The Research On Methods Of Mobile Robot Simultaneous Localization And Mapping

In mobile robot applications, it is a fundamental and important requirement that the robot should be able to localize itself accurately within its operating environments. It is a challenging research topic in mobile robot which has attracted many researchers. This thesis focuses on the study of mobile robot localization. However, mobile robot localization is based on the map of its environment. In contrast, the map building of the environment is based on the accurate localization of the robot. In unknown environments,this is a conflicting but correlated process. In order to provide a mobile robot with truly autonomous capabilities, the robot localization and mapping must be seen as one problem to be solved.Supported by the Natural Foundation of Shandong Province, the research topic of this thesis has been focused on the study of mobile robot localization and SLAM. In order to improve the robot's ability of autonomously exploring unknown environments, this thesis presents some improved methods for mobile robot localization and SLAM to deal with the defects of some traditional methods. The main contributions of this thesis include the following aspects:1. Through the analysis of the effects of the measurement noise's statistic characteristics on the performance of Extended Kalman filtering (EKF), a mobile robot localization method based on fuzzy-adapted EKF is proposed. Fuzzy logic and covariance-matching technique are adopted to adjust the measurement noise covariance R, so as to overcome the bad effects of the incomplete knowledge about it, and on-line improve the performance of the localization method. Moreover, a sensor fault diagnostic and recovery algorithm is used to monitor the sensors'state and...