移动机器人及其室内定位系统研究

发布时间：2018-05-06 01:04

本文选题：移动机器人 + 室内定位　；参考：《南昌大学》2016年硕士论文

【摘要】：机器人自主导航是当今研究的热点问题,从核电站环境中的巡检机器人到日常生活中的服务机器人都离不开机器人自主导航,而机器人精确的定位则是实现可靠导航的基本前提。由于室内环境的复杂性,综合考虑各种室内定位方法的优缺点,论文采用由惯性导航定位法和航迹推算法形成的组合导航方法来实现较高精度的机器人实时定位。首先,根据机器人基本运动功能要求及室内定位要求,选用了STM32控制器作为机器人的主控芯片,搭建了移动机器人的硬件平台。对电源、电机驱动、电机测速、MEMS传感器及WIFI通信等模块分别进行选型和接口设计,并对这些模块进行了调试。其次,分别介绍惯性导航系统和航迹推算系统的工作原理,并对它们的数字更新算法和误差方程进行推导。然后,结合机器人运动的特点,适当地简化惯性导航系统和航迹推算系统的误差方程。采用卡尔曼滤波的方法将两个系统的误差模型进行融合,取长补短,充分发挥各定位系统的优势。把卡尔曼滤波输出的最优估计误差用来修正数字更新后的姿态、速度和位置信息,得到精度较高的机器人位姿信息。最后,采用C语言完成了系统的软件设计,并在机器人平台上进行了实时定位测试。分别将圆形轨迹和矩形轨迹的定位结果与真实轨迹对比分析,实验结果证明该算法能实现较高精度的定位。
[Abstract]:Robot autonomous navigation is a hot issue in today's research. From the inspection robot in the nuclear power plant environment to the service robot in daily life, robot autonomous navigation can not be separated from the robot autonomous navigation. The accurate positioning of the robot is the basic premise of reliable navigation. Due to the complexity of indoor environment, the advantages and disadvantages of various indoor positioning methods are considered synthetically. In this paper, the integrated navigation method formed by inertial navigation method and track reckoning method is used to realize the real-time positioning of robot with high accuracy. Firstly, according to the requirements of basic motion function and indoor positioning, the STM32 controller is selected as the main control chip of the robot, and the hardware platform of the mobile robot is built. The selection and interface design of power supply, motor drive, motor speed measuring MEMS sensor and WIFI communication module are carried out, and these modules are debugged. Secondly, the principles of inertial navigation system and track reckoning system are introduced, and their digital updating algorithms and error equations are deduced. Then, the error equations of inertial navigation system and track reckoning system are simplified according to the characteristics of robot motion. The error models of the two systems are fused by Kalman filter to make up for each other and give full play to the advantages of each positioning system. The optimal estimation error of Kalman filter output is used to correct the position, velocity and position information after digital updating, and the robot pose information with high accuracy is obtained. Finally, the software design of the system is completed with C language, and the real-time positioning test is carried out on the robot platform. The results of circular and rectangular locus are compared with those of real locus, and the experimental results show that the algorithm can achieve high precision localization.
【学位授予单位】：南昌大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP242

【参考文献】