【作者】 王卫华；

【导师】 熊有伦； 孙容磊；

【作者基本信息】 华中科技大学 ， 机械电子工程， 2005， 博士

【摘要】 移动机器人定位是机器人学的重要研究方向,也是机器人实现自主导航的关键,对于提高机器人自动化水平具有重要意义。定位方法分为两类:绝对定位和相对定位。绝对定位要求机器人在不指定初始位置的情况下确定自己的位置。相对定位指机器人在给定初始位置的条件下确定自己的位置,是机器人定位处理中主要的研究方向。论文对相对定位技术进行了深入分析和系统研究,包括机器人系统参数校核、轮子打滑辨识与校核、测程法噪声建模和噪声统计特性的自适应估计、激光扫描数据和环境地图的匹配及基于激光扫描数据的机器人位姿误差补偿等问题。准确的机器人系统参数是精确定位的前提。UMBmark 校核算法是最广泛使用的机器人系统参数校核方法; 然而在实验机器人控制精度较低的情况下,该算法不能有效校核由于驱动轮实际直径的均值和名义直径不相等导致的方向误差,从而影响校核效果。论文深入分析UMBmark 校核算法,提出改进算法以测量低控制精度机器人的系统误差。改进算法以不相等的轮直径、轮距的不确定及驱动轮实际直径均值和名义直径不相等为测程法系统误差的主要来源,建立新的测程法系统误差模型,给出详细算法计算机器人的系统参数及对应的校核系数。实验结果表明改进算法比UMBmark校核方法具有更好的校核效果。校核轮子打滑导致的误差是提高机器人定位精度的关键。在电磁罗盘不受磁场干扰的前提下,基于编码器和电磁罗盘的测量信息,提出移动机器人直线移动时轮子打滑辨识和校核算法。基于实验数据,利用统计理论建立机器人直线移动时的轮子打滑模型。该模型能够判断机器人移动过程中轮子是否发生滑动; 当轮子打滑时,提出一种新颖的算法来判断哪个轮子发生滑动并校核对应的位置误差和方向误差。基于轮子打滑模型,进一步提出一种间接卡尔曼滤波器校核机器人驱动轮直径和轮距。实验结果表明:通过校核轮子打滑导致的误差,机器人的定位精度得到显著提高,且间接卡尔曼滤波器对机器人驱动轮直径具有良好的校核效果。测程法噪声统计特性的自适应估计问题是机器人定位研究的难点。论文系统分析了测程法的噪声来源,考虑编码器测量精度的影响,建立测程法噪声模型。基于该噪声模型,推导出计算测程法噪声统计特性的方程,使机器人在移动过程中能够根据导航环境的变化实时地、自适应地估计测程法噪声统计特性。激光定位是目前定位研究的热点,快速实现激光扫描数据和环境地图匹配以提取线段特征是激光定位研究的重要步骤。论文定义g-权重Hough 转换,提出“平面有效区域”的概念,从激光扫描数据中提取线段特征的同时实现了线段特征和环境地图的更多还原

【Abstract】 As an important research area of robotics, localization is the critical step towards autonomous navigation of mobile robots, and promises to improve automatization level. Position-estimation methods can be categorized into two groups: absolute and relative positioning. Absolute positioning requires that robot determines its position when initial position isn’t given. Relative positioning is the process of position determination on condition that robot is told its initial position, which is the main research topic of localization. In this dissertation, technologies related to relative positioning are focused on, including calibration of systematic parameters of robot, identification and correction of wheel-slippage, modeling of odometric noises and adaptive estimation of noise statistic character, matches between laser scanning data and map and posture error compensation of robot based on laser scanning data. Correct systematic parameters of robot are the prerequisite to accurate localization. UMBmark Calibration is the most widely used method to calibrate systematic parameters of robot. However, it can’t effectively correct the orientation error due to the difference between the average of two actual wheel diameters and the nominal wheel diameter when experimental robot has poor control precision, which deteriorates calibration result. This dissertation analyzes UMBmark Calibration and presents an improved method to measure systematic errors of low-precision mobile robots. The improved method considers unequal wheel diameters, uncertainty about the wheelbase and the difference between the average actual wheel diameter and the nominal wheel diameter as the main error sources of odometry and defines a new model for odometry. Detailed algorithm is proposed to calculate the systematic parameters of a mobile robot and the corresponding correction factors. Experimental results show that the improved method has better calibration effect than UMBmark Calibration. Correcting errors from wheel-slippage is the key technology of precise localization. Under the constraint that electronic compass isn’t distorted by magnetic interference, this dissertation is based on the information from encoders and electronic compass, and proposes a novel algorithm to identify and calibrate wheel-slippage when robot performs straight-line motion. Based on experimental data, statistic theory is implemented to define a model for wheel-slippage. This model can judge whether driving wheels have slippages when robot navigates. Based on the model, an algorithm is presented to determine which driving wheel has slippage and calibrate the resulting orientation error and displacement error when wheel-slippage occurs, and an Indirect Kalman Filter (IFK) is further introduced to calculate the actual diameters of driving wheels and wheelbase of robot. Experimental results show that localization accuracy can be effectively improved by correcting wheel-slippage, and the diameters of driving wheels can be precisely determined by IFK. Adaptive estimation of the statistic character of odometric noises is a difficult problem for localization. In this dissertation, noise sources of odometry are analyzed, the errors due to encoder resolution are taken into account and a model for odometric noises is defined. Based on the model, equations are deduced to estimate the statistic character of odometric noises real-time and adaptively when mobile robot moves. Recently, laser-based localization has been a topic of interest in position-estimation. It is an important step for laser-based localization to extract line segment characteristics by rapidly matching laser scanning data with map. By defining g-weighted Hough transform, Effective Area of Plane (EAP) is put forward in this dissertation. Line segment characteristics are extracted from laser scanning data, and simultaneously the matches between line segments and map are realized, which greatly improves process efficiency of laser scanning data. Simulation results show that line segment information can be effectively and rapidly extracted by our method. Based on the line segments extracted from laser scanning data, weighted least squares are utilized to calculate orientation error, and point-to-line least squares and point-to-point least squares are proposed to compute position error. By comparison, it is found that point-to-point least squares have better calibration effect than point-to-line least squares. Extended Kalman Filter (EKF) is implemented to fuse the information from encoders and laser range finder to locate mobile robot. Experimental results show that orientation error and position error can be effectively corrected and robot’s pose is also accurately tracked by EKF.更多还原