一种动态环境下的移动机器人避障策略

以激光雷达作为主要的环境感知传感器,针对动态环境设计了一种移动机器人避障策略.根据激光雷达采集的实时环境信息,检测动静态障碍物并建立障碍物链表.采用底层反应式避障和高层动态避障相结合的策略,实现了动态环境下的避障.使用分布式的软件设计方法,提高了系统的实时性.通过自行研制的移动机器人MORCS-1进行了实验,实验结果验证了该策略的有效性.     

基于环形红外阵列的移动机器人自动跟随系统

为了实现在室内空旷环境中跟随机器人对移动目标物的定位和动态跟踪,设计了一种基于环形红外阵列的移动机器人自动跟随系统.利用具有主动式环形大视场的测距罗盘作为环境感知传感器.测距罗盘由数个红外测距传感器组成周向阵列,实现对360°环向10～80 cm范围内移动目标物的二维定位,使跟随机器人可以快速确定其与移动目标物之间的距...     

可编程动态特性补偿滤波器设计

以常用的典型传感器为应用对象,设计了一种可编程配置的动态特性补偿模拟滤波器,通过单片机控制改变模拟滤波器电路的结构与参数以实现对不同传感器动态特性的补偿。通过对不同方案的分析和比较,选择了合适的电路结构以及元件参数,实现了针对常见工作频带在20 k Hz以下的机电系统3~6阶可编程传感器动态特性补偿滤波器。通过对由电路构成的一阶系统和悬臂梁系统的补偿,验证了该动态特性补偿滤波器的可用性。     

2.5D激光传感器设计及室内环境特征提取算法研究

在机器人同步定位与地图构建(Simultaneous Localization and Mapping,SLAM)问题中,机器人一般通过激光雷达或视觉传感器来感知环境。视觉传感器受环境光照影响很大,相比之下激光传感器具有测距精度高、性能稳定的优点。为改进普通2D激光雷达数据量小、在SLAM的数据匹配过程中鲁棒性不强的问题,本文设计了一种音圈电机驱动的2. 5D激光传感器。采用电机驱动2D激光雷达在竖直方向上往复运动,通过对2D激光数据和音圈电机编码器的同步实现了空间带状(2. 5D)区域的激光数据采集。当移动机器人工作在半结构化的室内环境中时,可以认为从2. 5D激光传感器获得的高度有限的带状点云具有与地面垂直的先验分布。本文对2. 5D激光传感器采集到的激光点云进行了特征的提取,提取了空间的平面和棱线特征以及竖直方向突变的特征。     

基于Kinect传感器的移动机器人环境检测及行为学习

研究了一种基于深度图像和强化学习算法的移动机器人导航行为学习方法。该方法利用机器人装配的Kinect传感器检测工作环境信息,然后对获取的深度图像数据和视频图像进行处理、融合和识别,并由此构建机器人任务学习的状态空间,最终利用强化学习方法实现移动机器人的导航任务的自学习。该方法的有效性通过实验得到验证。实验表明,该方法能够使机器入具有较强的环境感知能力,并能够通过自学习的方式掌握行为能力。     

基于加速度计和弯曲传感器的手指运动姿态监测（英文）

针对动态测量关节角度的需要,本文设计了两种手指运动姿态监测方法.第1种方法是通过基于MEMS技术的三轴加速度计ADXL330对近侧指间关节的运动姿态进行监测.ADXL330是通过每一敏感轴所感知的重力加速度分量来实现这一功能.为验证该方法的可行性,设计了步进电机控制的能在竖直平面内转动的装置.对比传感器的输出和电机转动的角度,结果表明:动态条件下,其绝对误差为1°~3°,相对误差为2%~6%.第2种方法是通过弯曲传感器在弯曲时输出电阻的变化来监测手指的运动姿态.实验结果表明:在一定的允许误差范围内,三轴加速度计ADXL330和弯曲传感器都能用于监测手指运动姿态.     

