摘要
近年，機器人的相關研究越來越受到關注。許多機器人相關的應用技術也廣為發展。尤其在一些耗費人力或是一些極度危險且不適合以人力在其中工作的環境中，我們都希望能利用機器人來代替人力完成工作。其中機器人的導航已是一個必要且廣泛被討論研究的課題。即時自我定位、建立地圖、路徑規劃以及閃避障礙物的能力是行動自主機器人不可或缺的。 然而，要達到這些功能並不容易。例如定位與建立地圖為兩個互相影響的問題，機器人若不能夠精準的自我定位，將導致建立的地圖產生誤差，並且以此含有誤差的地圖來定位機器人，將使得誤差不斷累積，不堪使用。路徑規劃以及閃避障礙物也需依賴良好的自我定位來確定機器人、障礙物、地圖之間的相對關係，避障更必須要有即時性以確保機器人能及時避開障礙物。 在本篇論文中，我們提出一套系統，讓機器人使用搭載於其上的全景攝影機以及雷射測距儀器擷取環境中的點特徵(垂直線段)以及線特徵(水平線段)作為地標，並以EKF(Extended Kalman Filter)修正誤差，來達到即時自我定位與建地圖。在地圖建立完成後，機器人能利用地圖規劃路徑並行走，行走的同時機器人持續利用全景攝影機以及雷射測距儀器不斷的自我定位和偵測障礙物，若發現影響路徑的障礙物，以改良型的RRT(Rapidly Random Tree)快速的建立出閃避路徑，使機器人迅速反應閃避，以達到機器人導航的需求。
並列摘要
In recent years, there is more and more attention on robotics research. Many related application and technology are also widely developed. In particular, for some labor-intensive or extremely dangerous works, we long for using robots to substitute for human being and accomplish these works. Therefore, robot navigation is necessary and widely discussed. Simultaneous self-positioning, environment map building, path planning and obstacle avoidance are essential abilities for autonomous mobile robots. However, it is not easy to achieve these functions. These four problems are not independent but mutually correlated. For example, once self-localization consists in error, it may cause the wrong map building. And the wrong map will cause self-localization in larger error. Furthermore, path planning and obstacle avoidance also need to rely on good self-localization to determine the relative position of robot, obstacles and map. In this thesis, we propose a system for wheeled robot SLAM and navigation in indoor environments. An omni-directional camera and a laser range finder are the sensors to extract the point features and the line features as the landmarks. In SLAM and self-localization while navigation, we use extended Kalman filter (EKF) to deal with the uncertainty of robot pose and landmark feature estimation. After the map is built, robot can navigate in the environment based on it. We apply two scale path-planning for navigation. The large-scale planning finds an appropriate path from starting point to destination. The local-scale path-planning fills up the drawbacks of the prior step, such as dealing with the static and dynamic obstacles and smoothing the path for easier robot following. Through the experiment results, we show that the proposed system can smoothly and correctly locate itself, build the environment map and navigate in indoor environments.