轮式移动机器人里程计系统误差校核*

里程计使用编码器为轮式移动机器人提供基本的位姿估计,在运行过程中里程计存在严重的误差累计,通过校核系统参数可以减小系统误差,UMBmark方法是轮式移动机器人广泛使用的系统误差校核方法。针对UMBmark方法存在的不足,提出一种改进的系统误差校核新方法：综合考虑三种主要系统误差来源产生的误差对移动机器人直线运动和定点旋转运动造成的影响,同时采用正方形回路终点的方向误差代替传统UMBmark方法中的位置误差来校核系统参数。实验结果表明提出的方法能够有效校核系统参数,提高移动机器人的定位精度。     

融合测程法与视觉信息的足球机器人自定位方法

针对足球机器人自定位问题,提出一种融合测程法与视觉信息的定位方法.方法综合考虑两种信息的特点,有效实现优势互补:一方面,针对视觉定位易出现的歧义,利用测程法获得的定位结果予以有效消解;另一方面,随着运动,测程法定位易出现误差的累积,利用消歧后的视觉定位结果加以动态修正.最后,在Webots模拟平台上进行的机器人球场定位实验表明文中方法的有效性.     

基于Accodometry法的两轮自平衡机器人位置估计研究

针对两轮自平衡机器人运行过程中遇到打滑、越障、碰撞等异常事件,测程法进行位置估计失效的情况,提出一种Accodometry方法,通过融合码盘与加速度计数据对位置进行估计,解决了非系统测程法误差对机器人位置估计的影响,降低了加速度计固有漂移的不利影响,提高了两轮自平衡机器人的定位精度.实验验证了Accodometry方法的有效性,结果显示位置误差降为原来的1/4.     

立体视觉测程研究进展

立体视觉测程技术正逐渐成为移动机器人在非结构化环境中导航定位的主要实现方法.本文对立体视觉测程技术在机器人导航应用中的现状进行分析,总结目前立体视觉测程技术的主要研究内容和方法,评述各种技术方法的优缺点,最后对立体视觉测程的发展进行展望.     

基于改进Alopex算法的燃机中冷器优化设计

舰船燃机中冷器可以改善其变工况性能,所以中冷器的质量对舰船有效载荷有影响.但目前中冷器优化多采用简单进化算法且约束条件多采用公式拟合致使误差较大,无法得到最优解.为了提出可行的设计优化中冷器算法,对板翅式换热模块采用效率-传热单元数法建立性能校核程序,随后研究全局及局部搜索能力俱佳的改进Alopex(Algorithms of patternextraction)进化算法,并将优化算法与性能校核过程结合,开发新的中冷器芯体质量、效率和热侧压降的多目标混合优化设计程序.数值优化结果表明,改进混合算法能够得到比SGA(简单遗传算法)和简单Alopex算法更优的设计结果.结果证明,改进方法能够为中冷器进一步优化设计提供参考.     

一种移动机器人轮子打滑的实验校核方法

提出一种机器人不受磁场干扰情况下，利用编码器和电磁罗盘校核轮子打滑的方法．该方法定义机器人轮子打滑的模型，利用该模型能够判断轮子是否发生滑动．当机器人轮子打滑时，提出一种算法来判断哪个驱动轮打滑并校核轮对应的方向误差和位置误差．试验结果表明了本文算法的有效性．     

仿真系统状态不连贯问题及解决方法研究

阐述了模型校核的意义和作用.对模型校核问题进行了研究.对属于模型校核范畴的仿真中的系统状态不连贯问题的基本概念、产生原因、存在和表现形式进行了说明.指出,解决系统状态不连贯问题的关键是以要求的精确度检测到系统状态不连贯的发生.分析了已有的解决系统状态不连贯问题的三种方法,并进行了优、缺点分析.提出了一种新方法一预测法.利用上述方法,最多进行两步最小步长仿真,就能够以要求的精确度检测到任何一个系统状态不连贯.针对系统状态不连贯问题的两个实例,具体应用了预测法.     

自主机器人自定位模块的设计

介绍了基于AT89C2051微处理器的机器人自定位模块的设计方法.为了解决用到的测程法存在无界累计误差的问题,将光电开关测得的信息与电子罗盘测得的角度信息进行融合,提高了定位的精度.通过硬件和软件的设计及具体实现证明了该方法的可行性和很好的鲁棒性.     

