一种用于机器人三维表面扫描系统的手眼标定算法

将线结构光测头安装在机器人末端,构成自由曲面非接触测量系统,利用齐次坐标变换理论建立测量系统的数学模型,针对该模型中的手眼标定问题,使用平面作为参考物,控制机器人以特定的位姿测量该平面,建立关于手眼关系的约束方程,并给出手眼参数的封闭解.通过对实际系统进行标定实验,验证了算法的有效性.     

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.     

非接触式机器人直线轨迹测量系统

视觉的、造价低廉实用的非接触式机器人直线轨迹测量系统．该测量系统由结构光视觉传感 器、测量轨道、主控计算机及相关软件组成．视觉传感器可固定于机器人末端．当机器人末 端带动视觉传感器沿测量轨道做直线运动时，通过传感器测量相对测量轨道的连续位姿关系 ，就可间接描述机器人末端的运动轨迹．当重复同一直线运动时，可检测出机器人的轨迹重 复性．本文综述了机器人直线轨迹测量设备的研究现状，介绍了轨迹检测系统的测量原 理，重点讨论了该测量系统的两个关键技术：空间特征点的图象提取技术和三维坐标计算方 法，并描述了该系统的结构、性能指标和测量试验结果．     

Graphical simulation for sensor based robot programming

The programming of robots is slowly evolving from traditional teach pendant methods to graphical Off-Line Programming (OLP) methods. Graphical simulation tools, such as OLP, are very useful for developing and testing robot programs before they are run on real industrial equipment. OLP systems are also used to develop task level programs. Traditional OLP systems, however, suffer from the limitations of using only position control which does not account for inherent robot inaccuracies and dynamic environments. This paper describes our work on improving and supplementing traditional position control programming methods. A baseline OLP system was implemented at NIST's Automated Manufacturing Research Facility (AMRF). Experience gained in implementing this system showed that an effective OLP system must accurately simulate the real world and must support sensor programming to compensate for real-world changes that cannot be simulated. The developed OLP geometric world model is calibrated using robot mounted ultrasound ranging sensors. This measurement capability produces a baseline geometric model of relatively good static accuracy for off-line programming. The graphical environment must also provide representations of sensor features. For this specific application, force is simulated in order to include force based commands in our robot programs. These sensor based programs are able to run reliably and safely in an unpredictable industrial environment. The last portion of this paper extends OLP and describes the functionality of a complete system for programming complex robot tasks.     

An on-line relative position and orientation error calibration methodology for workcell robot operations

Relative position and orientation inaccuracy always exists between a robot and the equipment with which it operates, especially in batch-type production cells that are subjected to dynamic changes. This inaccuracy causes robot relative positioning errors, and may even result in operation failure if the off-line programmed moving path is implemented without adjustment. To make use of off-line programming and simulation tools, an on-line calibration methodology for robot relative positioning inaccuracy was developed in this study. This methodology eliminates the need for time-consuming off-line calibrations relying on accurate models and expensive devices. An industrial robot system was enabled to detect and compensate automatically for relative positioning errors by incorporating a vision system, a 3-D force/torque sensor, and control strategies involving neural networks. The experimental results showed that this methodology is valid and robust in calibrating the relative position and orientation errors automatically without the need for mathematical models and complex off-line calibration procedures for model parameters. Consequently, batch-type production cells would be more flexible, adaptable and intelligent in accommodating dynamic workcell changes with less human effort.     

Closed form solution for the sensor registration problem using only position information

Sensors, mounted on the dexterous end of a robot, can be used for feedback control or calibration. When you mount a sensor on a robot it becomes necessary to find the pose (orientation and position) of the sensor relative to the robot. This is the sensor registration problem. Many researchers have provided closed-form solutions to the sensor registration problem; however, the published solutions apply only to sensors that can measure a complete pose (three positions and three orientations). Many sensors, however, can provide only position information; they cannot measure the orientation of an object. This article provides a closed-form solution to the sensor registration problem applicable when: (1) the sensor can provide only position information and (2) the robot can move along and rotate about straight lines. © 1994 John Wiley & Sons, Inc.     

