本論文針對室內移動機器人開發一應用感測器融合技術於室內移動機器人同步定位與地圖建構系統。一般室內移動機器人如果依靠單一感測器進行室內定位與導航時，容易受到感測器之累積誤差、機器人車輪打滑等不確定因素影響，造成定位資訊可信度較低；尤其當機器人快速移動時，可能造成其定位資訊不穩定。因此本論文透過基本型卡爾曼濾波器融合一組裝設於室內移動機器人之左右輪輪軸上的相對式編碼器與陀螺儀用來提供可信度較高之里程計，再利用無損型卡爾曼濾波器融合先前之里程計與Hector SLAM（Hector Simultaneous Localization and Mapping），在室內移動機器人進行路徑跟隨等動態移動任務與目標點停靠時，提供穩定性高的定位資訊於室內移動機器人。此系統包含一個使用者操作介面，主要用於供給使用者操控室內移動機器人進行地圖建構、地圖儲存與地圖讀取，並透過預先建立好之地圖，結合A*搜尋演算法與貝茲曲線進行路徑規劃，產生一較為平滑之室內機器人行走路徑，依此路徑使室內機器人進行路徑跟隨與目標點停靠任務。最後將透過卡爾曼濾波器融合感測器之里程計準確度實驗、動態定位準確度實驗、重現精度實驗、軌跡追蹤精度實驗、目標點停靠精度實驗等各項實驗來驗證本論文所提之系統在實際環境運作下之效能表現，最後實驗結果證明無損型卡爾曼濾波器之結果在動態路徑跟隨之效能表現較為優勢。

This thesis presents the approaches of applying sensor fusion technique for realizing indoor mobile robot simultaneous localization and mapping (SLAM) system. In general, the precision and robustness of mobile robot localization may be affected by the uncertain factors of environments if a single sensor was used. To overcome this problem, this work presented the Kalman filter (KF) approach to implement the sensor fusion technique in term of recruiting wheel-encoder based odometry and gyro to provide a better odometry precision. The KF-based odometry was further combined with the Hector SLAM in terms of unscented Kalman filter (UKF) to propose a more stable and reliable mobile robot localization performance. In addition, this work implemented a human machine interface (HMI) for the mobile robot operation. Furthermore, this work combined the A* algorithm with Bezier curve generator to plan the mobile robot trajectory automatically in a given map information for autonomous navigation. Finally, a number of experiments were done to validate the performance of original odometry, KF and UKF. The experiments showed that the UKF outperformed the KF; the UKF performed better localization performance at curvature paths.

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