机器人姿态精度的研究与测定方法

本文研究了机器人姿态精度的定义;使用了四点式半导体位置检测装置 PSD 和装有四个红外 LED 的空间测量坐标架等;提出了姿态测定方法,并测得了一些结果。是一种非接触式的,能够进一步提高机器人的姿态精度的较佳方法。     

一种新型机器人自标定装置及其算法

针对传统基于几何约束的机器人自标定装置仅能对局部工作空间内的机器人位型进行标定测量的问题,提出了一种由安装于机器人末端的球心位置测量装置和可移动球杆组成的新型便携式机器人自标定装置,通过利用球面约束和距离约束,可在较大工作空间内对机器人进行标定测量,从而提高标定结果的可靠性．根据可移动球杆的单、双球布置方式,分别建立了基于向量差和距离差的2种机器人自标定模型及其算法．通过采用局部指数积公式并引入位置伴随变换矩阵,简化了2种自标定模型,从而降低了对运动学方程线性化的计算量．最后,对一种6自由度串联机器人进行了仿真实验,实验结果表明2种自标定算法均能够快速收敛,验证了2种算法的有效性和鲁棒性．     

应用于机器人标定的主动式靶标装置设计与精度优化

激光跟踪仪是工业机器人标定技术中常用测量设备,但其测量范围受限于被动式靶标的激光光线接收角度,进而影响了机器人的标定精度。为解决该问题,设计了一种具有三自由度的主动式靶标装置,并提出了一种精度优化方法,有效地补偿因装置的装配而引入的测量误差。该方法利用圆点分析法初步辨识主动式靶标装置的DH参数,基于距离平方误差模型法对该装置进行DH参数的精辨识,并基于坐标系转换将主动式靶标装置的位置误差补偿到激光跟踪仪输出的位置向量,从而实现误差的补偿。通过实验验证了该主动式靶标装置能够有效地扩大工业机器人关节的被测范围。所提出的精度优化方法能够将该装置的测量误差降低9331%,实际定位精度为00507 mm,能够满足机器人标定的精度要求。     

机器人准确度的相机交会式测量系统

机器人的准确度是适应离线编程等应用要求而提出的.ISO 9283规定应对机器人进行准确度的检测;为了用误差补偿方法提高机器人的准确度,也必须测定机器人在其整个工作空间的绝对误差分布.但任何测试系统,如不以机器人的基座坐标定位,就无法实施准确度测量.本文首先提出了在相机交会式测量系统中,将测量坐标系与被测机器人机座坐标系精确对准的方法和装置,从而实现了 lSO 标准所要求的机器人绝对准确度的测量.     

自行走式地下掘进机器人姿态测量系统的设计

针对当前自行走式地下掘进机器人姿态测量方法只能应用于静态测量的不足,提出一种可实时动态测量机器人行进过程中姿态的新方法。该系统的核心是两个激光发射器和多个激光接收器。发射器发射三束带有一定特征信息的激光,包括一束选通光和两束扇形光。接收器接收到光束后,利用光束之间的几何关系、发射器的旋转速度和光束被接收的时间差来计算接收器相对于两发射器所在水平面的水平角和俯仰角,通过前方交会原理确定接受器即目标点空间坐标。根据系统的测量原理建立测量模型,进行误差分析,建立误差模型,并对其进行仿真。仿真结果表明该方法测量精度满足机器人位姿的测量要求。     

基于改进布谷鸟算法的机器人位置精度性能提升方法研究

主要解决工业机器人在高端制造领域精度性能不足的问题。首先阐述了工业机器人误差模型的构建方法，将机器人运动学参数、关节减速比参数、耦合比参数进行统一建模；其次，提出了一种改进的布谷鸟搜索算法(Cuckoo Search Algorithm, CSA)的优化多参数辨识方法，利用对数调整系数修正Levy搜索步长，提升CSA优化算法的收敛性和精确性。为了验证提出的误差模型和优化方法的有效性，构建了串联型工业机器人标定实验系统。通过在Staubli TX60机器人的运动空间内测量160个测量点，分别构成辨识点集和验证点集。实验结果表明，待标定机器人的平均综合位置误差降低了86.7%以上。说明提出的误差模型和优化方法能够较好地提升工业机器人的精度性能。     