Sensor Selection by GMB-REM in Real Robot Position Estimation

Modelling and reducing uncertainty are two essential problems with mobile robot localisation. Previously we developed a robot localisation system, namely, the Gaussian Mixture of Bayes with Regularised Expectation Maximisation (GMB-REM), using a single sensor. GMB-REM allows a robot"s position to be modelled as a probability distribution, and uses Bayes" theorem to reduce the uncertainty of its location. In this paper, a new system for performing sensor selection is introduced, namely an enhanced form of GMB-REM. Empirical results show the new system outperforms GMB-REM using sonar alone. More specifically, it is able to select between multiple sensors at each robot"s position, and further minimises the average robot localisation error.     

Optimal location of mouse sensors on mobile robots for position sensing

Optical mouse sensors have been utilized recently to measure the position and orientation of a mobile robot. This work provides a systematic solution to the problem of locating N optical mouse sensors on a mobile robot with the aim of increasing the quality of the position measurements. The developed analysis gives insights on how the selection of a particular configuration influences the estimation of the robot position, and it allows to compare the effectiveness of different configurations. The results are derived from the analysis of the singular values of a particular matrix obtained by solving the sensor kinematics problem. Moreover, given any mobile robot platform, an end-user procedure is provided to select the best location for N optical mouse sensors on such a platform. The procedure consists of solving a feasible constrained optimization problem.     

Improved Sensor Selection Technique by Integrating Sensor Fusion in Robot Position Estimation

Modelling and reducing uncertainty are two essential problems with mobile robot localisation. Previously we developed a robot localisation system, namely, the Gaussian Mixture of Bayes with Regularised Expectation Maximisation (GMB-REM), which introduced the sensor selection technique. GMB-REM allows a robot"s position to be modelled as a probability distribution and uses Bayes" theorem to reduce the uncertainty of its location. A new sensor selection technique incorporated with sensor fusion is introduced in this paper. Actually the new technique is realised by incorporating with the sensor fusion scheme. Empirical results show that the new system outperforms the previous GMB-REM with sensor selection alone. More specifically, we illustrate that the new technique is able to considerably constrain the error of a robot"s position.     

Robust bin-picking system using tactile sensor

ABSTRACT

This paper presents a robust bin-picking system utilizing tactile sensors and a vision sensor. The object position and orientation are estimated using a fast template-matching method through the vision sensor. When a robot picks up an object, the tactile sensors detect the success or failure of the grasping, and a force sensor detects the contact with the environment. A weight sensor is also used to judge whether the lifting of the object has been successful. The robust and efficient bin-picking system presented herein is implemented through the integration of different sensors. In particular, the tactile sensors realize rope-shaped object picking that has yet to be made possible with conventional picking systems. The effectiveness of the proposed method was confirmed through grasping experiments and in a competitive event at the World Robot Challenge 2018.     

一种新型的并联机器人位姿立体视觉检测系统

建立了一种并联机器人位姿立体视觉测量系统框架,主要包括图像采集与传输、摄像机标定、尺度不变量特征变换（SIFT）匹配、空间点重建和位姿测量五个部分。该系统基于SIFT,能够很好地处理图像在大视角有遮挡、平移、旋转、亮度和尺度变化时的特征点匹配,有较高的匹配精度,特别适用于对并联机器人多自由度和空间复杂运动的检测。最后使用该方法对并联机器人位姿检测做了仿真实验。     

A Rotating Sonar and a Differential Encoder Data Fusion for Map-Based Dynamic Positioning

In this paper, a dynamic positioning system using a rotating sonar and a differential encoder is proposed. The method is implemented by employing an indirect feedback Kalman filter. The state equation is written for encoder propagation and its error characteristic. A measurement equation describes a map-based measurement equation based on rotating sonar sensor data. In other words, sonar data compensates for the system and navigation errors of the differential encoder. The positioning system calculates the position and headings of a mobile robot. The real-time calculation is performed by a map-based measurement update utilizing wide-angle beam characteristics of the sonar sensor and the Kalman filter. In addition, an observability analysis for the positioning system is performed. Experimental results show that the proposed hybrid positioning system successfully provides accurate position and headings in real-time. The position and heading errors arc bounded within few centimeters and within few degrees, respectively.     