基于多传感器融合信息的移动机器人速度控制方法

为提高移动机器人在复杂环境下的速度控制精度和适应能力,提出了一种基于多传感器融合信息的移动机器人速度控制方法。首先,根据多传感器非线性优化融合理论,通过最小化运动观测残差的方法来构建移动机器人运动状态优化估计模型。然后,对利用单目相机、轮式里程计及惯性测量单元（inertial measurement unit,IMU）观测移动机器人运动的方法进行介绍,并计算了各传感器对移动机器人运动的观测残差及其雅可比矩阵。最后,结合移动机器人运动状态估计信息与增量式PID （proportion integration differentiation,比例积分微分）控制策略,设计了移动机器人速度控制系统,并通过多项试验验证了该控制系统的性能。试验结果表明,所提出的移动机器人速度控制方法有效减小了速度估计误差,较基于轮式里程计信息的速度控制方法在精度与稳健性方面有较大提升。研究结果对提升移动机器人在复杂环境下的工作性能有显著意义。     

动态补偿数字滤波器的设计及硬件实现方法

本文介绍了采用动态补偿技术减小动态测量误差的原理，研究了利用传感器或测试系统的动态设计相应的动态补偿数字滤波器的方法，并讨论了这种数字滤波器的硬件实现，介绍了利用可编程逻辑器件实现的动态补偿数字滤波器。     

基于有限状态机的足球机器人行为设计与综合

介绍了一个面向动态环境作业的全自主移动机器人系统。该机器人系统集成了包括视觉、激光雷达、声纳环、里程仪在内的多传感器系统,双轮独立驱动的移动小车以及高速无线局域网,并通过车载控制系统进行实时决策。控制系统采用递阶的混合式结构,在上层利用基于有限状态机的行为选择方法实现行为规划,在低层采用基于行为的控制技术。通过对指定任务的分解,该方法实现了动态环境下多行为的协调,使机器人具有较高的决策智能,同时又保证了快速的反应能力。动态环境下的实验和比赛结果证明了该方法的有效性。     

全地形移动机器人车身结构分析与优化

为了提高全地形移动机器人车身结构的力学性能,提出了一种结合多体动力学、有限元分析与正交试验的优化方法。针对全地形移动机器人的满载弯曲和扭转工况,利用多体动力学分析获得了该移动机器人车身在运动中所受的外部载荷,并结合有限元分析获得了车身结构的动态力学性能参数,以验证极限工况下车身结构的力学性能。基于四因素三水平正交试验,研究了底板厚度、悬架支座厚度、悬架支座加强筋长度和增高支柱加强筋厚度对全地形移动机器人车身最大应力、最大变形量和质量的影响。利用灰色关联分析法对正交试验结果进行分析。通过对比各因素的灰色关联度获得了全地形移动机器人车身结构的最佳优化方案：底板厚度为5 mm、悬架支座厚度为2 mm、悬架支座加强筋长度为65 mm和增高支柱增设2 mm厚加强筋。分析结果表明,相较于原始方案,优化后全地形移动机器人车身在质量减小6.93%的同时,最大应力减小了12.47%,最大变形量减小了41.69%,说明该优化方案可在提高车身结构性能的同时减小车身质量和降低整车功耗,验证了所提出的优化方法的有效性。研究结果可为机械结构的动态力学性能分析与优化提供参考。     

Three-dimensional reconstruction of fracture surfaces

An original method is presented to improve fracture surface characterization through an accurate three-dimensional (3-D) reconstruction. The method, based on digital image-processing techniques, was developed under the Khoros system. The reconstruction technique is based upon the stereoscopic principle to extract the surface local elevations from the stereo-pair images. The fractographs that compose the stereo pair are obtained by scanning electron microscopy from two points of view by tilting the object at two observation angles. The first step of image processing is the alignment of the two images. Next, an iterative processing based on the cross-correlation operation builds a very dependable high resolution elevation map of the fracture surface. Finally, the elevation map can be used to provide a 3-D perspective view of the surface by using various visualization tools. Also, profiles and horizontal sections are generated by sectioning the 3-D reconstructed surface.     