矿用粉尘浓度传感器的设计与应用

针对现有粉尘测量方法存在无法实时反映现场空气中粉尘浓度的问题,设计了一种矿用粉尘浓度传感器,介绍了该传感器组成、工作原理及其在煤矿自动喷雾降尘装置中的应用。该传感器利用激光散射原理测量悬浮在空气中的粉尘颗粒浓度,同时采用根据粉尘沉降原理设计的精密机械结构和激光镜头刷镜机构,保证了测量的可靠性和精确性。     

提高大型激光加工机器人精度的方法

本文介绍了大范围、高精度5轴激光加工机器人系统的研究开发情况．在提高 其绝对精度的前提下，对大范围框架式机器人的结构、高精度机器人的误差补偿方法进行了 探讨．采用有限元分析的方法对机器人本体进行了优化设计，确保了高精度大型激光加工机 器人设计的正确性．基于测量数据，建立了机器人误差模型，对机器人系统误差进行了补偿 ，取得了较好的结果，保证机器人系统的激光加工精度．     

一种并联激光焊接机器人的机构设计与运动学分析

提出了一种3 分支5 自由度的并联激光焊接机器人,通过3 个分支共同作用,使整机具备了5 个自由 度的空间加工能力．针对激光焊接,通过分析该机器人的结构特性,建立了其正反解运动学模型,通过解析法求解 该模型并进行了计算仿真．最后,对机器人进行激光拼焊实验,仿真数据和实验结果表明,本文研究的并联机器人 机构适用于实际的高速、高精度激光焊接．     

视觉导引可移动机器人中的空间不确定性的表示和推理

本文通过对视觉导引可移动机器人建模、导航和规划中涉及的空间不确定性问题的研究, 提出了一套系统的空间不确定性的表示和推理方法.     

Optimal landmark selection for triangulation of robot position

A mobile robot can identify its own position relative to a global environment model by using triangulation based on three landmarks in the environment. It is shown that this procedure may be very sensitive to noise depending on spatial landmark configuration, and relative position between robot and landmarks. A general analysis is presented which permits prediction of the uncertainty in the triangulated position.

In addition an algorithm is presented for automatic selection of optimal landmarks. This algorithm enables a robot to continuously base its position computation on the set of available landmarks, which provides the least noise sensitive position estimate. It is demonstrated that using this algorithm can result in more than one order of magnitude reduction in uncertainty.     

基于粒子滤波的未知环境下机器人同时定位、地图构建与目标跟踪

为了解决机器人在未知环境下的目标跟踪问题,提出了一种基于粒子滤波的机器人同时定位、地图构建与目标跟踪方法．该方法采用Rao-Blackwellized粒子滤波器对机器人位姿状态、标志柱分布和目标位置同时进行估计．该方法中,粒子群的总体分布情况表征机器人位姿状态,而每个粒子均包含2类EKF滤波器,其中一类用来完成对标志柱分布的估计,另一类用来完成对目标状态的估计,粒子的权值则由粒子状态相对于标志柱和目标状态2类相似度共同产生．通过仿真和实体机器人实验验证了该方法的有效性．     

A new approach to solve SLAM challenges by relative map filter

In this paper we propose a new approach to solve some challenges in the simultaneous localization and mapping (SLAM) problem based on the relative map filter (RMF). This method assumes that the relative distances between the landmarks of relative map are estimated fully independently. This considerably reduces the computational complexity to average number of landmarks observed in each scan. To solve the ambiguity that may happen in finding the absolute locations of robot and landmarks, we have proposed two separate methods, the lowest position error (LPE) and minimum variance position estimator (MVPE). Another challenge in RMF is data association problem where we also propose an algorithm which works by using motion sensors without engaging in their cumulative error. To apply these methods, we switch successively between the absolute and relative positions of landmarks. Having a sufficient number of landmarks in the environment, our algorithm estimates the positions of robot and landmarks without using motion sensors and kinematics of robot. Motion sensors are only used for data association. The empirical studies on the proposed RMF-SLAM algorithm with the LPE or MVPE methods show a better accuracy in localization of robot and landmarks in comparison with the absolute map filter SLAM.     

