【作者】 于金霞；

【导师】 蔡自兴；

【作者基本信息】 中南大学 ， 控制理论与控制工程， 2007， 博士

【摘要】 本论文来源于国家自然科学基金支持的“未知环境中移动机器人导航控制的理论与方法研究”(批准号：60234030)。作为该项目研究的一部分，本论文以移动机器人导航中的定位问题为研究内容，利用自行研制的装配有二维激光雷达环境感知系统，并通过里程计、陀螺仪等内部传感器来实现航迹推测的移动机器人“中南移动1号MORCS-1”，重点围绕影响移动机器人系统定位的四类不确定性处理展开研究：通过移动机器人定位传感器的误差分析及校准，旨在消除传感器噪声所带来的测量误差；通过建立移动机器人的三维运动学模型进行航迹推测，以期实现复杂地形下精确的移动机器人本体姿态感受；通过基于激光雷达的动静态障碍的自主检测等相关研究，尽量消除环境的不确定因素影响进而实现可靠的移动机器人绝对定位；通过以上研究，针对未知数据关联下移动机器人增量式环境建图与自定位的研究提出一种鲁棒的滤波算法，改善未知环境中移动机器人自定位的性能。总结全文，在移动机器人定位的不确定性处理研究中，提出了一些具有一定创新性的方法：结合自行研制的移动机器人MORCS-1系统的多种内外部定位传感器，针对内部本体感受传感器光纤陀螺仪的漂移误差，提出采用基于遗传算法优化的神经网络来对光纤陀螺仪的温漂建模及校准，能够将其温漂变化控制在恒温条件下标准测试输出附近；针对外部环境感知传感器激光雷达测距数据中包含的噪声干扰，考虑移动机器人导航中激光雷达测距数据的时空关联性，提出采用动态自适应滤波技术进行预处理，从而可以有效滤除噪声干扰满足导航中障碍实时精确检测的要求。根据刚体运动学的约束分析了一种轮式结构与悬浮式摇架系统相结合的移动机器人在复杂地形下的航迹推测，采用里程计、光纤陀螺仪、倾角传感器等传感器信息推导移动机器人的运动学模型，提出一种运动学模型与车轮．地面运动角度实现运动轨迹估计的方法。通过对不同地形下的运动进行仿真以及利用机器人进行的实验，获得的移动机器人航迹推测效果比直接运用倾角推测的效果更为接近真实值。利用二维激光雷达作为环境感知的外部传感器，通过占据栅格地图融合机器人航迹推测的位姿信息和激光雷达的障碍测距信息，提出一种非静态环境中基于二维激光雷达的自主动静态障碍检测方法。对于获得的动态障碍，利用改进建议分布的粒子滤波实现运动过程跟踪定位。对于获得的静态障碍地图，将模糊逻辑与最大似然估计相结合，采用基于模糊似然的局部地图匹配方法改进移动机器人自定位的性能。实验结果表明该方法能够实现动静态障碍自主检测与分离，进行有效的单目标跟踪定位，并可以校准航迹推测误差。针对未知数据关联下移动机器人的增量式环境建模与自定位，利用改进的Rao-Blackwellized粒子滤波算法实现移动机器人位姿和环境特征位置的联合评估。为了自主地对二维激光雷达的环境障碍感知信息进行类别划分特征提取，提出将无监督聚类学习应用于障碍的特征提取，并将模糊逻辑引入到增量式特征的数据关联进行障碍的分类判决。对于评估机器人路径位姿的粒子滤波进行了两点改进：考虑将地图匹配和粒子滤波重采样相结合，并基于有效样本大小ESS来实现粒子滤波的重采样自适应；对于环境特征的评估，利用过程噪声自适应评估技术和Unscented卡尔曼滤波相结合的滤波方法。更多还原

【Abstract】 This dissertation is supported by the key project of the National NaturalScience Foundation of China under grant no.60234030, Research on Theoriesand Methods of Navigation Control for Mobile Robots under UnknownEnvironments. As one part of the project, the dissertation is developed with thelocalization problem in mobile robot navigation. Combined with "MobileRobot 1 of Central South university (MORCS-1)", a mobile robot designed byus that equipped with a 2D laser measurement system to sense the environmentand the proprioceptive sensors such as the odometry, gyroscope to calculate itsdead reckoning, the approach about the four kind uncertainty factors of mobilerobot localization is studied. These researches include that the error analysisand calibration of position sensors is implemented to reduce the measurementnoise, the 3D kinematic model of mobile robot is built to gain the accuratepose in complex terrain, some work on the automatic detection of static ordynamic obstacles based on laser scanner is investigated to eliminate thedynamic influence of the environment and to realize the reliably absoluteposition, and lastly a robust algorithm is presented to involve the incrementalenvironment mapping and self-localization of mobile robot with unknown dataassociation and to improve the self-localization performance of mobile robotunder unknown environment.So, the study in this dissertation focuses on some key points in theuncertainty treatment of mobile robot localization as follows:Combined with the multiple proprioceptive and exteroceptive sensors ofmobile robot MORCS-1, aimed at the drift error of fiber optic gyro as theproprioceptive sensor, the neural network using genetic algorithm as optimaltool is proposed to accomplish the modeling and calibration for temperaturedrift of fiber optic gyro, which can reduce the drift error to the standard outputat constant temperature; and aimed at the noisy disturbance of ranging datafrom the exteroceptive sensor laser scanner, a dynamic adaptive filter isintroduced through the analysis of neighboring ranging data in time and spatialcorrelation to realize the real-time and dynamic filter, which can validly filter the noisy disturbance to meet the requirement of the accurately real-timeobstacle detection in mobile robot navigation.Dead reckoning of mobile robot in complex terrain is analyzed by therigid-body kinematic constraints of mobile robot that is on the basis oflocomotion architecture with the wheeled and rocker-bogie suspension system.At the same time, the kinematic model of mobile robot is obtained using themultiple sensors’ information from odometry, fiber optic gyro, tilt sensor, et al.A method of kinematic model integrated with wheel-ground contact angle issuggested to estimate the relative motion trajectory of mobile robot.Experimental results obtained in simulation and with real robot on differentterrains demonstrate that this method is more close to real pose of mobile robotthan to calculate only with the pitch.2D laser scanner is utilized to sense the operating environment of mobilerobot, and the occupancy grids map is imposed to fuse the information of therobot’s pose by dead reckoning and the range to obstacles by laser scanner. Anautomatic detection method of static and dynamic obstacles is investigatedbased on 2D laser scanner in non-static environment. The particle filter withthe improved proposal distribution is adopted to track the dynamic obstacles soas to get the localization performance in motion process, and the local mapmatching combined fuzzy logic with maximum likelihood estimation isintroduced to deal with the static obstacles so as to improve theself-localization capability of mobile robot. These methods is verified byexperiments, which shows that it can autonomously divide and detect staticand dynamic obstacles, efficiently track single dynamic obstacle and calibratethe error of dead reckoning.Aimed at the incremental environment mapping and self-localization ofmobile robot with unknown data association, the Rao-Blackwellized particlefilter is improved to get the unite estimation of the pose of mobile robot andthe position of the environmental features. In order to make the right obstacleclassification from the 2D laser scanner, an unsupervised clustering algorithmis presented to realize the feature extraction of obstacles and fuzzy logic isintegrated into incremental data association of obstacles features. Moreover,particle filter for the pose estimation of mobile robot is mended by executing its resampling strategy after the map matching and adapting the resamplingprocess grounded on the effective sample size (ESS). Furthermore, theunscented Kalman filter with the adaptation estimation for the process noise isintroduced into the position evaluation of the environmental features.更多还原