Localization and Geometric Reconstruction of Mobile Robots Using a Camera Ring

In this paper, a system capable of obtaining the 3-D pose of a mobile robot using a ring of calibrated cameras attached to the environment is proposed. The system robustly tracks point fiducials in the image plane of the set of cameras generated by the robot's rigid shape in motion. Each fiducial is identified with a point belonging to a sparse 3-D geometrical model of the robot's structure. Such a model allows direct pose estimation from image measurements, and it can easily be enriched at each iteration with new points as the robot motion evolves. The process is divided into an initialization step, where the structure of the robot is obtained, and an online step, which is solved using sequential Bayesian inference. The approach allows the proper modeling of uncertainty in measurements and estimations, and at the same time, it serves as a regularization step in pose estimation. The proposed system is verified using simulated and real data.      

Reconstructing gas distribution maps via an adaptive sparse regularization algorithm

In this paper, we present an algorithm to be used by an inspection robot to produce a gas distribution map and localize gas sources in a large complex environment. The robot, equipped with a remote gas sensor, measures the total absorption of a tuned laser beam and returns integral gas concentrations. A mathematical formulation of such measurement facility is a sequence of Radon transforms, which is a typical ill-posed problem. To tackle the ill-posedness, we develop a new regularization method based on the sparse representation property of gas sources and the adaptive finite-element method. In practice, only a discrete model can be applied, and the quality of the gas distribution map depends on a detailed 3-D world model that allows us to accurately localize the robot and estimate the paths of the laser beam. In this work, using the positivity of measurements and the process of concentration, we estimate the lower and upper bounds of measurements and the exact continuous model (mapping from gas distribution to measurements), and then create a more accurate discrete model of the continuous tomography problem. Based on adaptive sparse regularization, we introduce a new algorithm that gives us not only a solution map but also a mesh map. The solution map more accurately locates gas sources, and the mesh map provides the real gas distribution map. Moreover, the error estimation of the proposed model is discussed. Numerical tests for both the synthetic problem and practical problem are given to show the efficiency and feasibility of the proposed algorithm.     

曲面复合材料缝合机器人自动路径规划技术

为提高复合材料预制件缝合机器人的灵活性与加工柔性,针对缝合机器人自动路径规划问题,研究了基于三维光栅扫描技术的曲面复合材料缝合机器人视觉接缝提取技术.针对三维扫描摄像机视场固定的特点,对摄像机进行标定实验,改进传统的手眼标定方法,提出一种用于求解手眼关系矩阵的四点标定新方法,建立了机器人坐标系与摄像机坐标系的位姿映射关系.经过手眼关系标定,三维光栅扫描仪采样得到的缝合预制件点云数据可转化为机器人坐标系下的点云数据坐标.进一步对采集的预制件点云数据进行接缝中心线提取,提出了一种缝合轨迹及姿态规划算法,采用三次多项式对接缝中心线进行空间曲线拟合,采用最小二乘法对缝合微切平面进行平面拟合,完成机器人操作空间前进矢量及接近矢量的计算.最后,将规划结果应用于缝合机器人的离线编程仿真与实验中.实验结果表明,该系统精度高,线迹成型良好,可以满足机器人缝合系统的要求.     

Three-Dimensional TOF Robot Vision System

Stereo vision (SV) is widely used and is well known for many 3-D image applications. In this paper, a 3-D time-of-flight system will be introduced, which is integrated into a robot vision system. The pros and cons of the system in comparison with SV are discussed, and the basic equations needed to acquire the range information are illustrated. Moreover, object segmentation algorithms working on the range data are presented. Experimental results show that the accuracy and repeatability of such a system is applicable for industrial applications.      

