最近幾年，有許多研究者投入心力於服務性機器人的研究，其主要目的在於協助人群處理一些例行事務。日常生活中，有許多地方可由服務性機器人提供支援，譬如居家安全、娛樂，以及遞送物品等。對於室內的環境來說，如何引導這些機器人抵達預定地，並且賦予它們一些智慧去做日常工作，是一個值得探討的問題。本論文將提出一個在室內環境下，自主性機器人對於字牌偵測及辨識的技術，並藉由字牌所提供的資訊，達到導引的目標，進而完成所交付的任務，以提供諸如辦公室環境的便利生活。

於我們所建構具有字牌偵測與辨識能力的自主性機器人，其視覺系統為主要擷取週遭資訊的配備，它是利用PTZ 攝影機獲得室內環境中的連續影像，在我們的實驗裡，將使用在影像強度上對比較高的字牌提供區域切割的資訊。在自主性機器人行進的途中，會對所取得的連續影像做字牌偵測，我們先利用顏色資訊做分析，當在取得預測區域之後，再使用邊緣偵測法來觀察每一個區域的邊點數，其中邊點數過少的區域將被過濾掉；接著，我們使用線性鑑別分析的方法做字牌辨識，若是辨識結果符合所預定的目標，則機器人會做出相對應的動作。

在我們的實驗過程中，受到光影的影響之下，字牌偵測率大於93%，而受到遮蔽的影響之下，則高於90%；另外，字牌辨識率若以字母作統計，則平均約有93.5%。在我們的系統，影像的解析度為640×480，而偵測和辨識程序的處理速度平均一秒約有兩個畫面。

In recent years, many researchers do the best of their abilities in the development of service robots. The main purpose of these researches is to assist people deal with routine matters. In our daily life, many supports could be offered from service robots; for example, home security, entertainment, and delivery. In an indoor environment, there is an issue worthy to be investigated that how to guide robots to reach a destination and embed them some intelligence to handle day-and-day works. In this thesis, we would present text plate detection and recognition techniques used for an autonomous robot navigating in indoor environments. We would use text plates to provide the surrounding information and achieve the goal of navigation. Furthermore, some given tasks could be accomplished by the autonomous robot to supply convenient services in an indoor environment such as an office room.

The vision system of our autonomous robot which possesses the text plate detection and recognition abilities is the main equipment to capture surrounding information. By use of a PTZ camera, we could obtain the sequential images of indoor environments. In the experiments, we would adopt the text plates that have high contrast of image intensity to provide the information of region segmentation. As the autonomous robot moving forward, the obtained sequential images are used in text plate detection. We analyze the color information in these images to determine candidate regions, and observe the edge points in each candidate region after edge detection. Some regions would be filtered out because the number of edge points is too few in them. We would recognize the text plate by applying the linear discriminant analysis method. When the recognition result matches with the predefined target, the autonomous robot would execute the corresponding actions.

The experimental results reveal that the text plate detection rate is larger than 93% under the influence of lights and shadows. The detection rate is higher than 90% when the text plates are occluded with blots. Besides, the average text plate recognition rate is about 93.5%, where every letter is counted in statistics. In our system, each captured frame contains 640×480 pixels, and the procedures of text plate detection and recognition work by two frames per second.

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