移动机器人方位测量仪

为了直接测量移动机器人的绝对方位角,利用磁阻式传感器对大地磁场敏感的特性开发了移动机器人方位测量仪。主要介绍了磁阻元件、信号放大、转换电路和误差补偿各部分的原理和方法,并通过引入神经网络补偿器,有效地减少了传感器的系统性误差,经过在移动机器人上的现场试验证明:该方位测量仪是可行的。     

Indoor low-cost localization system for controlling aerial robots

This paper presents a low-cost localization system to guide an Unmanned Aerial Vehicle (UAV) in indoor flights, considering an environment with invariant texture and typical indoor illumination. The first contribution of the paper is the proposal of a system to estimate the position and orientation of the UAV, through a multi-sensor fusion scheme, dealing with data provided by a RGB-D sensor, an inertial measurement unit (IMU), an ultrasonic sensor and optical flow-based velocity estimates. A second contribution of the paper is the proposal of a high-level control system to guide the UAV in path-following tasks, involving two controllers: a kinematic one, responsible for generating reference velocities for the vehicle, and a PD one, responsible for tracking such reference velocities, thus characterizing a cascade controller. Experiments with such a localization and control systems, during which abrupt disturbances are applied, were carried out to check the effectiveness of the developed capture and control systems, whose results validate the proposed framework.     

一种基于视觉的移动机器人定位系统

具有自主的全局定位能力是自主式稳定机器人传感器系统的一项重要功能,为了实现这个目的,国内外均在不断地研究发展各种定位传感器系统,这里介绍了一种采用光学蝗全方位位置传感器系统,该传感器系统由主动式路标、视觉传感器、图象采集与数据处理系统组成,其视觉传感器和数据处理系统可安装在移动机器人上,然后可通过观测路标物「视角定位的方法,计算出机器人在世界坐标系中的位置和方向,实验证明,该系统可以只的在线定位,     

基于多传感器集成的仿人机器人足部感知系统

足部是仿人机器人本体支撑的基础,也是唯一与地面接触并发生相互作用的主要部件,其各种地面信息获取能力是机器人实现仿人的自然性稳定行走控制的关键.基于六维力传感器、惯量测量单元和柔性触觉阵列传感器,设计了一种新型仿人机器人集成化足部感知系统(IPFS).具备对各种地面环境识别和足部姿态获取、足底与外界接触位置的实时感知和估...     

高压巡线机器人电磁传感器导航方法

提出了一种采用电磁传感器为高压巡线机器人沿输电线路相线进行导航的方法。该方法根据相对值检测原理,通过合理布局的传感器测头阵列,将空间划分为不同的区域,根据传感器的输出,经过特定的算法,即可判断高压导线相对于机器人的空间位姿。实验室及实际线路带电运行实验表明:电磁传感器工作可靠,导航方法切实可行,稳定识别精度为10 mm,满足实际线路带电运行要求。     

基于立体视觉的串联机器人跟踪检测系统

建立一种六自由度串联机器人视觉跟踪检测系统框架,包括图像采集、摄像机标定、机器臂跟踪检测、机器臂位姿建模与计算等。提出利用CamShift算法对机器人进行在线粗跟踪,搜寻和画定出机器臂操作器在当前窗口的区域位置。对跟踪到的机器臂按照SURF算法进行特征提取与立体匹配。该方法被用于对串联机器人位姿检测进行实验。实验结果表明,2种算法的结合适用于六自由度串联机器人在空间复杂运动的跟踪检测。     

用于移动机器人的视觉全局定位系统研究

本文叙述了用于移动机器人自主导航定位的一种视觉全局定位系统技术．该视觉定 位系统由LED主动路标、全景视觉传感器和数据处理系统组成．本文主要介绍了为提高全景 视觉图像处理速度和环境信标识别可靠性、准确性的应用方法,并给出了实验结果．实验表 明,视觉定位是具有明显研究价值和应用前景的全局导航定位技术．     

工业机器人精度参数的一种测量方法

本文提出在工作空间中对工业机器人位置和方位精度参数进行测量的一种实用方法,包括测量原理、微机测量系统的组成、测量软件和系统测量误差分析,同时给出对 Junghelnrich 机器人实际测量的部分结果。