基于自控测距法的机器人位姿估计

针对两轮自平衡机器人运行过程中遇到打滑、越障、碰撞等异常事件,测程法进行位姿估计失效的情况,提出一种自控测距法。结合改进的Gyrodometry方法,通过融合码盘、陀螺仪与加速度计数据对机器人的位姿进行估计,实现了机器人的准确定位,解决了非系统定位误差对机器人位姿估计的影响,降低了陀螺仪、加速度计固有漂移的不利影响,提高了两轮自平衡机器人的定位精度。实验结果显示与测程法误差相比自控测距法位置误差降为1／3,方向误差降为1／6,验证了自控测距法的有效性。     

基于高级Petri网的仿真剧情正规校核方法

针对仿真剧情主观校核不理想这一问题，提出了基于高级Petri网的仿真剧情正规校核方法．首先给出仿真剧情的形式化定义，并分析仿真剧情可能存在的错误类型；其次给出仿真剧情到高级Petri网的映射途径，并给出基于高级Petri网的仿真剧情校核准则和算法，此外，还给出实现仿真剧情动态校核的推理规则和机制；最后给出了一个正规校核工具框架．实际应用已经证明了该方法的有效性．     

Improved Pose Estimation for Mobile Robots by Fusion of Odometry Data and Environment Map

This paper proposes a new approach for calibration of dead reckoning process. Using the well-known UMBmark (University of Michigan Benchmark) is not sufficient for a desirable calibration of dead reckoning. Besides, existing calibration methods, usually require explicit measurement of actual motion of the robot. Some recent methods, use the smart encoder trailer or long range finder sensors such as ultrasonic or laser range finders for automatic calibration. Manual measurement is necessary in the case of the robots that are not equipped with long range detectors or such smart encoder trailer. Our proposed approach, uses an environment map that is created by fusion of proximity data, in order to calibrate the odometry error automatically. In the new approach, the systematic part of the error is adaptively estimated and compensated by an efficient and incremental maximum likelihood algorithm. Actually, environment map data are fused with the odometry and current sensory data in order to acquire the maximum likelihood estimation. The advantages of the proposed approach are demonstrated in some experiments with Khepera robot. It is shown that the amount of pose estimation error is reduced by a percentage of more than 80%.     

A calibration method for odometry of mobile robots based on the least-squares technique: theory and experimental validation

For a mobile robot, odometry calibration consists of the identification of a set of kinematic parameters that allow reconstructing the vehicle's absolute position and orientation starting from the wheels' encoder measurements. This paper develops a systematic method for odometry calibration of differential-drive mobile robots. As a first step, the kinematic equations are written so as to underline linearity in a suitable set of unknown parameters; thus, the least-squares method can be applied to estimate them. A major advantage of the adopted formulation is that it provides a quantitative measure of the optimality of a test motion; this can be exploited to drive guidelines on the choice of the test trajectories and to evaluate accuracy of a solution. The proposed technique has been experimentally validated on two different mobile robots and, in one case, compared with other existing approaches; the obtained results confirm the effectiveness of the proposed calibration method.     

室内移动机器人视觉里程计研究

研究了一种精度较高的视觉里程计实现方法。首先采用计算量小、精度高的SURF算法提取特征点,并采用最近邻向量匹配法进行特征点匹配;采用RANSAC算法求得帧间摄像机坐标系的转换矩阵;提出了一种视觉里程计标定方法,并通过该方法得到机器人起始点坐标系下的位移信息,保证了机器人有自身姿态变化时的定位的准确性;圆轨迹实验验证了视觉里程计算法的可行性。     

Simultaneous localization and odometry self calibration for mobile robot

This paper presents both the theory and the experimental results of a method allowing simultaneous robot localization and odometry error estimation (both systematic and non-systematic) during the navigation. The estimation of the systematic components is carried out through an augmented Kalman filter, which estimates a state containing the robot configuration and the parameters characterizing the systematic component of the odometry error. It uses encoder readings as inputs and the readings from a laser range finder as observations. In this first filter, the non-systematic error is defined as constant and it is overestimated. Then, the estimation of the real non-systematic component is carried out through another Kalman filter, where the observations are obtained by two subsequent robot configurations provided by the previous augmented Kalman filter. There, the systematic parameters in the model are regularly updated with the values estimated by the first filter. The approach is theoretically developed for both the synchronous and the differential drive. A first validation is performed through very accurate simulations where both the drive systems are considered. Then, a series of experiments are carried out in an indoor environment by using a mobile platform with a differential drive.     

