| Summary: | A novel method of indoor mobile robot navigation is presented. The proposed approach fuses the data of odometry and electronic compass for navigation. It includes two calibration methods and a fusion algorithm. First of all, calibration method of systematic odometry error is used to reduce the error of navigation and provide the reliable estimate of pose of mobile robot for adaptive extended Kalman filter fusion algorithm later. Secondly, calibration method of electronic compass using an adaptive neural fuzzy inference system provides direction angle of mobile robot as observation for adaptive extended Kalman filter algorithm later. Finally, a fusion algorithm using adaptive extended Kalman filter algorithm fuses data of odometry and electronic compass that provides the position and orientation of mobile robot. In addition, the value of the parameter k of adaptive extended Kalman filter algorithm which is related to the process noise covariance is decided by fuzzy algorithm. In order to illustrate the feasibility of the proposed approach, two types of experiments are done: the first-type experiment is that the mobile robot runs along default path only with odometry, and the mobile robot with odometry and electronic compass in the second-type experiment utilizes the proposed approach for navigation. The results of experiments show that the error of localization and navigation in the first-type experiment is larger than one in the second-type experiment. They prove the good performance of the proposed approach.
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