GPS/电子地图组合定位技术在侦察机器人系统中的应用

介绍了GPS／电子地图组合定位技术在侦察机器人系统中的应用。对GPS接收机进行二次开发，在WINDOWS环境下，基于VB6．0，利用通信控件捕获基准站和移动站的RTCM SC－104格式差分GPS电文。分析比较了几种GPS定位算法的优劣，根据侦察机器人系统的特点，选择逆向伪距差分算法对电文进行处理，并与电子地图相结合进行精确定位。对逆向伪距差分算法的实现做了详细介绍并对实验结果做了简要分析，在电子地图的管理上，提出了将监视区域二次划分的方法，利用BMP位图，对侦察机器人所在的方位进行可视化精确定位。     

Effective manipulation for a multi-DOF robot manipulator in laboratory environments

Robots are expected to enter human environments soon, and many challenging problems may then emerge when facing uncertain and varying environments. Among the challenges, one issue of great interest is how they can be effectively operated for task execution. If a robot is not to be controlled by detailed analysis and program coding, one appealing alternative is to provide a kind of manipulation system which enables the user to operate the robot naturally and efficiently. This idea has motivated us to develop an effective manipulation system for multi-degree of freedom (DOF) robot manipulators based on a 6-DOF haptic device. In this article, we focus on the laboratory environment, which is somewhat organized, but still demands sophisticated human manipulation. The proposed manipulation system, aimed for 3-D applications, provides two kinds of assistance. One is to enhance the linkage between the user and robot manipulator via the haptic clue. The other is to guide the movement of the user during task execution via a set of virtual tools, e.g., a ruler. We also propose a method for achieving smooth force rendering in manipulation, especially during the transition between two consecutive manipulations for guidance. For performance evaluation, this system is employed to conduct experiments in the chemical laboratory, which involve delicate and challenging maneuvers.     

Autonomous target tracking and following mobile robot

Tracking and following a moving target in real time is a very challenging task in autonomous mobile robot applications, due to the unstructured and unknown environment. In this article, a real time, autonomous, dynamic, whole-field target tracking system (360° coverage) is developed based on a pan/tilt/zoom CCD vision system. The vision system scans and locks the pose of the moving target and commands the tracking mobile robot to follow the target while avoiding obstacles. Bayes’ theorem-based foreground–background segmentation algorithm is applied for motion detection. The color-based particle filter algorithm is used to track the moving object. The system steers the tracking mobile robot toward the moving target based on the angular difference obtained from the tracking module. Fuzzy logic is applied and a sensor data fusion algorithm is developed to reduce the ultrasonic phantom effect of obstacle detection using eight ultrasonic sensors. Multiple behaviors are integrated through the proposed system. Experimental results show the proposed system can successfully track and follow a moving target and avoid obstacles in real time. The tracking accuracy is higher than 80%.     

点线特征结合的视觉SLAM闭环检测研究

在视觉SLAM系统中,闭环检测是非常重要的一个模块,它主要用于解决移动机器人在位置估计时随时间漂移的问题。移动机器人搭载相机在场景中运动,通过前端视觉里程计估计自身位姿和场景地图点坐标。视觉里程计历史时刻的估计误差会传递到下一时刻,导致一段时间后估计的结果出现累计误差,闭环检测可有效减少该累计误差。针对传统基于点特征的视觉SLAM闭环检测在点稀缺环境下精度和鲁棒性较差的问题,提出结合环境权重点线特征的闭环检测系统。该系统相比传统的基于点特征的闭环检测算法,系统鲁棒性更强,检测精确度更高。     

未知环境中基于视觉的增量式拓扑建模及导航

针对未知环境提出并实现了一个新的基于摄像机视觉的增量式拓扑建模及导航系统.该系统包括自然路标提取、建模与定位、地图及路标库管理、规划4个主要部分.采用局部显著图像区域构造自然路标,并利用隐马尔科夫模型(HMM)建模当前场景中所获得路标间的关系,构造拓扑顶点.为提高定位精度,设计了最大后验概率(MAP)的学习策略.设计了基于竞争学习的路标管理机制,采用简单的最短路径算法在拓扑图上进行路径规划.该系统支持在线增量式建模,利用局部图像特征及其关系表示环境,定位算法简洁有效,辅以路标管理使之能够适应大规模环境导航任务.实验结果表明该系统路标提取稳定、位置识别率高、定位精确,能够保证机器人在未知环境中的安全导航.