一种有效的移动机器人里程计误差建模方法

移动机器人里程计误差建模是研究移动机器人定位问题的基础. 现有的移动机器人里程计误差建模方法多数针对某一种驱动类型移动机器人设计, 运动过程中缺乏对里程计累计误差的实时反馈补偿, 经过长距离运动过程定位精度大幅度降低. 因此本文针对同步驱动和差动驱动轮式移动机器人平台提出了一种通用的里程计误差建模方法. 在假设机器人运动路径近似弧线基础上, 依据里程计误差传播规律推导了非系统误差、系统误差与里程计过程输入之间的近似函数关系, 进而提出一种具有闭环误差实时反馈补偿功能的移动机器人定位算法, 对定位过程中产生的里程计累计误差给予实时反馈补偿. 实验表明新算法有效地减少了里程计累计误差, 提高了定位精度.     

Portable autonomous walk calibration for 4-legged robots

In the present paper we describe an efficient and portable optimization method for calibrating the walk parameters of a quadruped robot, and its contribution for the robot control and localization. The locomotion of a legged robot presents not only the problem of maximizing the speed, but also the problem of obtaining a precise speed response, and achieving an acceptable odometry information. In this study we use a simulated annealing algorithm for calibrating different parametric sets for different speed ranges, with the goal of avoiding discontinuities. The results are applied to the robot AIBO in the RoboCup domain. Moreover, we outline the relevance of calibration to the control, showing the improvement obtained in odometry and, as a consequence, in robot localization.     

Effect of global position information in unknown world exploration — A case study using the Teleworkbench

This paper presents empirical results of the effect of the global position information on the performance of the modified local navigation algorithm (MLNA) for unknown world exploration. The results show that global position information enables the algorithm to maintain 100% success rate irrespective of initial robot position, movement speed, and environment complexity. Most mobile robot systems accrue an odometry error while moving, and hence need to use external sensors to recalibrate their position on an ongoing basis. We deal with position calibration to compensate the odometry error using the global position information provided by the Teleworkbench, which is a teleoperated platform and test bed for managing experiments using mini-robots. In this paper we demonstrate how we incorporate the global position information during and after the experiments.     

High resolution relative localisation using two cameras

Wheel odometry is a common method for high resolution relative localisation. However, wheel odometry relies on the integrity and accuracy of a kinematic model. In this paper, a new method for relative localisation, ‘visiodometry’, which does not rely on a kinematic model, is proposed. The system consists of two ground-facing cameras mounted on either side of the robot. From the sequence of images acquired, the relative change in pose of the robot is estimated using a phase correlation based method. Results on a plain coloured carpeted surface, show that the method provides a truly odometric type sensor data input similar in modality and resolution to wheel odometry. A method to calibrate the visiodometry system using a 1D object is also presented.     

Robust real-time ground plane motion compensation from a moving vehicle

One method to detect obstacles from a vehicle moving on a planar road surface is the analysis of motion-compensated difference images. In this contribution, a motion compensation algorithm is presented, which computes the required image-warping parameters from an estimate of the relative motion between camera and ground plane. The proposed algorithm estimates the warping parameters from displacements at image corners and image edges. It exploits the estimated confidence of the displacements to cope robustly with outliers. Knowledge about camera calibration, measuremts from odometry, and the previous estimate are used for motion prediction and to stabilize the estimation process when there is not enough information available in the measured image displacements. The motion compensation algorithm has been integrated with modules for obstacle detection and lane tracking. This system has been integrated in experimental vehicles and runs in real time with an overall cycle of 12.5 Hz on low-cost standard hardware. Received: 23 April 1998 / Accepted: 25 August 1999     

Flexible three-dimensional modeling of plants using low- resolution cameras and visual odometry

The three-dimensional reconstruction of plants using computer vision methods is a promising alternative to non-destructive metrology in plant phenotyping. However, diversity in plants form and size, different surrounding environments (laboratory, greenhouse or field), and occlusions impose challenging issues. We propose the use of state-of-the-art methods for visual odometry to accurately recover camera pose and preliminary three-dimensional models on image acquisition time. Specimens of maize and sunflower were imaged using a single free-moving camera and a software tool with visual odometry capabilities. Multiple-view stereo was employed to produce dense point clouds sampling the plant surfaces. The produced three-dimensional models are accurate snapshots of the shoot state and plant measurements can be recovered in a non-invasive way. The results show a free-moving low-resolution camera is able to handle occlusions and variations in plant size and form, allowing the reconstruction of different species, and specimens in different stages of development. It is also a cheap and flexible method, suitable for different phenotyping needs. Plant traits were computed from the point clouds and compared to manually measured reference, showing millimeter accuracy. All data, including images, camera calibration, pose, and three-dimensional models are publicly available.