导航的需求,SLAM效果更为直观,系统更加灵活,成本和硬件配置要求大大降低。证明优化后的算法更贴近实际的使用需求。     

嵌入式Linux下超声波避障系统设计

介绍了新型超声波传感器的工作原理、测距系统的软硬件实现,及机器人平台整个软件工作流程.利用软件方法解决了S3C2410中不利于检测回波的问题,完成了基于ARM9与嵌入式Linux系统的移动机器人平台下未知环境的避障导航系统的设计.提出在Linux驱动程序模块中利用轮循方法实现多路超声波的测距,并结合开发的超声波驱动程序完成了机器人在未知环境下的导航避障功能,非结构环境下机器人平台运行效果良好.     

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.     

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。     

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。     

A New Mobile Robot Toolbox for Matlab

In this study, a wheeled mobile robot navigation toolbox for Matlab is presented. The toolbox includes algorithms for 3D map design, static and dynamic path planning, point stabilization, localization, gap detection and collision avoidance. One can use the toolbox as a test platform for developing custom mobile robot navigation algorithms. The toolbox allows users to insert/remove obstacles to/from the robot’s workspace, upload/save a customized map and configure simulation parameters such as robot size, virtual sensor position, Kalman filter parameters for localization, speed controller and collision avoidance settings. It is possible to simulate data from a virtual laser imaging detection and ranging (LIDAR) sensor providing a map of the mobile robot’s immediate surroundings. Differential drive forward kinematic equations and extended Kalman filter (EKF) based localization scheme is used to determine where the robot will be located at each simulation step. The LIDAR data and the navigation process are visualized on the developed virtual reality interface. During the navigation of the robot, gap detection, dynamic path planning, collision avoidance and point stabilization procedures are implemented. Simulation results prove the efficacy of the algorithms implemented in the toolbox.     

Set-point Control of Mobile Robot with Obstacle Detection and Avoidance Using Navigation Function - Experimental Verification

This paper presents the results of an experimental verification of mobile robot control algorithm including obstacle detection and avoidance. The controller is based on the navigation potential function that was proposed in work (Urakubo, Nonlinear Dyn. 81(3), 1475–1487 2015). Conducted experiments considered the task of reaching and stabilization of robot in point. The navigation potential agregates information of robot position and orientation but also the repelling potentials of obstacles. The obstacle detection is performed solely with the use of laser scanner. The experiments show that the method can easily handle environments with one or two obstacles even if they instantly hide or show-up due to the scanner range limits. The experiments also indicate that the utilized control method has a good potential for being used in parallel parking task.     

地图构建中动态障碍干扰滤除的一种方法

在移动机器人环境建图中,动态障碍物的存在直接影响传感器的读数,导致产生不一致的环境地图,因此,移动机器人构建地图必须滤除动态障碍物干扰。采用直线插补的方法在先前的局部图上搜寻机器人与目标点之间是否存在障碍物,若存在障碍,则可判定该障碍物已移走(即为动态障碍),应该予以滤除。实验结果证明,该方法能在建图过程中有效地滤除动态障碍,并能有效减少静态障碍物探测的误差累积,算法复杂度小。     

基于激光雷达的动态障碍物实时检测

动态障碍的存在直接影响到环境地图的构建精度,可靠实时地检测出动态障碍物是未知环境下移动机器人构建环境地图的根本前提。基于2D激光雷达传感器,提出了一种移动机器人在未知环境下实时检测动态障碍物的方法。将激光雷达的观测数据经过滤波映射到世界坐标系,构建相邻采样时刻的三幅栅格地图;判断相邻时刻三幅栅格地图上对应栅格的占用状态,确定环境中的静态障碍物,以静态障碍物为参考,根据当前的栅格地图可以检测出环境中的动态障碍物。基于激光雷达时空关联性分析,采用八邻域滚动窗口的方法处理不确定性因素。在实际移动机器人MORCS-1上进行的实验结果表明,该方法可使移动机器人准确有效地检测出未知环境中的动态障碍物,实时性好,可靠性高。     

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.     

环境特征提取在移动机器人导航中的应用

针对移动机器人在未知结构化环境中导航的需要,采用2D激光雷达作为主要传感器,对诸如墙壁、拐角、出口等这些典型的环境特征分别设计了一套有效的特征提取算法,并在该算法的基础上提出了基于特征点的移动机器人导航策略.该策略不需要里程计等其他一些内部传感器的信息,并且也不依赖具体的环境表述模型,从激光雷达扫描一次所得的数据中即可提取出环境特征,从而来指引机器人导航,实现起来快速可靠.应用到移动机器人MORCS-1上进行实验,取得了满意的结果,算法的实时性与鲁棒性得到了验证.     

Motion planning for mobile robots using a laser range finder

We have designed a mobile robot with a distribution structure for intelligent life space. The mobile robot was constructed using an aluminum frame. The mobile robot has the shape of a cylinder, and its diameter, height, and weight are 40 cm, 80 cm, and 40 kg, respectively. There are six systems in the mobile robot, including structure, an obstacle avoidance and driving system, a software development system, a detection module system, a remote supervision system, and others. In the obstacle avoidance and driving system, we use an NI motion control card to drive two DC servomotors in the mobile robot, and detect obstacles using a laser range finder and a laser positioning system. Finally, we control the mobile robot using an NI motion control card and a MAXON driver according to the programmed trajectory. The mobile robot can avoid obstacles using the laser range finder, and follow the programmed trajectory. We developed the user interface with four functions for the mobile robot. In the security system, we designed module-based security devices to detect dangerous events and transmit the detection results to the mobile robot using a wireless RF interface. The mobile robot can move to the event position using the laser positioning system.     

Navigation strategy of multiple mobile robot systems based on the null-space projection method

This paper deals with a navigation algorithm for swarm robot systems in which multiple mobile robots work together. The motion of each mobile robot is modeled in such a way to have more inputs than the number of outputs. The null-space projection method of this model is employed to resolve the motion of the swarm robot system while avoiding obstacles. The feasibility of the proposed navigation algorithm is verified through a simulation study using several swarm robot models.