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多传感器信息融合及其在机器人导航中的应用
其他题名Multi-sensor Attitude Fusion and Its Application In Robot Navigation
高建辉
学位类型工程硕士
导师李成荣
2013-05-31
学位授予单位中国科学院大学
学位授予地点中国科学院自动化研究所
学位专业控制工程
关键词移动机器人 全局定位 传感器校准 扩展ekf滤波 轨迹跟踪 Mobile Robot Global Localization Sensor Calibration Extended Ekf Filtering Trajectory Tracking
摘要近几十年来,随着计算机技术、信息技术和人工智能技术的不断发展,移动机器人的应用领域越来越广泛,除了在工业、农业等传统领域外,移动式服务机器人在家居、医疗环境等许多方面都得到了越来越广泛的应用。在自主移动机器人研究领域中,最主要的三个问题是路径规划、全局定位和轨迹跟踪问题。其中,全局定位需要获取机器人的姿态信息,包括位置和姿态角。因此,获取精确地姿态角对于轨迹跟踪至关重要。 本文在深入分析国内外研究现状的基础上,以机器人在全局坐标系中偏航角的求解为重点,研究了基于加速度计、陀螺仪、磁强计的多传感器姿态融合算法,产生出抗干扰性、精度高的机器人姿态角信息,并设计了基于运动学模型的轨迹跟踪控制器。论文的主要工作包括以下几个方面: 1.针对低成本传感器的误差问题,在分析传感器可预测误差来源的基础上,提出了消除零点漂移、标度因子误差、安装误差的数学校准模型,使用基于椭球的最小二乘拟合法和Gauss-Newton迭代法分别进行校准,并对两种方法的校准结果做了对比。 2.在对传感器输出的原始数据校准后,构建了基于加速度计、陀螺仪和磁强计的微惯性姿态测量系统,该系统利用加速度计和磁强计测量值补偿陀螺仪的零漂,利用陀螺仪保证动态性能的稳定性,通过扩展卡尔曼滤波器融合产生最优姿态估计,实验证明该捷联式姿态测量系统具有体积小、精度高等优点。 3.在前两步的基础上,将基于路标的移动视觉定位模块确定的全局位置坐标和三传感器融合的姿态角作为反馈量,设计了基于运动学模型的全向移动平台解耦轨迹跟踪控制器,并且通过Matlab仿真验证了控制算法的可行性和全局稳定性。
其他摘要In recent decades, with the development of computer technology, information technology and artificial intelligence technology, the mobile service robot is not only used in the traditional areas such as industry, agriculture, but also in health care, homes and so on. Path planning, global localization and trajectory tracking play an important role in the realization of autonomous mobile robots. Of the three branches, global localization, which includes the robot’s position and yaw angle, is very important to navigation and control. In this paper, on the basis of analyzing overall technical requirements and related literature, we mainly focus on solving the yaw angle of mobile robot in the global coordinate system. With the sensors of accelerometer, gyroscope, magnetometer, Extended Kalman filter is used to fuse three kinds of sensors such as accelerometer, gyroscope, magnetometer, to obtain the the precise yaw information, and a trajectory tracking decoupling controller is also designed, based on the omni-directional mobile robot’s kinematics model. In general, the main work of this paper includes following aspects: Firstly, the sensor-error calibration problem is resolved. After analyzing the sources of sensors’ predictable error, the mathematical error calibration model is established. Then we use the ellipsoid-based least square fitting method and Gauss-Newton iterative calibration method to calibrate the sensor. The calibration results of the two methods are also compared. Secondly, the micro Inertial Attitude Heading Reference System is constructed. The system is composed of gyroscope, accelerometer and three-axis magnetometer. In the system accelerometer and magnetometer are used to compensate for the gyroscope’s zero drift, and gyro is used to enhance the system’s accuracy and stability in the moving environment. The optimal attitude estimate is generated by Kalman filter. The experiment shows that system is a good choice for high-precision attitude angle. Finally, on the basis of the previous two steps, a decoupling controller based on the omni-directional mobile robot’s kinematics model is designed. In the controller, the position coordinates from passive infrared landmark visual localization module and yaw angle from sensor fusion module are regarded as feedback signals. The trajectory tracking controller has global stability. At last, we use Matlab simulation to verify the feasibility of the controller and system st...
馆藏号XWLW1959
其他标识符2010E8014668005
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/7700
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
高建辉. 多传感器信息融合及其在机器人导航中的应用[D]. 中国科学院自动化研究所. 中国科学院大学,2013.
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