机器人全向视觉定位系统的建模与应用

针对机器人对目标定位的需要,根据机器人结构及全向视觉系统的特点,研究并建立了包括双曲面镜的反射模型、成像模型在内的一套数学模型.确立了由先验信标位置求机器人位置,由像点和机器人位置求目标位置的数学过程.为目标定位提供了必要的数学模型和算法.定位时,为了减小视觉定位中单幅图像存在的不确定性,提出了基于图像序列及多传感器信息融合的目标定位方法.与传统方法相比,该方法抗外部和系统干扰的能力更强,且精度更高.实验结果证明了方法的有效性.     

融合语义激光与地标信息的SLAM技术研究

针对移动机器人在SLAM（即时定位与地图构建）过程中出现的定位失真问题,提出一种通过搭建地标数据库和位姿推导模型,修正机器人错误定位的方法。建图过程中,融合视觉信息与激光数据,得到语义激光,赋予地标语义标签并记录其在地图上的位置信息。导航过程中,当产生定位偏差时,结合多种位姿数据和相对位置关系,推算出机器人在地图上的实际位置,完成重定位。通过实验测试可知,该方法克服了现有机器人在实际室内动态环境下,单一地采用激光或视觉进行定位或重定位技术的缺点和不足,能有效解决“机器人位置漂移问题”。将机器人从当前位置劫持到另一位置,也能根据提出的算法迅速重定位,且定位精度高。     

Cleaning robot navigation using panoramic views and particle clouds as landmarks

The paper describes a visual method for the navigation of autonomous floor-cleaning robots. The method constructs a topological map with metrical information where place nodes are characterized by panoramic images and by particle clouds representing position estimates. Current image and position estimate of the robot are interrelated to landmark images and position estimates stored in the map nodes through a holistic visual homing method which provides bearing and orientation estimates. Based on these estimates, a position estimate of the robot is updated by a particle filter. The robot’s position estimates are used to guide the robot along parallel, meandering lanes and are also assigned to newly created map nodes which later serve as landmarks. Computer simulations and robot experiments confirm that the robot position estimate obtained by this method is sufficiently accurate to keep the robot on parallel lanes, even in the presence of large random and systematic odometry errors. This ensures an efficient cleaning behavior with almost complete coverage of a rectangular area and only small repeated coverage. Furthermore, the topological-metrical map can be used to completely cover rooms or apartments by multiple meander parts.     

基于全景视觉的机器人回航方法

提出一种使用全景视觉系统引导机器人回航的方法.利用全景视觉装置采集出发位置(Home位置)的全景图像,使用SURF(Speeded-Up Robust Feature)算法提取全景图像中的特征点作为自然路标点.机器人回航过程中,将当前位置获得的全景图像与Home位置的全景图像进行特征匹配,确定白然路标点之间的对应关系....     

Generalized measuring-worm algorithm: high-accuracy mapping and movement via cooperating swarm robots

Recently, many extensive studies have been conducted on robot control via self-positioning estimation techniques. In the simultaneous localization and mapping (SLAM) method, which is one approach to self-positioning estimation, robots generally use both autonomous position information from internal sensors and observed information on external landmarks. SLAM can yield higher accuracy positioning estimations depending on the number of landmarks; however, this technique involves a degree of uncertainty and has a high computational cost, because it utilizes image processing to detect and recognize landmarks. To overcome this problem, we propose a state-of-the-art method called a generalized measuring-worm (GMW) algorithm for map creation and position estimation, which uses multiple cooperating robots that serve as moving landmarks for each other. This approach allows problems of uncertainty and computational cost to be overcome, because a robot must find only a simple two-dimensional marker rather than feature-point landmarks. In the GMW method, the robots are given a two-dimensional marker of known shape and size and use a front-positioned camera to determine the marker distance and direction. The robots use this information to estimate each other’s positions and to calibrate their movement. To evaluate the proposed method experimentally, we fabricated two real robots and observed their behavior in an indoor environment. The experimental results revealed that the distance measurement and control error could be reduced to less than 3 %.     

Probabilistic relaxation method of matching visual landmarks for controlling the accurate positioning of mobile robots

The navigation problem of controlling the accurate positioning of a mobile robot (MR) with a computer vision system under conditions of uncertainty of information about its position is considered. Visual servo control is used, in which the error signal is calculated as the difference in the coordinates of natural visual landmarks detected by the computer vision system on the current and reference (received in a target position) images. To compute the error signal, a probabilistic relaxation method of correct matching of the landmarks extracted in the image is proposed. The efficiency of the proposed method has been confirmed by numerical experiments for processing real images.